by Paul Gilster | Feb 28, 2024 | Astrobiology and SETI |
Galaxies look fixed in astronomical photos, but of course they’re dynamic systems ever in motion. The closest stars to Earth at Alpha Centauri will eventually close to within about 3 light years if we wait thirty thousand years or so. After that, as the system moves away again, Ross 248 will emerge as the closest interstellar target, closing to about the same distance before moving off into the night. But that will require waiting a bit longer, on the order of another 6000 years. We might also keep an eye on Gliese 445, which in 46,000 years or so will close to less than 4 light years of the Sun.
So everything is moving all the time, and we can say something more about future encounters. The REsearch Consortium On Nearby Stars (RECONS) has found that within the local 10-parsec volume, 81 percent of the 357 main sequence stars in its stellar census are less than half as massive as the Sun. That’s about the current estimate for the percentage of stars in the galaxy that are M-dwarfs, and we might expect that most close passages to our system have been and will be with this type of star.
The average distance between these stars is 3.85 light years, which again is close to what we see locally, since the Alpha Centauri stars are not far beyond that range from us. In this collection of stars, 232 single-star systems appear, and 85 multiples. This has led to interesting speculations, such as Bradley Hansen and Ben Zuckerman’s work at UCLA arguing that if stars get close enough, interstellar flight between them is more feasible. Suppose a star at 14,000 AU and the prospect seems more workable.
Stars do indeed get that close, as the example of Gliese 710 shows. If we’re patient, we can wait out the 1.3 million years it is projected for this to happen, for this star, on the borderline between M-dwarf and K-class, is headed our way from its current vantage in the constellation Serpens Cauda. As it will eventually be well inside the Oort Cloud, we can imagine quite an impact on cometary orbits and planetary ones as well over the long haul, as the paper I’m about to discuss shows. But before leaving Hansen and Zuckerman, let me mention that they calculate that if we widen the timeframe to a billion years, the likelihood of a star getting as close as 5000 AU is 81 percent.
Now let’s flip the question backward in time. Scholz’s Star (WISE J072003.20-084651.2) moved near our system about 70,000 years ago, reaching by current estimates somewhere between 52,000 AU and 68,000 AU from the Sun. Here we’ve got a binary consisting of an M-dwarf and a probable brown dwarf companion at 0.8 AU.
Image: Now about 20 light years away, Scholz’s Star (red star in the center) and its brown dwarf companion passed close to the Solar System and remained within 100,000 AU for a period of roughly 10,000 years. Image Credit: ESO VPHAS / Wikimedia Commons CC BY-SA 4.0.
Nathan Kaib (Planetary Science Institute) and Sean Raymond (Laboratoire d’Astrophysique de Bordeaux, CNRS) have gone to work on the question of passing stars as it relates to the Solar System’s evolution. Study the changes that have occurred in Earth’s climate over the millennia and it appears that fluctuations in the eccentricity of Earth’s orbit are involved. Kaib points to a specific episode that is illuminated by the analysis in this paper:
“One example of such an episode is the Paleocene-Eocene Thermal Maximum 56 million years ago, where the Earth’s temperature rose 5-8 degrees centigrade. It has already been proposed that Earth’s orbital eccentricity was notably high during this event, but our results show that passing stars make detailed predictions of Earth’s past orbital evolution at this time highly uncertain, and a broader spectrum of orbital behavior is possible than previously thought.”
So we’re digging into the history of our planet’s orbit, along with that of the other planets. The authors’ research shows that stellar encounters are not uncommon. A star passes within 50,000 AU on average every million years, and within 10,000 AU every 20 million years. To study the matter, Kaib and Raymond use a hybrid integrator – a set of specialized numerical methods – called MERCURY to simulate these encounters, under conditions described in the paper. The computer runs show that orbital changes to Earth do indeed result, or are at least accelerated, by perturbations from other stars.
Complicating these calculations is the fact that orbital evolution is chaotic, so that beyond timescales on the order of 100 million years, it is impossible to do more than characterize it statistically. The authors point out that even the long-term stability of the Solar System is not guaranteed, as over the Sun’s lifetime there is a 1 percent chance that Mercury will be lost by collision with either the Sun or Venus.
Earth’s past or future orbital evolution can only be confidently predicted inside a time horizon much shorter than Earth’s age, as small uncertainties in current planetary orbits eventually lead to dramatically divergent behavior. The time horizon set by the internal chaos among the Sun’s eight planets is ∼70 Myr, but additional strong chaos resulting from encounters between large asteroids shortens the horizon by another ∼10 Myr (Laskar et al. 2011b). However, inside this time horizon, backward integration of the Sun’s planets has been used to predict the detailed past orbital evolution of the Earth (Laskar et al. 2004).
Such perturbations make it difficult to pin down Earth’s orbital evolution the farther back in time we go, and it appears as well that perturbations from asteroid interactions are less significant than stellar encounters in degrading our ability to predict orbital changes. That’s useful information, because most simulations of the long-term evolution of the planetary orbits have modeled the Solar System in isolation. Changes in Earth’s orbit can also be the result of interactions with the giant planets, but a passing star can affect their orbits, which in turn impacts Earth’s orbital trajectory. Indeed, adding the giant planets to the simulations shows what a major factor these worlds are on Earth’s orbit once they themselves are perturbed by the passing star. Kaib and Raymond call them “a dynamical link that ultimately allows the Milky Way’s stars to influence the long-term evolution of Earth’s orbit.”
The paper turns to a specific encounter, that with the star HD 7977, which is a G-class star in Cassiopeia now some 250 light years away. 2.8 million years ago, this star moved past the Solar System at about 27 kilometers per second, its trajectory taking it somewhere in the neighborhood of 13,200 AU from the Sun, although the authors point out the wide cone of uncertainty about the distance, which may have been as little as 3900 AU. The ‘impulse gradient’ that shows the level of perturbation to the planets was over an order of magnitude higher than the norm during this time.
Working this into their models, the authors find that the median value of 13,200 AU would have had little effect on the Solar System’s long term chaotic evolution, but a passage at 3900 AU would have affected the eccentricity of Earth’s orbit. This is in addition, of course, to whatever effect such a close passage would have on Oort Cloud objects. A passage at 3900 AU would be one of the ten most powerful encounters experienced in the history of our system given the star’s above average mass and an encounter velocity that is below the average.
Image: Illustration of the uncertainty of Earth’s orbit 56 million years ago due to a potential past passage of the Sun-like star HD7977 2.8 million years ago. Each point’s distance from the center corresponds to the degree of ellipticity of Earth’s orbit, and the angle corresponds to the direction pointing to Earth’s perihelion, or closest approach distance to the Sun. 100 different simulations (each with a unique color) are sampled every 1,000 years for 600,000 years to construct this figure. Every simulation is consistent with the modern Solar System’s conditions, and the differences in orbital predictions are primarily due to orbital chaos and the past encounter with HD 7977. Credit: N. Kaib/PSI.
What’s notable here is that the authors have demonstrated that stellar encounters can be significant drivers for system evolution, an area not as widely studied as the internal dynamics of the Solar System. Here’s their conclusion:
…stellar encounters significantly accelerate the chaotic diffusion of Earth’s orbit and the time back to which numerical simulations can confidently predict Earth’s orbital evolution is ∼10% shorter than previously thought. Second, this chaotic divergence that stellar passages impart on Earth’s orbit results from their perturbations to the giant planets’ orbits, and these perturbations roughly scale with the velocity impulse gradients of stellar encounters. Third, the known encounter with HD 7977 2.8 Myr ago has the potential to unlock new sequences of Earth’s past orbital evolution beyond 50 Myr ago that have not been considered or generated in previous modeling efforts. Although it takes tens of Myr for the effects of stellar passages to significantly manifest themselves, the long-term orbital evolution of the Earth and the rest of the planets is linked to these stars.
We seem to have overestimated our ability to describe Earth’s orbital state in earlier eras given what we’re learning about the disruptive effects of stellar encounters. These close brushes with other stars can potentially cause changes in eccentricity that have been overlooked. Factoring stellar encounters into future models is going to be complicated, and necessary.
The paper is Kaib & Raymond, “Passing Stars as an Important Driver of Paleoclimate and the Solar System’s Orbital Evolution,” Astrophysical Journal Letters 962 (14 February 2024) L28 (full text). The Hansen and Zuckerman paper I mentioned above is “Minimal conditions for survival of technological civilizations in the face of stellar evolution,” Astronomical Journal Col. 161, No. 3 (25 February 2021) 145 (full text). See also Bobylev & Bajkova, “Search for Close Stellar Encounters with the Solar System Based on Data from the Gaia DR3 Catalogue,” Astronomical Letters Vol. 48 (13 February 2023), 542-549 (abstract).
by Paul Gilster | Feb 2, 2024 | Astrobiology and SETI |
We live in a world that is increasingly at ease with the concept of intelligent extraterrestrial life. The evidence for this is all around us, but I’ll cite what Louis Friedman says in his new book Alone But Not Lonely: Exploring for Extraterrestrial Life (University of Arizona Press, 2023). When it polled in the United States on the question in 2020, CBS News found that fully two-thirds of the citizenry believe not only that life exists on other planets, but that it is intelligent. That this number is surging is shown by the fact that in polling 10 years ago, the result was below 50 percent.
Friedman travels enough that I’ll take him at his word that this sentiment is shared globally, although the poll was US-only. I’ll also agree that there is a certain optimism that influences this belief. In my experience, people want a universe filled with civilizations. They do not want to contemplate the loneliness of a cosmos where there is no one else to talk to, much less one where valuable lessons about how a society survives cannot be learned because there are no other beings to teach us. Popular culture takes many angles into ETI ranging from alien invasion to benevolent galactic clubs, but on the whole people seem unafraid of learning who aliens actually are.
Image: Louis Friedman, Co-Founder and Executive Director Emeritus, The Planetary Society. Credit: Caltech.
The silence of the universe in terms of intelligent signals is thus disappointing. That’s certainly my sentiment. I wrote my first article on SETI back in the early 1980s for The Review of International Broadcasting, rather confident that by the end of the 20th Century we would have more than one signal to decipher from another civilization. Today, each new report from our active SETI efforts at various wavelengths and in varying modes creates a sense of wonder that a galaxy as vast as ours has yet to reveal a single extraterrestrial.
It’s interesting to see how Friedman approaches the Drake equation, which calculates the number of civilizations that should be out there by setting values on factors like star and planet formation and the fraction of life-bearing planets where life emerges. I won’t go through the equation in detail here, as we’ve done that many times on Centauri Dreams. It’s sufficient to note that when Friedman addresses Drake, he cites the estimates for each factor in the current scientific literature and also gives a column with his own guess as to what each of these items might be.
Image: This is Table 1 from Friedman’s book. Credit: Louis Friedman / University of Arizona Press.
This gets intriguing. Friedman comes up with 1.08 civilizations in the Milky Way – that would be us. But he also makes the point that if we just take the first four terms in the Drake equation and multiply them by the time that Earth life has been in existence, we get on the order of two billion planets that should have extraterrestrial life. Thus a point of view I find consistent with my own evolving idea on the matter: Life is all over the place, but intelligent life is vanishingly rare.
Along the way Friedman dismisses the ‘cosmic zoo’ hypothesis that we looked at recently as being perhaps the only realistic way to support the idea that intelligent life proliferates in the Milky Way. Ian Crawford and Dirk Schulze-Makuch see a lot wrong with the zoo hypothesis as well, but argue that the idea we are being observed but not interacted with is stronger than any other explanation for what David Brin and others have called ‘the Great Silence.’ I’ll direct you to Milan M. Ćirković’s The Great Silence: Science and Philosophy of Fermi’s Paradox for a rich explanation both cultural and scientific of our response to the ‘Where are they?’ question.
Before reading Alone But Not Lonely, my own thinking about extraterrestrial intelligence has increasingly focused on deep time. It’s impossible to run through even a cursory study of Earth’s geological history without realizing how tiny a slice our own species inhabits. The awe induced by these numbers tends to put a chill up the spine. The ‘snowball Earth’ episode seems to have lasted, for example, about 85 million years in its entirety. Even if we break it into two periods (accounting for the most severe conditions and excluding periods of lesser ice penetration), we still get two individual eras of global glaciation, each lasting ten million years.
These are matters that are still in vigorous debate among scientists, of course, so I don’t lean too heavily on the precise numbers. The point is simply to cast something as evidently evanescent as our human culture against the inexorable backdrop of geological time. And to contrast even that with a galaxy that is over 13 billion years old, where processes like these presumably occurred in multitudes of stellar systems. What are the odds that, if intelligence is rare, two civilizations would emerge at the same time and live long enough to become aware of each other? And does the lack of hard evidence for extraterrestrial civilizations not make this point emphatic?
But let me quote Friedman on this:
Let’s return to that huge difference between the time scales associated with the start of life on Earth and its evolution to intelligence. The former number was 3.5 to 3.8 billion years ago, a “mere” 0.75 to 1 billion years after Earth formed. Is that just a happenstance, or is that typical of planets everywhere? I noted earlier that intelligence (including the creation of technology) has only been around for 1/2,000,000 of that time—just the last couple thousand years. Life has been on Earth for about 85 percent of its existence; intelligence has been on Earth for about 0.0005 percent of that time. Optimists might want to argue that intelligence is only at its beginning, and after a million years or so those numbers will drastically change, perhaps with intelligence occupying a greater portion of Earth’s history. But that is a lot of optimism, especially in the absence of any other evidence about intelligence in the universe.
Friedman argues that the very fact we can envision numerous ways for humanity to end – nuclear war, runaway climate effects, deadly pandemics – points to how likely such an outcome is. It’s a good point, for technology may well contain within its nature the seeds of its own destruction. What scientists like Frank Tipler and Michael Hart began pointing out decades ago is that it only takes one civilization to overcome such factors and populate the galaxy, but that means we should be seeing some evidence of this. SETI continues the search as it should and we fine-tune our methods of detecting objects like Dyson spheres, but shouldn’t we be seeing something by now?
The reason for the ‘but not lonely’ clause in Friedman’s title is that ongoing research is making it clear how vast a canvas we have to analyze for life in all its guises. Thus the image below, which I swipe from the book because it’s a NASA image in the public domain. What I find supremely exciting when looking at an actual image of an exoplanet is that this has been taken by our latest telescope, which is itself in a line of technological evolution leading to completely feasible designs that will one day be able to sample the atmospheres of nearby exoplanets to search for biosignatures.
Image: This image shows the exoplanet HIP 65426 b in different bands of infrared light, as seen from the James Webb Space Telescope: purple shows the NIRCam instrument’s view at 3.00 microns, blue shows the NIRCam instrument’s view at 4.44 microns, yellow shows the MIRI instrument’s view at 11.4 microns, and red shows the MIRI instrument’s view at 15.5 microns. These images look different because of the ways that the different Webb instruments capture light. A set of masks within each instrument, called a coronagraph, blocks out the host star’s light so that the planet can be seen. The small white star in each image marks the location of the host star HIP 65426, which has been subtracted using the coronagraphs and image processing. The bar shapes in the NIRCam images are artifacts of the telescope’s optics, not objects in the scene. Credit: NASA, ESA, CSA, Alyssa Pagan (STScI).
Bear in mind the author’s background. He is of course a co-founder (with Carl Sagan and Bruce Murray) of The Planetary Society. At the Jet Propulsion Laboratory in the 1970s, Friedman was not only involved in missions ranging from Voyager to Magellan, but was part of the audacious design of a solar ‘heliogyro’ that was proposed as a solution for reaching Halley’s Comet. That particular sail proved to be what he now calls ‘a bridge too far,’ in that it was enormous (fifteen kilometers in diameter) and well beyond our capabilities in manufacture, packaging and deployment at the time, but the concept led him to a short book on solar sails and has now taken him all the way into the current JPL effort (led by Slava Turyshev) to place a payload at the solar gravitational lens distance from the Sun. Doing this would allow extraordinary magnifications and data return from exoplanets we may or may not one day visit.
Friedman is of the belief that interstellar flight is simply too daunting to be a path forward for human crews, noting instead the power of unmanned payloads, an idea that fits with his current work with Breakthrough Starshot. I won’t go into all the reasons for his pessimism on this – as the book makes clear, he’s well aware of all the concepts that have been floated to make fast interstellar travel possible, but skeptical they can be adapted for humans. Rather than Star Trek, he thinks in terms of robotic exploration. And even there, the idea of a flyby does not satisfy, even if it demonstrates that some kind of interstellar payload can be delivered. What he’s angling for beyond physical payloads is a virtual (VR) model in which AI techniques like tensor holography can be wrapped around data to construct 3D holograms that can be explored immersively even if remotely. Thus the beauty of the SGL mission:
We can get data using Nature’s telescope, the solar gravity lens, to image exoplanets identified from Earth-based and Earth-orbit telescopes as the most promising to harbor life. It also would use modern information technology to create immersive and participatory methods for scientists to explore the data—with the same definition of exploration I used at the beginning of this book: an opportunity for adventure and discovery. The ability to observe multiple interesting exoplanets for long times, with high-resolution imaging and spectroscopy with one hundred billion times magnification, and then immerse oneself in those observations is “real” exploration. VR with real data should allow us to use all our senses to experience the conditions on exoplanets—maybe not instantly, but a lot more quickly than we could ever get to one.
The idea of loneliness being liberating, which Friedman draws from E. O. Wilson, is a statement that a galaxy in which intelligence is rare is also one which is entirely open to our examination, one which in our uniqueness we have an obligation to explore. He lists factors such as interplanetary smallsats and advanced sail technologies as critical for a mission to the solar gravitational lens, not to mention the deconvolution of images that such a mission would require, though he only hints at what I consider the most innovative of the Turyshev team’s proposals, that of creating ‘self-assembling’ payloads through smallsat rendezvous en-route. In any case, all of these are incremental steps forward, each yielding new scientific discoveries from entirely plausible hardware.
Such virtual exploration does not, of course, rule out SETI itself, including the search for other forms of technosignature than radio or optical emissions. Even if intelligence ultimately tends toward machine incarnation, evidence for its existence might well turn up in the work of a mission to the gravitational lens. So I don’t think a SETI optimist will find much to argue with in this book, because its author makes clear how willing he is to continue to learn from the universe even when it challenges his own conceptions.
Or let’s put that another way. Let’s think as Friedman does of a program of exploration that stretches out for centuries, with not one but numerous missions exploring through ever refined technologies the images that the bending of spacetime near the Sun creates. We keep hunting, in other words, for both life and intelligence, for we know that the cosmos seems to have embedded within it the factor of surprise. A statement sometimes attributed to Asimov comes to mind: “The most exciting phrase to hear in science, the one that heralds new discoveries, is not “Eureka!” (I found it!) but “That’s funny…” The history of astronomy is replete with such moments. There will be more.
The book is Friedman, Alone but Not Lonely: Exploring for Extraterrestrial Life, University of Arizona Press, 2023.
by Paul Gilster | Jan 30, 2024 | Astrobiology and SETI |
Globular clusters, those vast ‘cities of stars’ that orbit our galaxy, get a certain amount of traction in SETI circles because of their age, dating back as they do to the earliest days of the Milky Way. But as Henry Cordova explains below, they’re a less promising target in many ways than the younger, looser open clusters which are often home to star formation. Because it turns out that there are a number of open clusters that likewise show considerable age. A Centauri Dreams regular, Henry is a retired map maker and geographer now living in southeastern Florida and an active amateur astronomer. Here he surveys the landscape and points to reasons why older open clusters are possible homes to life and technologies. Yet they’ve received relatively short shrift in the literature exploring SETI possibilities. Is it time for a new look at open clusters?
by Henry Cordova
If you’re looking for signs of extra-terrestrial intelligence in the cosmos, whether it be radio signals or optical beacons or technological residues, doesn’t it make sense to observe an area of sky where large numbers of potential candidates (particularly stars) are concentrated? Galaxies, of course, are large concentrations of stars, but they are so remote that it is doubtful we would be able to detect any artifacts at those distances. Star clusters are concentrations of stars gathered together in a small area of the celestial sphere easily within the field of view of a telescope or radio antenna. These objects also have the advantage that all their members are at the same distance, and of the same age,
Ask any amateur astronomer; “How many kinds of star cluster are there?” and he will answer; “Two, Open Clusters (OCs) and Globular Clusters (GCs)”. The terms “Globular” and “Open” refer to both their general morphology as well as their appearance through the eyepiece. It’s important to keep in mind that both are collections of stars presumably born at the same time and place (and hence, from the same material) but they are nevertheless very different kinds of objects. There does not seem to be a clearly defined transitional or intermediate state between the two. One type does not evolve into the other. Incidentally, the term ‘Galactic Cluster’ is often encountered when researching this field. It is an obsolete term for an OC and should be abandoned. It is too easily misunderstood as meaning a ‘cluster of galaxies’ and can lead to confusion.
GCs are in fact globular. They are collections of thousands, if not hundreds of thousands, of stars forming spheroidal aggregates much more densely packed towards their centers. OCs are amorphous and irregular in shape, random clumps of several hundred to several thousand stars resembling clouds of buckshot flying through space. Their distribution throughout the galaxy is different as well. GCs orbit the galactic center in highly elliptical orbits scattered randomly through space. They are, for the most part, located at great distances from us. OCs, on the other hand, appear to be restricted to mostly circular orbits in the plane of the Milky Way. Due to the obscuring effects of interstellar dust in the plane of the galaxy, most are seen relatively near Earth. although they are scattered liberally throughout the spiral arms.
Image: The NASA/ESA Hubble Space Telescope has captured the best ever image of the globular cluster Messier 15, a gathering of very old stars that orbits the center of the Milky Way. This glittering cluster contains over 100 000 stars, and could also hide a rare type of black hole at its center. The cluster is located some 35 000 light-years away in the constellation of Pegasus (The Winged Horse). It is one of the oldest globular clusters known, with an age of around 12 billion years. Very hot blue stars and cooler golden stars are seen swarming together in this image, becoming more concentrated towards the cluster’s bright center. Messier 15 is also one of the densest globular clusters known, with most of its mass concentrated at its core. Credit: NASA, ESA.
Studies of both types of clusters in nearby galaxies confirm these patterns are general, not a consequence of our Milky Way’s history and architecture, but a feature of galactic structure everywhere. Other galaxies are surrounded by clouds of GCs, and swarms of OCs circle the disks of nearby spirals. It appears that the Milky Way hosts several hundred GCs and several thousand OCs. It is now clear that not only is the distribution and morphology of star clusters divided into two distinct classes but their populations are as well. OCs are often associated with clouds of gas and dust, and are sometimes active regions of star formation. Their stellar populations are often dominated by massive bright, hot stars evolving rapidly to an early death. GCs, on the other hand, are relatively dust and gas free, and the stars there are mostly fainter and cooler, but long-lived. Any massive stars in GCs evolved into supernovae, planetary nebulae or white dwarfs long ago.
It appears that the globulars are very old. They were created during the earliest stages of the galaxy’s evolution. Conditions must have been very different back then; indeed, globulars may be almost as old as the universe itself. GC stars formed during a time when the interstellar medium was predominantly hydrogen and helium and their spectra now reveal large concentrations of heavy elements (“metals”, in astrophysical jargon). The metals have been carried up from the stellar cores by convective processes late in the stars’ life. Any planets formed around this early generation of stars would likely be gas giants, composed primarily of H and He—not the rocky Earth-type worlds we tend to associate with life.
Open Clusters, on the other hand, are relatively new objects. Many of them we can see are still in the process of formation, condensing from molecular clouds well enriched by metals from previous cycles of nucleogenesis and star formation. These clouds have been seeded by supernovae, solar winds and planetary nebulae with fusion products so that subsequent generations of stars will have the higher elements to incorporate in their own retinue of planets.
Image; Some of our galaxy’s most massive, luminous stars burn 8,000 light-years away in the open cluster Trumpler 14. Credit: NASA, ESA, and J. Maíz Apellániz (Institute of Astrophysics of Andalusia, Spain); Acknowledgment: N. Smith (University of Arizona).
Older OCs may have broken up due to galactic tidal stresses but new ones seem to be forming all the time, and there appears to be sufficient material in the galactic plane to ensure a continuous supply of new OCs for the foreseeable future. In general, GCs are extremely old and stable, but not chemically enriched enough to be suitable for life. OCs are young, several million years old, and they usually don’t survive long for life to evolve there. Any intelligent life would probably evolve after the cluster broke up and its stars dispersed. BUT…there are exceptions.
The most important parameter that determines a star’s history is its initial mass. All stars start off as gravitationally collapsing masses of gas, glowing from the release of gravitational potential energy. Eventually, temperatures and pressures in the stars’ cores rise to the point where nuclear fusion reactions start producing light and heat. This energy counteracts gravity and the star settles down to a long period of stability, the main sequence. The terminology arises from a line of stars in the color-magnitude diagram of a star cluster. Main sequence stars stay on this line until they run out of fuel and wander off the main sequence.
All stars follow the same evolutionary pattern, but where on the main sequence they wind up, and how long they stay there, depend on their initial mass. Massive stars evolve quickly, lighter ones tend to stay on the main sequence a long time. Our Sun has been a main sequence star for about 4.6 billion years, and it will remain on the main sequence for about another 5 billion years. When it runs out of nuclear fuel it will wander off the main sequence, getting brighter and cooler as it evolves.
All stars evolve in a similar way, but the amount of time they spend in that stable main sequence state is highly dependent on their mass at birth. Studying the point on the color-magnitude diagram of a cluster’s main sequence where stars start to “peel-off” from the MS allows astrophysicists to determine the age of the cluster. It is not necessary to know the absolute brightness, or distance, of the stars since, by definition, all the stars in a cluster are at the same distance. The color-magnitude (or Hertzprung-Russell) diagram is as important to astronomy as the periodic table is to chemistry. It allows us to visualize stellar evolution using a simple graphic model to interpret the data. It is one of the triumphs of 20th century science.
It is this ability to determine the age of a cluster that allows us to select a set of OCs that meet the criterion of great age needed for biological evolution to take place. Although open clusters tend to quickly lose their stars through gravitational interactions with molecular clouds in the disc of the galaxy, a surprising number seem to have survived long enough for biological, and possibly technologically advanced, species to evolve. Although less massive stars, such as main sequence red dwarfs, tend to be preferentially ejected from OCs due to gravitational tides, more massive F, G, and K stars are more likely to remain.
Sky Catalog 2000.0 (1) lists 32 OCs of ages greater than 1.0 Gyr. A more up-to-date reference, the Wikipedia entry (2), lists others. No doubt, a thorough search of the literature will reveal still more. A few of these OCs are comparable in age to the globulars. They are relics of an ancient time. But many others are comparable to our Sun in age (indeed, our own star, like many others, was born in an open cluster).
Regardless of the observing technique or wavelength utilized, an OC provides the opportunity to examine a large number of stars simultaneously, stars which have been pre-selected as being of a suitable age to support life or a technically advanced civilization. It will also be assured that, as members of an OC, all the stars sampled were formed in a metal-rich environment, and that any planets formed about those stars may be rocky or otherwise Earthlike.
If a technical civilization has arisen on any of those stars, it is possible that they have explored or colonized other stars in the cluster and we have the opportunity to eavesdrop on intra-cluster communications. And from the purely practical point of view, when acquiring scarce funding or telescope time for such a project, it will be possible to piggy-back a SETI program onto non-SETI cluster research. Other than SETI, there are very good reasons to study OCs. They provide a useful laboratory for investigations into stellar evolution.
References
1) Sky Catalog 2000.0, Vol II, Sky Publishing Corp, 1985.
2) https://en.wikipedia.org/wiki/List_of_open_clusters
Suggestions for Additional reading
1. H. Cordova, The SETI Potential of Open Star Clusters, SETIQuest, Vol I No 4, 1995
2. R. De La Fuente Marcos, C. De La Fuente Marcos, SETI in Star Clusters: A Theoretical Approach, Astrophysics and Space Science 284: 1087-1096, 2003
3. M.C. Turnbull, J.C. Tarter, Target Selection for SETI II: Tycho-2 Dwarfs, Old Open Clusters, And the Nearest 100 Stars, ApJ Supp. Series 149: 423-436, 2003
by Paul Gilster | Jan 24, 2024 | Astrobiology and SETI |
Working in the field has its limitations, as Alex Tolley reminds us in the essay that follows, but at least biologists have historically been on the same planet with their specimens. Today’s hottest news would be the discovery of life on another world, as we saw in the brief flurries over the Viking results in 1976 or the Martian meteorite ALH84001. We rely, of course, on remote testing and will increasingly count on computer routines that can make the fine distinctions needed to choose between biotic and abiotic reactions. A new technique recently put forward by Robert Hazen and James Cleaves holds great promise. Alex gives it a thorough examination including running tests of his own to point to the validity of the approach. One day using such methods on Mars or an ice giant moon may confirm that abiogenesis is not restricted to Earth, a finding that would have huge ramifications not just for our science but also our philosophy.
by Alex Tolley
Perseverance rover on Mars – composite image.
Cast your mind back to Darwin’s distant 5-year voyage on HMS Beagle. He could make very limited observations, make drawings and notes, and preserve his specimen collection for his return home to England.
Fifty years ago, a field biologist might not have much more to work with. Hours from a field station or lab with field guides and kits to preserve specimens, with no way to communicate. As for computers to make repetitive calculations, fuggedaboutit.
Fast forward to the late 20th and early 21st centuries, and fieldwork is extending out to the planets in our solar system to search for life. Like Darwin’s voyage, the missions are distant and long. Unlike Darwin, samples have not yet been returned from any planets, only asteroids and comets. Communication is slow, more on the order of field experiences. But instead of humans, our robot probes are “Going where no one has gone before” and humans may not go until much later. The greater the communication lag, the more problematic the central command to periphery control model. Reducing this delay demands a need for more peripheral autonomy at the periphery to make local decisions.
The 2006 Astrobiology Field Laboratory Science Steering Group report recommended that the Mars rover be a field laboratory, with more autonomy [17]. The current state of the art is the Perseverance rover taking samples in the Jezero crater, a prime site for possible biosignatures. Its biosignature instrument, SHERLOC, uses Raman spectrography and luminescence to detect and identify organic molecules [6]. While organic molecules may have been detected [19], the data had to be transmitted to Earth for interpretation, maintaining the problem of lag times between each sample to be chosen and analyzed.
As our technology improves, can these robots operating on planetary surfaces be able to do more effective in situ analyses in the search for extant or extinct life, so that they can operate more quickly like a human field scientist, in the search for life?
While we “know life when we see it”, nevertheless we still struggle to define what life is, although with terrestrial life we have sufficient characteristics except for edge cases like viruses and some ambiguous early fossil material. However, some defining characteristics do not apply to dead, or fossilized organisms and their traces. Fossil life does not metabolize, reproduce, or move, and molecules that are common to life no longer exist in their original form. Consider the “fossil microbes” on the Martian meteorite ALH84001 that caused such a sensation when announced but proved ambiguous.
Historically, for fossil life, we have relied on detecting biosignatures, such as C13/C12 ratios in minerals (due to chlorophyll carbon isotope preference), long-lasting biomolecules like lipids, homochirality of organic compounds, and disequilibria in atmospheric gases. Biomolecules can be ambiguous, as the amino acids detected in meteorites are most likely abiotic, something the Miller-Urey experiment demonstrated many decades ago.
Ideally, we would like a detection method that is simple, robust, and whose results can be interpreted locally without requiring analysis on Earth.
A new method to try to identify the probably biotic nature of samples with organic material is the subject of a new paper from a collaboration under Prof. Robert Hazen and James Cleaves. The team not only uses an analytical method—pyrolysis gas chromatography coupled to electron impact ionization mass spectrometry (Pyr-GC-EI-MS) to heat (pyrolyze), fractionate volatile components (gas chromatography), and determine their mass (mass spectrometry), but also analyzes the data to classify whether the new samples contain organic material of biological origin. Their reported early results are very encouraging [10, 11, 12].
The elegance of Hazen et al’s work has been to apply the Pyr-GC-EI-MS technique [3, 15, 18] that is not only available in the laboratory, but is also designed for planetary rovers to meet the need for local analysis. Their innovation has been to couple this process with computationally lightweight machine learning models to classify the samples, thereby bypassing the time lags associated with distant terrestrial interpretation. A rover could relatively rapidly take samples in an area and determine whether any might have a biosignature based on a suite of different detected compounds and make decisions locally on how to proceed.
The resulting data of masses and extraction time can be reduced and then classified using the pre-trained Random Forest [4], which is a suite of Decision Trees (see Figure 3) using samples of the feature set of masses, to provide a classification, which with the currently tested samples, provides a better than 90% probability of correct classification. The reported experiment used 134 samples, 75 labeled as abiotic and 59 as biotic or of biotic origin. The data set ranged in mass from 50 to 700 and several thousand scans over time. This data was reduced to a manageable size by reducing the mass and time ranges to 8147 values. The samples were then run against several machine learning methods, of which the Random Forest worked best.
To provide a visualization of which mass and time values were most instrumental in classifying the data, the 20 most informative data points were extracted and overlaid on the MS data as shown in Figure 1.
Figure 1 above shows 2 samples of data. One is the Allende meteorite which is classified as a 4.5 billion years old abiotic sample. This is contrasted with one of the microbial samples. While the details of the meteorite sample were not provided, older analyses by others indicated that the surface was contaminated with terrestrial material, whilst the interior matrix included polycyclic aromatic hydrocarbons, a common material found in samples from space missions [7,8]. The bacterial sample, as expected, shows many more compounds after pyrolysis, as the organism is composed of a large variety of organic compounds including amino acids, nucleobases, lipids, and sugars which will decompose with heating. A key point is that the discriminant features are not coincident with the most common masses in the samples, but rather in the rarer compounds as indicated by their intensities. [The lower bound mass bin ensures that common pyrolysis products such as low carbon number compounds will be excluded from the analysis and visualization. The data is normalized to the highest peak so that relative values rather than absolutes are analyzed to eliminate sample amounts.] Most of the defining compounds are in the 140 – 200 mass range, which would imply all-carbon compounds with 12-16 atoms.
Figure 2 shows a 2-dimensional Principal Components Analysis (PCA) using the 20 most informative features that illustrate the separation of the sample types. The expanded box encompasses all the abiotic samples.
I note that even though the biotic and natural samples were given the same classification label, these samples separate quite clearly too, indicating that the natural samples appear almost distinct from the biotic samples. What is perhaps surprising is that biological materials like cedarwood (oils derived from the bark) cluster with the abiotic samples, and even cyanobacteria seem similar in this view. Notice that the dried oak leaf, clearly a degraded living material, is remarkably similar to a cysteine (amino acid) and glucose Maillard reaction (used in the searing of foods to create flavors). A number of the natural materials that were classified as of biological origin or containing material of biological origin, also cluster closely with the abiotic samples, such as Quisqueite and Asphaltum. The peat sample (labeled natural) is placed between the bulk of both biological and natural samples.
Why should this technique work to classify samples according to the type labels? It has been argued that living things are both complex, but composed of molecules that occupy a relatively small space of possible diversity. [Work by Lee Cronin’s group has looked at the way biology restricts the possible structures of organic molecules to create complex macromolecules using few subunits. For example, the amino acid glycine is both important as a constituent of proteins, forming much of the structure of collagen, and is central to several biosynthesis pathways, that include the synthesis of porphyrins and thence to heme in red blood corpuscles. Some macromolecules such as cellulose are formed entirely of D-glucose, as are most complex sugar macromolecules. Cronin calls his technique Assembly Theory [1].]
But larger molecules constructed of a small number of simpler molecules alone are insufficient. Cellulose is a polymer of D-glucose molecules, but clearly, we would not state that a sheet of wet paper was once living, or formed by natural processes. A minimal complexity is required. Life relies on a suite of molecules connected by metabolic pathways that exquisitely restrict the possible number of resulting molecules, however complex, such as proteins that are constructed from just 20 of the possible much greater number of amino acids. At the heart of all life is the Krebs cycle which autotrophs use in the reverse direction to oxidation as part of carbon fixation to build biomass, often glucose to build cellulose cell walls.
The Pyr-GC-EI-MS technique detects a wide range of organic molecules, but the machine learning algorithm uses a set of specific ones to detect the requisite complexity as well as the abiotic randomness. In other words, this is complementary to Cronin’s “Assembly Theory” of life.
I would note that the PCA uses just 20 variables to separate the abiotic and biotic/natural samples. This appears adequate in the majority of the sample set but may be fewer than the variables used in the Random Forest machine learning algorithm. [A single Decision Tree using my reduced data uses just 12 rules – (masses and normalized frequency), but the accuracy is far lower. The Random Forest using different rules (masses and quantities, would be expected to use more features.]
How robust is this analysis?
The laboratory instrument generates a large amount of data for each sample, over 650 mass readings repeated over 6000 times over the scan time. The data was reduced for testing which in this case was 8149 values. There were 134 samples, 59 were classed as biotic or natural, and 75 were abiotic samples. A Random Forest (a suite of Decision Trees) algorithm proved the best method to classify the samples. This resulted in a 90+% correct classification of the sample types. The PCA visualization in Figure 2 is instructive as it shows how the samples were likely classified by the Random Forest model, and which samples were likely misclassified. The PCA used just 20 of the highest-scoring variables to separate the 2 classes of samples.
Generally, the Pyr-GC-EI-MS technique is considered robust with respect to masses extracted from different samples of the same material. The authors included replicates in the samples which should, ideally, be classified together in the same leaf in each Decision Tree in the Random Forest. That this is the case in this experiment is hinted by the few labels that point to 2 samples that are close together in the PCA shown in Figure 2, e.g. the cysteine-glucose Maillard reaction. That replicates are very similar is important as it indicates that the sample processing technique reliably produces the same output and therefore single samples are producing reliable mass and time signals with low noise. [In my experiment (see Appendix A) where K-means clustering was used, in most cases, the replicate pairs were collected together in the same cluster indicating that no special data treatment was needed to keep the replicates together.]
The pyrolysis of the samples transforms many of the compounds, often with more species than the original. For example, cellulose composed purely of D-Glucose will pyrolyze into several different compounds [18]. The assumption is that pyrolysis will preserve the differences between the biotic and abiotic samples, especially for material that has already undergone heating, such as coal. As the pyrolysis products in the mass range of 50 to 200 may no longer be the same as the original compounds, this technique can be applied to any sample containing organic material.
The robustness of the machine learning approach can be assessed by the distribution of the accuracy of the individual runs of the Random Forest. This is not indicated in the article. However, the high accuracy rate reported does suggest that the technique will report this level of accuracy consistently. What is not known is whether this existing trained model would continue to classify new samples accurately. This will also indicate the likely boundary conditions where this model works and whether retraining will be needed after the sample set is increased. This will be particularly important when assessing the nature of any confirmed extraterrestrial organic material that is materially different from that recovered from meteorites.
The robustness may be dependent on the labeling to train the Random Forest model. The sample set labels RNA and DNA as abiotic because they were sourced from a laboratory supply, while the lower complexity insect chitin exoskeleton was labeled biotic. But note that the chitin sample is within the abiotic bounding box in Figure 2, as well as the DNA sample.
Detecting life from samples that are fossils, degraded material, or parts of an organism like a skeletal structure, probably requires being able to look for both complexity and material that is composed of fewer, simpler subunits. In extremis, a sample with few organic molecules even after pyrolysis will likely not be complex enough to be identified as biotic (e.g. the meteorite samples), while a large range of organic molecules may be too varied and indicate abiotic production (e.g. Maillard reactions caused by heating). There will be intermediate cases, such as the chitinous exoskeleton of an insect that has relatively low molecular complexity but which the label defines as biotic.
What is important here is that while it might be instructive to know what the feature molecules are, and their likely pre-heated composition, the method does not rely on anything more than the mass and peak appearance time of the signal to classify the material.
Why does the Random Forest algorithm work well, and exceed that of a single Decision Tree or 2-layer Perceptron [a component of neural networks used in binary classification tasks]? A single Decision Tree requires that the set of features have a strong common overlap for all samples in the class. The greater the overlap, the fewer rules are needed. However, a single Decision Tree model is brittle in the face of noise. This is overcome with the Random Forest by using different subsets of the features to build each tree in the forest. With noisy data, this builds robustness as the predicted classification is based on a majority vote. (See Appendix A for a brief discussion on this.)
Is this technique agnostic?
Now let me address the important issue of whether this approach is agnostic to different biologies, as this is the crux of whether the experimental results will detect not just life, but extraterrestrial life. Will this approach address the possibly very different biologies of life evolved from a different biogenesis?
Astrobiology, a subject with no examples, is currently theoretical. There is almost an industry trying to provide tests for alien life. Perhaps the most famous example is the use of the disequilibria of atmospheric gases, proposed by James Lovelock. The idea is that life, especially autotrophs like plants on Earth, will create an imbalance in reactive gases such as oxygen and methane that keeps them apart from their equilibrium. This idea has since been bracketed with constraints and additional gases, but the basic idea remains a principal approach for exoplanets where only atmospheric gas spectra can be measured.
As life is hypothesized to require a complex set of molecules, yet far fewer than a random set of all possible molecules, or as Cronin has suggested, reuse of molecules to reduce the complexity of building large macromolecules, it is possible that there could be fossil life, either terrestrial or extraterrestrial, that has the same apparent complexity, but largely non-overlapping molecules. The Random Forest could therefore build some Decision Trees that could select different sets of molecules to make the same biotic classification, suggesting that this is an agnostic method. However, this has yet to be tested as there are no extraterrestrial biotic samples to test. It may require such samples, if found and characterized as biotic, to be added to a new training set should they not be classified as biotic using the current model.
As this experiment assumes that life is carbon-based, clearly truly exotic life based on other key elements such as silicon would be unlikely, but not impossible, to be detected if volatile non-organic materials in a sample could be classified correctly.
The authors explain what agnostic in their experiment means:
Our Proposed Biosignature is Agnostic. An important finding of this study is that abiotic, living, and taphonomic suites of organic molecules display well-defined clusters in their high-dimensional space, as illustrated in Fig. 2. At the same time, large “volumes” of this attribute space are unpopulated by either abiotic suites or terrestrial life. This topology suggests the possibility that an alien biochemistry might be recognized by forming its own attribute cluster in a different region of Fig. 2—a cluster that reflects the essential role in selection for function in biotic systems, albeit with potentially very different suites of functional molecules. Abiotic systems tend to cluster in a very narrow region of this phase space, which could in principle allow for easy identification of anomalous signals that are dissimilar to abiotic geochemical systems or known terrestrial life.
What they are stating is that their approach will detect the signs of life in both extant organisms and the resulting decay of their remains when fossilized, such as shales and fossil fuels like coal and oil. As the example PCA of Figure 2 shows, the abiotic samples are tightly clustered in a small space compared to the far greater space of the biotic and once-biotic samples. The authors’ Figure 1 shows that their chosen method results in fewer different molecules found in the Allende meteorite compared to a microbe. I note that the dried oak leaf that is also within the abiotic cluster of the PCA visualization is possibly there because the bulk of the material is cellulose. Cellulose is made of chains of polymerized D-glucose, and while the pyrolysis of cellulose is a physical process that creates a wider assortment of organic compounds [18], this still limits the possible pyrolysis products.
This analysis is complementary to Cronin’s Assembly Theory which theorizes a reduced molecular space of life compared to the randomness and greater complexity of purely chemical and physical processes. This is because life constrains its biochemistry to enzyme-mediated reaction pathways. Assembly Theory [1] and other complexity theories of life [15] would be expected to reduce the molecular space compared to the possible arrangements of all the atoms in an organism.
The authors’ method is probably detecting the greater space of molecules from the required complexity of life compared to the simpler samples and reactions that were labeled as abiotic.
For any extraterrestrial “carbon units” that are theorized to follow organizing principles, this method may well detect extraterrestrial life, whether extant or fossilized, from a unique abiogenesis. However, I would be cautious of this claim simply because there were no biotic extraterrestrial samples used, because we have none, only presumed abiotic samples such as the organic material inside meteorites that should not be contaminated with terrestrial life.
The authors suggest that an alien biology using very different biological molecules might form their own discrete cluster and therefore be detectable. In principle, this is true, but I am not sure that the Random Forest machine learning model would detect the attributes of this cluster without training examples to define the rules needed. Any such samples might simply expose any brittleness in the model and either cause an error or be classified as a false positive for either a biotic or abiotic sample. Ideally, as Asimov once stated, the phrase most associated with interesting discoveries “is not ‘Eureka’ but ‘That’s funny . . .’”, might be associated with an anomalous classification. This might be particularly noticeable if the technique indicates that the sample is abiotic, while a direct observation by microscope clearly shows wriggling microbes.
In summary, it is yet to be tested against new, unknown samples to confirm whether it is both robust, and also agnostic, for other carbon-based life.
The advantage of this technique for remote probes
While the instrument data would likely be sent to Earth regardless of local processing and any subsequent rover actions, the trained Random Forest model is computationally very lightweight and easy to run on the data. Inspection of the various Decision Trees in the Random Forest allows an explanation for which features best classify the samples. As the Random Forest is updated by larger sample sets, it is easy to update the model to analyze samples in the lab or on a remote robotic instrument, in contrast to Artificial Neural Network architectures (ANN) that are computationally intensive. Should a sample that looks like it could be alien life but produces an anomalous result (That’s funny…”), the data can be analyzed on Earth and then assigned a classification, and the Random Forest model rerun with the new data either on Earth and the model uploaded, or locally on the probe.
Let me stress again that the instrumentation needed is already available for life-detection missions on robotic probes. The most recent is the Mars Organic Molecule Analyzer (MOMA) [9] which is to be one of the suite of instruments on the Rosalind Franklin rover as part of the delayed ExoMars mission which is now planned for a 2028 launch. MOMA will use both the Pyr-GC-EI-MS sample processing approach, plus a UV laser on the organic material extracted from 2-meter subsurface drill cores to characterize the material. I would speculate that it might make sense to calibrate the sample set with the MOMA instruments to determine if the approach is as robust with this instrument as the lab equipment for this study. The sample set can be increased and run on the MOMA instruments and finalized well before the launch date.
[If the Morningstar Mission to Venus does detect organic material in the temperate Venusian clouds, perhaps in 2025, this type of analysis using instruments flown on a subsequent balloon mission might offer the fastest way to determine if that material is from a life form before any later sample return.]
While this is an exciting, innovative approach to classifying organic molecules and classifying them as biotic or abiotic, it is not the only approach and should be considered complementary. For example, terrestrial fossils may be completely mineralized, with their form indicating origin. A low-complexity fragment of an insect’s exoskeleton would have a form indicative of biotic origin. The dried oak leaf in the experiment that clusters with the abiotic samples would leave an impression in the sediment indicative of life, just as we see occasionally in coal seams. Impressions left by soft-bodied creatures that have completely decayed would not be detectable by this method even though their shape may be obviously from an organism. [Although note that shape alone was insufficient for determining the nature of the “fossils” in the Martian meteorite, ALH84001.]
Earlier, I mentioned that the cellulose of paper represents an example with low complexity compared to an organism. However, if a robot probe detected a fragment of paper buried in a Martian sediment, we would have little hesitation in identifying it as a technosignature. Similarly, a stone structure on Mars might have no organic material in its composition but clearly would be identified as an artifact built by intelligent beings.
Lastly, isotopic composition of elements can be indicative of origin when compared to the planetary background isotopic ratios. If we detected methane (CH4) with isotope ratios indicative of production by subsurface methanogens, that would be an important discovery, one that would be independent of this experimental approach.
Despite my caveats and cautions, local life detection, rather like the attempts with the 1976 Viking landers may be particularly important now that the Mars Sample Return mission costs are ballooning and may result in a cancelation, stymying the return to Earth of the samples Perseverance is collecting [16]. One of the major benefits of training the Apollo astronauts to understand the geology and identify important rock samples was the local decisions made by the astronauts over which rock samples to collect, rather than taking random samples and hoping the selection was informative. A mission to an icy moon would benefit from such local life detection efforts if multiple attempts need to be made in fairly rapid succession without requiring communication delays with Earth for analysis and decision-making and where no sample return to Earth was likely. This innovative technique appears to be an important contribution to the search for extraterrestrial life in our system, and possibly even beyond if our probes capture samples from interstellar objects.
The paper is “Cleaves, J et al, Hazen, R, “A robust, agnostic molecular biosignature based on machine Learning,” PNAS 120 (41) (September 25, 2023) e2307149120. Abstract.
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Appendix A. My experiment with the supplied data. [12]
Method
To test some of the feedback from the authors, I ran some simple machine-learning experiments on the data. Rather than reduce the data to the number of variables in the paper, I used a simple data reduction by collapsing the scan data dimension so that only the single mass values remained. I normalized to the largest mass value in a sample that was set to 100 and all normalized floating point numbers were reduced to integers. All the resulting values of less than 1 were therefore set to 0. I used the classification labels as given. I also shuffled the class labels to test that the information in the data was lost with this operation. I used the Weka ML software package for running Decision Trees, Random Forests, and other ML methods [20].
Results and Discussion
Using the example I ran [figure 3] it is clear that the presence of a molecule[s] of mass 280 is sufficient to classify 14 of the 59 biological samples with no other rules needed, and if that rule fails, passing a rule with the presence of a molecule about ½ the mass of the first rule, adds a further 8 samples correctly classified as biological. However, it takes a further 6 rules to classify another 22 biological samples, and 7 rules to select 48 (1 sample was a false positive) of the 75 abiotic samples. The rules used mostly used larger molecules to determine the classifications because they had the most discriminatory power, as suggested by the number of the larger molecules of the 20 used in the PCA visualization. Of the 12 rules in my experiment, all but 3 used masses of 100 or greater, with 3 rules of 200 or greater. It should be noted that many rules simply needed the presence or absence (less than 1% of the peak frequency) of a molecule. The 2 largest biotic and abiotic leaves each required 7 rules, but about half required some non-zero value. The biotic leaf with 22 samples had just 3 rules with peak values that were present, while the abiotic leaf with 49 classified samples had all 7 rules with no peak value or values below a threshold.
Figure 3. The model for a Decision Tree output for a reduced collapsed set of data. It shows the rule tree of different mass normalized frequencies to classify abiotic [A], and biotic and natural [B], samples as leaves. There are 134 samples, For training, all the samples were used, 75 are classed abiotic, and 59 and biotic/natural. [The few misclassified samples were excluded for simplicity and clarity]. As all samples were used, there was no out-of-sample testing of the model.
The best classifier was the Random Forest, as found by the authors. This far exceeded a single Decision Tree. It even exceeded a 2 layer Perceptron. The Random Forest managed to reach a little more than 80% correct classification, which fell to random with the shuffled data. While the results using the more greatly reduced data were less accurate than those of the paper, this is expected by the data reduction method.
To test whether the data had sufficient information to separate the 2 classes simply by clustering, I ran a K-Means clustering [14] to determine how the data separated.
1. The 2 clusters were each comprised of about 60% of one class. Therefore while the separation was poor, there was some separation using all the data. Shuffling the labels destroyed any information in the samples as it did with the Decision Tree and Random Forest tests.
2. The replicate pairs almost invariably stayed in the same cluster together, confirming the robustness of the data.
3. The natural samples, i.e. those with a biogenic origin, like coal, tended mostly to cluster with the abiogenic samples, rather than the biotic ones.
I would point out that the PCA in Figure 2 was interpreted to mean that abiotic samples clustered tightly together. However, an alternative interpretation is that the abiotic and natural samples separate from the biotic if a separation is drawn diagonally to separate the biotic samples from all the rest.
One labeling question I have was placing the commercially supplied DNA and RNA samples in the abiotic class. If we detected either as [degraded] samples on another world, we would almost certainly claim that we had detected life once the possibility of contamination was ruled out. Switching these labels made very little difference to my Random Forest classification overall, but it did switch more samples to be classified as biotic, in excess of the switch of the 2 samples to biotic labels. It did make a difference for a simpler Decision Tree. It increased the correct classifications (92 to 97 of 134), mostly reducing the misclassification of abiotic to biotic classes, (23 to 16). The cost of this improvement was 2 extra nodes and 1 leaf in the Decision Tree.
The poor results of the 2-layer Perceptron indicate that the nested rules used in the Decision Trees are needed to classify the data. Perceptrons are 2-layer artificial neural networks (ANNs) that have an input and output layer, but no hidden neural layers. Perceptons are known to fail the exclusive-OR test (XOR) although the example Decision Tree in Figure 3 does not require any variables to overcome this issue. A multilayer neural net with at least 1 hidden layer would be needed to match the results of the Random Forest.
In conclusion, my results show that even with a dimensionally reduced data set, the data contains some information in total that allows a weak separation of the 2 classification labels and that the random Forest is the best classifier of many that were available in the WEKA ML software package.
References
1. Assembly Theory (AT) – A New Approach to Detecting Extraterrestrial Life Unrecognizable by Present Technologies www.centauri-dreams.org/2023/05/16/assembly-theory-at-a-new-approach-to-detecting-extraterrestrial-life-unrecognizable-by-present-technologies/
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www.centauri-dreams.org/2022/06/03/venus-life-finder-scooping-big-science/
3. Pyrolysis – Gas Chromatography – Mass Spectroscopy en.wikipedia.org/wiki/Pyrolysis%E2%80%93gas_chromatography%E2%80%93mass_spectrometry
4. Random Forest en.wikipedia.org/wiki/Random_forest accessed 10/05/2023/
5. PCA “Principal Component Analysis” en.wikipedia.org/wiki/Principal_component_analysis accessed 10/05/2023
6, SHERLOC “Scanning Habitable Environments with Raman and Luminescence for Organics and Chemicals“ en.wikipedia.org/wiki/Scanning_Habitable_Environments_with_Raman_and_Luminescence_for_Organics_and_Chemicals accessed 10/06/2023
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9. Goesmann, F et al The Mars Organic Molecule Analyzer (MOMA) Instrument: Characterization of Organic Material in Martian Sediments. Astrobiology. 2017 Jul 1; 17(6-7): 655–685.
Published online 2017 Jul 1. doi: 10.1089/ast.2016.1551
10. Cleaves, J et al, Hazen, R, A robust, agnostic molecular biosignature based on machine Learning, PNAS September 25, 2023, 120 (41) e2307149120
doi.org/10.1073/pnas.2307149120
11. __ Supporting information. www.pnas.org/action/downloadSupplement?doi=10.1073%2Fpnas.2307149120&file=pnas.2307149120.sapp.pdf
12. __ Mass Spectroscopy data: osf.io/ubgwt
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17. The Astrobiology Field Laboratory. September 26, 2006. Final report of the MEPAG Astrobiology Field Laboratory Science Steering Group (AFL-SSG). Web: mepag.jpl.nasa.gov/reports/AFL_SSG_WHITE_PAPER_v3.doc
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19. Sharma, S., Roppel, R.D., Murphy, A.E. et al. Diverse organic-mineral associations in Jezero crater, Mars. Nature 619, 724–732 (2023). https://doi.org/10.1038/s41586-023-06143-z
20. Weka 3: Machine Learning Software in Java https://www.cs.waikato.ac.nz/ml/weka/
by Paul Gilster | Jan 10, 2024 | Astrobiology and SETI |
Speculating about the diffusion of intelligent species through the galaxy, as we’ve been doing these past few posts, is always jarring. I go back to the concept of ‘deep time,’ which is forced on us when we confront years in their billions. I can’t speak for anyone else, but for me thinking on this level is closer to mathematics than philosophy. I can accept a number like 13.4 × 10⁹ years (the estimate for the age of globular cluster NGC 6397 and a pointer to the Milky Way’s age) without truly comprehending how vast it is. As biological beings, a century pushes us to the limit. What exactly is an aeon?
NGC 6397 and other globular clusters are relevant because these ancient stellar metropolises are the oldest large-scale populations in the Milky Way. But I’m reminded that even talking about the Milky Way can peg me as insufferably parochial. David Kipping takes me entirely out of this comparatively ‘short-term’ mindset by pushing the limits of chronological speculation into a future so remote that elementary particles themselves have begun to break down. Not only that – the Columbia University astrophysicist finds a way for human intelligence to witness this.
You absolutely have to see how he does this in Outlasting the Universe, a presentation on his Cool Worlds YouTube channel. Now Cool Worlds is a regular stop here because Kipping is a natural at rendering high-level science into thoughtful explanations that even the mathematically challenged like me can understand. Outlasting the Universe begins with Kipping the narrator saying “We are in what you would call the future…the deep future” and takes human evolution through the end of its biological era and into a computer-borne existence in which a consciousness can long outlive a galaxy.
Image: Astrophysicist, author and indeed philosopher David Kipping. Credit: Columbia University.
Along the way we remember (and visit in simulation) Freeman Dyson, who once speculated that to become (almost) immortal, a culture could slow down the perceived rate of time. “Like Zeno’s arrow,” says Kipping, “we keep dialing down the speed.” The visuals here are cannily chosen, the script crisp and elegant, imbued with the ‘sense of wonder’ that brought so many of us to science fiction. Outlasting the Universe is indeed science fiction of the ‘hard SF’ variety as Kipping draws out the consequences of deep time and human consciousness in ways that make raw physics ravishing. I envy this man’s students.
With scenarios like this to play with, where do we stand with the ‘zoo hypothesis?’ It must, after all, reckon with years by the billions and the spread of intelligence. Science fiction writer James Cambias responded to my Life Elsewhere? Relaxing the Copernican Principle post with a tight analysis of the notion that we may be under observation from a civilization whose principles forbid contact with species they study. This is of course Star Trek’s Prime Directive exemplified (although the lineage of the hypothesis dates back decades), and it brings up Jim’s work because he has been so persistent a critic of the idea of shielding a population from ETI contact.
Jim’s doubts about the zoo hypothesis go back to his first novel. A Darkling Sea posits an Europa-like exoplanet being studied by a star-faring species called the Sholen, who are employing a hands-off policy toward local intelligence even as they demand that human scientists on the world’s sea bottom do the same. Not long after publication of the novel (Tor, 2014), he told John Scalzi that he saw Prime Directives and such as “ …a mix of outrageous arrogance and equally overblown self-loathing, a toxic brew masked by pure and noble rhetoric.” The arrogance comes from ignoring the desires of the species under study and denying them a choice in the matter.
In a current blog post called The Zoo Hypothesis: Objections, Jim lays this out in rousing fashion:
…we deduce that you can’t hide a star system which contains a civilization capable of large-scale interstellar operations, which the Zookeepers are by definition. They’re going to be emitting heat, EM radiation, laser light, all the spoor of a Kardashev Type I or higher civilization. And the farther away they are, the more they’re going to be emitting because they need to be bigger and more energy-rich in order to have greater reach.
This gives us one important lesson: if the goal of a Zoo is to keep the civilizations inside from even knowing of the existence of other civilizations, the whole thing is impossible. You can’t have a Zoo without Zookeepers, and the inhabitants of the Zoo will detect them.
Jim’s points are well-taken, and he extends the visibility issue by noting that we need to address time, which must be deep indeed. For a civilization maintaining all the apparatus of a protected area around a given star has to do so on time frames that are practically geological in length. Here we can argue a bit, for a ‘zoo’ set up for reasons we don’t understand in the first place might well come into existence only when the species being studied has reached the capability of detecting its observers.
I referenced Amri Wandel (Hebrew University of Jerusalem) on this the other day. Wandel argues that our own industrial lifespan is currently on the order of a few centuries, and who knows what level of technological sophistication a ‘zoo-keeping’ observer culture might want us to reach before it decides it can initiate contact? That would drop the geological timeframe down to a more manageable span, although the detectability problem still remains. So does the issue of interaction with other star-faring species who might conceivably need to be warned off entering the zoo. Cambias again:
If Captain Kirk or whoever shows up on your planet and says “I’m from another planet. Let’s talk and maybe exchange genetic material — or not, if you want me to leave just say so,” that’s an infinitely more reasonable and moral act than for Captain Kirk to sneak around watching you without revealing his own existence. The first is an interaction between equals, the second is the attitude of a scientist watching bacteria. Is that really a moral thing to do? Why does having cooler toys than someone else give you the right to treat them like bacteria?
This is lively stuff, and speculation of this order is why many people begin reading and writing science fiction in the first place. A hard SF writer, a ‘world builder,’ will make sure that he or she has thought through implications for every action he attributes not only to his characters but the non-human intelligences they may interact with. One thing that had never occurred to me was the issue of visibility when translated to the broader galaxy. Because a zoo needs to be clearly marked. Here’s Jim’s view:
If you’re going to exclude other civilizations from a particular region of the Galaxy, you have to let them know. Shooting relativistic projectiles or giant laser beams at incoming starships is a very ham-fisted way of communicating “keep out!” — and it runs the risk of convincing the grabby civilization that you’re shooting at to start shooting back. And if they’re grabby and control a lot of star systems, that’s going to be a lot of shooting.
Jim’s points are telling, and the comments on my recent Centauri Dreams posts also reflect readers’ issues with the zoo hypothesis. My partiality to it takes these issues into account. If the zoo hypothesis is the best of the solutions to the Fermi question, then the likelihood that other intelligent species are in our neighborhood is vanishingly small. Which lets me circle back to the paper by Ian Crawford and Dirk Schulze-Makuch that set off this entire discussion. It asked, you’ll recall, whether the zoo hypothesis wasn’t the last standing alternative to the idea that technological civilizations are, at the least, rare. It’s not a good alternative, but there it is.
In other words, I’d like the zoo hypothesis to have some traction, because it’s the only way I can find to imagine a galaxy in which intelligent civilizations are common.
Consider the thinking of Crawford and Schulze-Makuch on other hypotheses. Interstellar flight might be impossible for reasons of distance and energy, but this seems a non-starter given that we know of ways within known physics to send a payload to another star even in this century. A slow exploration front moving at Voyager speeds could do the trick in a fraction of the time available given the age of the Milky Way. The lack of SETI detections likewise points to technologies that are physically feasible (various kinds of technosignatures) but are not yet observed.
Is the answer that civilizations don’t live very long, and the chances of any two existing at the same brief time in the galaxy are remote? The nagging issue here is that we would have to assume that all civilizations are temporally limited. It takes only one to find a way through whatever ‘great filter’ is out there and survive into a star-faring maturity to get the galaxy effectively visited and perhaps colonized by now. Crawford and Schulze-Makuch reject models that result in volumes of the galactic disk being unvisited during the four billion years of Earth’s existence, considering them valid mathematically but implausible as solutions to the larger Fermi puzzle.
Many of the hypotheses to explain the Great Silence go even further into the unknowable. What, for example, do we make of attempts to parse out an alien psychology, which inevitably is seen, wittingly or not, as reflecting our own human instincts and passions? Monkish cultures that choose not to expand for philosophical reasons will remain unknowable to us, for example, as will societies that self-destruct before they achieve interstellar flight. We can still draw a few conclusions, though, as Crawford and Schulze-Makuch do, all pointing at least to intelligence being rare.
Although we know nothing of alien sociology, it seems inevitable that the propensity for self-destruction, interstellar colonization and so on must be governed by probability distributions of some kind. The greater the number of ETIs that have existed over the history of the Galaxy, the more populated will be the non-self-destructed and/or pro-colonization wings of these distributions, and it is these ETIs that we do not observe. On the other hand, if the numbers of ETIs have always been small, these distributions will have been sparsely populated and the non-observation of ETIs in their expansionist wings follows naturally.
Image: Are ancient ruins the only thing we may expect to find if we reach other star systems? Are civilizations always going to destroy themselves? The imposing remains of Angkor Wat. Credit: @viajerosaladeriva.
Likewise, we still face the problem that, as Stapledon long ago noted, different cultures will choose different priorities. Why assume that in a galaxy perhaps stuffed with aliens adopting Trappist-like vows of silence there will not be a few societies that do want to broadcast to the universe, a METI-prone minority perhaps, but observable in theory. We have no paradox in the Fermi question if we assume that aliens are rare, but if they are as common as early science fiction implied, the paradox is only reinforced.
So Crawford and Schulze-Makuch have boiled this down to the zoo hypothesis or nothing, with the strong implication that technological life must indeed be rare. I rather like my “one to ten” answer to the question of how many technological species are in the galaxy, because I think it squares with their conclusions. And while we can currently only speculate on reasons for this, it’s clear that we’re on a path to draw conclusions about the prevalence of abiogenesis probably in this century. How often technologies emerge after unicellular life covers a planet is a question that may have to wait for the detection of a technosignature. And as is all too clear, it’s possible this will never come.
The paper is Crawford & Schulze-Makuch, “Is the apparent absence of extraterrestrial technological civilizations down to the zoo hypothesis or nothing?” Published online in Nature Astronomy 28 December 2023 (abstract). James Cambias’ fine A Darkling Sea (Tor, 2014) is only the first of his novels, the most recent of which is The Scarab Mission (Baen, 2023), part of his ‘billion worlds’ series. Modesty almost, but not quite, forbids me from mentioning my essay “Ancient Ruins” which ran in Aeon a few years back.
by Paul Gilster | Jan 5, 2024 | Astrobiology and SETI |
If we were to find life other than Earth’s somewhere else in the Solar System, the aftershock would be substantial. After all, a so-called ‘second genesis’ would confirm the common assumption that life forms often, and in environments that range widely. The implications for exoplanets are obvious, as would be the conclusion that the Milky Way contains billions of living worlds. The caveat, of course, is that we would have to be able to rule out the transfer of life between planets, which could make Mars, say, controversial. But find living organisms on Titan and the case is definitively made.
Ian Crawford and Dirk Schulze-Makuch point out in their new paper on the Fermi question and the ‘zoo hypothesis’ that this issue of abiogenesis could be settled relatively soon as our planetary probes gain in sophistication. We could settle it within decades if we found definitive biosignatures in an exoplanet atmosphere, but here my skepticism kicks in. My guess is that once we have something like the Habitable Worlds Observatory in place (and a note from Dominic Benford informs me that NASA has just put together teams to guide the development of HWO, the flagship mission after the Nancy Grace Roman Space Telescope), the results will be immediately controversial.
In fact, I can see a veritable firestorm of debate on the question of whether a given biosignature can be considered definitive. Whole journals a few decades from now will be filled with essays pushing abiotic ways to produce any signature we can think of, and early reports that support abiogenesis around other stars will be countered with long and not always collegial analysis. This is just science at work (and human nature), and we can recall how quickly Viking results on Mars became questioned.
So I think in the near term we’re more likely to gain insights on abiogenesis through probing our own planetary system. Life on an ice giant moon may turn up, or around a gas giant like Saturn in an obviously interesting moon like Enceladus, and we can strengthen our hunch that abiogenesis is common. In which case, where do we stand on the development of intelligence or, indeed, consciousness? What kind of constraints can we put on how often technology is likely to be the result of highly evolved life? Absent a game-changing SETI detection, we’re still left with the Fermi question. We have billions of years of cosmic history to play with and a galaxy that over time could be colonized.
Image: JWST’s spectacular image of M51 (NGC 5194), some 27 million light-years away in the constellation Canes Venatici. Taken with the telescope’s Near-InfraRed Camera (NIRCam), the image is so lovely that I’ve been looking for an excuse to run it. This seems a good place, for we’re asking whether a universe that can produce so many potential homes for life actually gives rise to intelligence and technologies on a galaxy-wide scale. Here the dark red features trace warm dust, while colors of red, orange, and yellow flag ionized gas. How long would it take for life to emerge in such an environment, and would it ever become space-faring? Credit: ESA/Webb, NASA & CSA, A. Adamo (Stockholm University) and the FEAST JWST team.
Crawford and Schulze-Makuch ask a blunt question in the title of their paper in Nature Astronomy: ”Is the apparent absence of extraterrestrial technological civilizations down to the zoo hypothesis or nothing?” The zoo hypothesis posits that we are being studied by beings that for reasons of their own avoid contact. David Brin referred in his classic 1983 paper “The Great Silence” (citation below) to this as one variation of a quarantine, with the Solar System something like a nature preserve whose inhabitants have no idea that they are under observation.
Quarantines can come in different flavors, of course. Brin notes the possibility that observers might wait for our species to reach a level of maturity sufficient to join what could be a galactic ‘club’ or network. Or perhaps the notion is simply to let planets early in their intellectual development lie fallow as their species mature. Wilder notions include the idea that we could be quarantined because we represent a danger to the existing order, though it’s hard to imagine a scenario in which this occurs.
But the Crawford / Schulze-Makuch paper is not exactly a defense of the zoo hypothesis. Rather, it asks whether it is the only remaining alternative to the idea that the galaxy is free of other civilizations. The paper quickly notes the glaring issue with the hypothesis, and it’s one anticipated by Olaf Stapledon in Star Maker. While any species with the ability to cross interstellar distances might remain temporarily hidden, wouldn’t there be larger trends that mitigate the effectiveness of their strategy? Can you hide one or more civilizations that have expanded over millions of years to essentially fill the galaxy? At issue is the so-called ‘monocultural fallacy’:
…to explain the Fermi paradox in a Galaxy where ETIs are common, all these different, independently evolved civilizations would need to agree on the same rules for the zoo. Moreover, to account for the apparent non-interference with Earth’s biosphere over its history, these rules may have had to remain in place, and to have been adhered to, ever since the first appearance of colonizing ETI in the Galaxy, which might be billions of years if ETIs are common. Indeed, Stapledon (ref. 29, p.168) anticipated this problem when he noted, from the point of view of a future fictional observer, that “different kinds of races were apt to have different policies for the galaxy”.
I always return to Stapledon with pleasure. I dug out my copy of Star Maker to cite more from the book. Here the narrator surveys the growth and philosophies of civilizations in their multitudes during his strange astral journey:
Though war was by now unthinkable, the sort of strife which we know between individuals or associations within the same state was common. There was, for instance, a constant struggle between the planetary systems that were chiefly interested in the building of Utopia, those that were most concerned to make contact with other galaxies, and those whose main preoccupation was spiritual. Besides these great parties, there were groups of planetary systems which were prone to put the well-being of individual world-systems above the advancement of galactic enterprise. They cared more for the drama of personal intercourse and the fulfillment of the personal capacity of worlds and systems than for organization or exploration of spiritual purification. Though their presence was often exasperating to the enthusiasts, it was salutary, for it was a guarantee against extravagance and against tyranny.
That’s a benign kind of strife, but it has an impact. The matter becomes acute when we consider interacting civilizations in light of the differential galactic rotation of stars, as Brin pointed out decades ago. The closest species to us at any given time would vary as different stars come into proximity. That seems to imply a level of cultural uniformity that is all but galaxy-wide if the zoo hypothesis is to work. But Crawford and Schulze-Makuch are on this particular case, noting that a single early civilization (in galactic history) might be considered a ‘pre-emptive civilization’ (this is Ronald Bracewell’s original idea), thus enforcing the rules of the road to subsequent ETIs. In such a way we might still have a galaxy filled with technological societies.
An interesting digression here involves the age of likely civilizations. We know that the galaxy dates back to the earliest era of the universe. European Southern Observatory work on the beryllium content of two stars in the globular cluster NGC 6397 pegs their age at 13,400 ± 800 million years. Extraterrestrial civilizations have had time to arise in their multitudes, exacerbating the ‘monocultural’ issue raised above. But the authors point out that despite its age, the galaxy’s habitability would have been influenced by such issues as “a possibly active galactic nucleus, supernovae and close stellar encounters.” Conceivably, the galaxy at large evolved in habitability so that it is only within the last few billion years that galaxy-spanning civilizations could become possible.
Does that help explain the Great Silence? Not really. Several billion years allows ample time for civilizations to develop and spread. As the paper notes, we have only the example of our Earth, in which it took something like two billion years to develop an atmosphere rich in the oxygen that allowed the development of complex creatures. You don’t have to juggle the numbers much to realize that different stellar systems and their exoplanets are going to evolve at their own pace, depending on the growth of their unique biology and physical factors like plate tectonics. There is plenty of room even in a galaxy where life only emerged within the last billion years for civilizations to appear that are millions of years ahead of us technologically.
Image: The globular cluster NGC 6397. A glorious sight that reminds us of the immensity in both space and time that our own galaxy comprehends. Credit: ESO.
Back to the zoo hypothesis. Here’s one gambit to save it that the paper considers. A policy of non-interference would only need to be enforced for a few thousand years – perhaps only a few hundreds – if extraterrestrials were interested primarily in technological societies. This is Amri Wandel’s notion in an interesting paper titled “The Fermi paradox revisited: technosignatures and the contact era” (citation below). Wandel (Hebrew University of Jerusalem) eases our concern over the monocultural issue by compressing the time needed for concealment. Crawford and Schulze-Makuch cite Wandel, but I don’t sense any great enthusiasm for pressing his solution as likely.
The reasons for doubt multiply:
Even if they can hide evidence of their technology (space probes, communications traffic and so forth), hiding the large number of inhabited planets in the background implied by such a scenario would probably prove challenging (unless they are able to bring an astonishingly high level of technical sophistication to the task). In any case, advanced technological civilizations may find it difficult to hide the thermodynamic consequences of waste heat production, which is indeed the basis of some current technosignature searches. Moreover, any spacefaring civilization is likely to generate a great deal of space debris, and the greater the number of ETIs that have existed in the history of the Galaxy the greater the quantity of debris that will drift into the Solar System, where a determined search may discover evidence for it.
Why then highlight the zoo hypothesis when it has all these factors working against it? Because in the view of the authors, other solutions to the Fermi question are even worse. I’m running out of time this morning, but in the next post I want to dig into some of these other answers to see whether any of them can still be salvaged. For the more dubious our solutions to the ‘where are they’ question, the more likely it seems that there are no civilizations nearby. We’ll continue to push against that likelihood with technosignature and biosignature searches that could change everything.
The paper is Crawford & Schulze-Makuch, “Is the apparent absence of extraterrestrial technological civilizations down to the zoo hypothesis or nothing?” Published online in Nature Astronomy 28 December 2023 (abstract). David Brin’s essential paper is “The Great Silence – the Controversy Concerning Extraterrestrial Intelligent Life,” Quarterly Journal of the Royal Astronomical Society Vol. 24, No.3 (1983), pp. 283-309 (abstract/full text). Amri Wandel’s paper is “The Fermi Paradox revisited: Technosignatures and the Contact Era,” Astrophysical Journal 941 (2022), 184 (preprint).
