Centauri Dreams

Imagining and Planning Interstellar Exploration

What We Know Now about TRAPPIST-1 (and what we don’t)

Our recent conversations about the likelihood of life elsewhere in the universe emphasize how early in the search we are. Consider recent work on TRAPPIST-1, which draws on JWST data to tell us more about the nature of the seven planets there. On the surface, this seven-planet system around a nearby M-dwarf all but shouts for attention, given that we have three planets in the habitable zone, all of them of terrestrial size, as indeed are all the planets in the system. Moreover, as an ultracool dwarf star, the primary is both tiny and bright in the infrared, just the thing for an instrument like the James Webb Space Telescope to harvest solid data on planetary atmospheres.

This is a system, in other words, ripe for atmospheric and perhaps astrobiological investigation, and Michaël Gillon (University of Liége), the key player in discovering its complexities, points in a new paper to how much we’ve already learned. If its star is ultracool, the planetary system at TRAPPIST-1 can also be considered ‘ultracompact’ in that the innermost and outermost planets orbit at 0.01 and 0.06 AU respectively. By comparison, Mercury orbits at 0.4 AU from our Sun. The stability of the system through mean motion resonances means that we’re able to deduce tight limits on mass and density, which in turn give us useful insights into their composition.

Image: Measuring the mass and diameter of a planet reveals its density, which can give scientists clues about its composition. Scientists now know the density of the seven TRAPPIST-1 planets with a higher precision than any other planets in the universe, other than those in our own solar system. Credit: NASA/JPL-Caltech/R. Hurt (IPAC).

Because we’ve been talking about SETI recently, I’ll mention that the SETI Institute has already subjected TRAPPIST-1 to a search using the Allen Telescope Array at frequencies of 2.84 and 8.2 gigahertz. The choice of frequencies was dictated by the researchers’ interest in whether a system this compact might have a civilization that had spread between two or more worlds. Searching for powerful broadband communications when planetary alignments between two habitable planets occur as viewed from Earth is thus a hopeful strategy, and as is obvious, the search yielded nothing unusual. A broader question is whether life might spread between such worlds through impacts and subsequent contamination.

What I’m angling for here is the relationship between a bold, unlikely observing strategy and a more orthodox study of planetary atmospheres. Both of these are ongoing, with the investigation of biosignatures a hot topic as we work with JWST but also plan for subsequent space telescopes like the Habitable Exoplanet Observatory (HabEx). The gap in expectations between SETI at TRAPPIST-1 and atmosphere characterization via such instruments highlights what a shot in the dark SETI can be. But it’s a useful shot in the dark. We need to know that there is a ‘great silence’ and continue to poke into it even as we explore the likelihood of abiogenesis elsewhere.

But back to the Gillon paper. Here you’ll find the latest results on planetary dynamics at TRAPPIST-1 and the implications for how these worlds form, along with current data on their densities and compositions. Another benefit of the compact nature of this system is that the planets interact with each other, which means we get strong signals from Transit Timing Variations that help constrain the orbits and masses involved. No other system has rocky exoplanets with such tight density measurements. The three inner planets are irradiated beyond the runaway greenhouse limit, and recent work points to the two inner planets being totally desiccated, with volatiles likely in the outer worlds.

What we’d like to know is whether, given that habitable zone planets are found in M-dwarf systems (Proxima Centauri is an obvious further example), such worlds can maintain a significant atmosphere given irradiation from the parent star. This is tricky work. There are models of the early Earth that involve massive volatile losses, and yet today’s Earth is obviously life supporting. Is there a possibility that rocky planets around M-dwarfs could begin with a high volatile content to counterbalance erosion from stellar bombardment? Gillon sees TRAPPIST-1 as an ideal laboratory to pursue such investigations, one with implications for M-dwarfs throughout the galaxy. From the paper:

Indeed, its planets have an irradiation range similar to the inner solar system and encompassing the inner and outer limits of its circumstellar habitable zone, with planet b and h receiving from their star about 4.2 and 0.15 times the energy received by the Earth from the Sun per second, respectively. Detecting an atmosphere around any of these 7 planets and measuring its composition would be of fundamental importance to constrain our atmospheric evolution and escape models, and, more broadly, to determine if low-mass M-dwarfs, the larger reservoir of terrestrial planets in the Universe, could truly host habitable worlds.

Image: Belgian astronomer Michaël Gillon, who discovered the planetary system at TRAPPIST-1. Credit: University of Liége.

Thus the early work on TRAPPIST-1 atmospheres, conducted with Hubble data and sufficient to rule out the presence of cloud-free hydrogen-dominated atmospheres for all the planets in the system. But now we have early papers using JWST data, and the issues become more stark when we turn to work performed by Gwenaël Van Looveren (University of Vienna) and colleagues. While previous studies of the system have indicated no thick atmospheres on the two innermost planets (b and c), the Van Looveren team focuses specifically on thermal losses occurring as the atmosphere heats as opposed to hard to measure non-thermal processes like stellar winds.

Here the situation clarifies. Working with computer code called Kompot, which calculates the thermo-chemical structure of an upper atmosphere, the team has analyzed the highly irradiated TRAPPIST-1 environment, modeling over 500 photochemical reactions in light of X-Ray, ultraviolet and infrared radiation, among other factors. The results show strong atmospheric loss in the early era of system development, but take into account losses through the different stages of the system’s evolution. It’s important to keep in mind that a star like this takes between 1 and 2 billion years to settle onto the main sequence, a period of high radiation. It’s also true that even main-sequence M-dwarfs can show high levels of radiation activity.

The upshot: X-ray and UV activity declines very slowly in the first several billion years on the main sequence, and stellar radiation in these wavelengths is the main driver of atmospheric loss. Things look dicey for atmospheres on any of the TRAPPIST-1 planets, and the Van Looveren model generalizes to other stars. From the paper:

The results of our models tentatively indicate that the habitable zone of M dwarfs after their arrival on the main sequence is not suited for the long-term survival of secondary atmospheres around planets of the considered planetary masses owing to the high ratio of spectral irradiance of XUV to optical/infrared radiation over a very long time compared to more massive stars. Maintaining atmospheres on planets like this requires their continual replenishment or their formation very late in the evolution of the planets. A further expansion of the grid and more detailed studies of the parameter space are required to draw definitive conclusions for the entire spectral class of M dwarfs.

Image: This is Figure 8 from the paper. Caption: Overview of the planets in the TRAPPIST-1 system and the estimated habitable zone (indicated by the green lines, taken from Bolmont et al. 2017). We added vertical lines at the minimum distances at which atmospheres of various compositions could survive for more than 1 Gyr. Credit: Van Looveren et al.

Note the term ‘primary atmosphere.’ Primary atmospheres of hydrogen and helium give way to secondary atmospheres that are the result of later processes like volcanic outgassing and molecules breaking down under stellar radiation on the planet’s surface. The paper, then, is saying that the kind of secondary atmospheres in which we might hope to find life are unlikely to survive in this environment, although active processes on a given planet might still allow them. The paper ends this way:

Our conclusion from this work is therefore significant for terrestrial planets with a mass that is similar to the Earth’s mass that orbit mid- to late-M dwarfs such as TRAPPIST-1 near or inside the (final) habitable zone. For these planets, substantial N2/CO2 atmospheres are unlikely unless atmospheric gas is continually replenished at high rates on timescales of no more than a few million years (the loss timescales estimated in our work), for example, through volcanism.

I wouldn’t call this the death knell for atmospheric survival at TRAPPIST-1, nor do the authors, but the work points to the factors that have to be addressed in further study of the system, and the results certainly challenge the possibility of life-sustaining atmospheres on any of these planets. The Van Looveren work isn’t included in Michaël Gillon’s paper, which appeared just before its release, but I hope you’ll look at both and keep the Gillon available as the best current overview of TRAPPIST-1.

As to M-dwarf prospects in general, it’s one thing to imagine a high-radiation environment, with the possibilities that life might find an evolutionary path forward, but quite another to strip a planet of its atmosphere altogether. If that is the prospect, then the census of ‘habitable’ worlds drops sharply, for M-dwarfs make up somewhere around 80 percent of all the stars in the Milky Way. A sobering thought to close the morning as I head upstairs to grind coffee beans and rejuvenate myself with caffeine.

The papers are Gillon, “TRAPPIST-1 and its compact system of temperate rocky planets,” to be published in Handbook of Exoplanets (Springer) and available as a preprint. The Van Looveren paper is “Airy worlds or barren rocks? On the survivability of secondary atmospheres around the TRAPPIST-1 planets,” accepted at Astronomy & Astrophysics (preprint).

White Holes: Tunnels in the Sky?

It’s good now and then to let the imagination soar. Don Wilkins has been poking into the work of Carlo Rovelli at the Perimeter Institute, where the physicist and writer explores unusual ideas, though perhaps none so exotic as white holes. Do they exist, and are there ways to envision a future technology that can exploit them? A frequent contributor to Centauri Dreams, Don is an adjunct instructor of electronics at Washington University, St. Louis, where he continues to track research that may one day prove relevant to interstellar exploration. A white hole offers the prospect of even a human journey to another star, but turning these hypothesized objects into reality remains an exercise in mathematics, although as the essay explains, there are those exploring the possibilities even now.

by Don Wilkins

Among the many concepts for human interstellar travel, one of the more provocative is an offspring of Einstein’s theories, the bright twin of the black hole, the white hole. The existence of black holes (BH), the ultimate compression stage for aging stellar masses above three times the mass of our sun, is announced by theory and confirmed by observation. White holes, the matter spewing counterparts of BHs, escape observation but not the explorations of theorists.

Carlo Rovelli, an Italian theoretical physicist and writer, now the Distinguished Visiting Research Chair at the Perimeter Institute, discusses all this in a remarkably brief book called, simply, White Holes (Riverhead Books, 2023) wherein he travels in company with Dante Alighieri, another author with experience at descents into perilous places. Rovelli makes two remarkable assertions. [1]

1) Rovelli states that another scientist, Daniel Finkelstein, demonstrated that Einstein and other analysts are incorrect when they depict what occurs as one enters a black hole. From the Finkelstein paper (citation below):

The gravitational field of a spherical point particle is then seen not to be invariant under time reversal for any admissible choice of time coordinate. The Schwarzschild surface, r=2m is not a singularity but acts as a perfect unidirectional membrane: causal influences can cross it but only in one direction. [2]

In other words, no time dilation, no spaghettification of trespassers entering a black hole. Schwarzchild’s solution only applies to distant observers; it does not describe the observer crossing the event horizon of the black hole.

2) Rovelli believes in the existence of white holes. His white hole births when the black hole compresses its constituent parts into the realm of quantum mechanics. Rovelli speculates “… a black hole … quantum tunnels into a white one on the inside – and the outside can stay the same.”

In Figure 1 and Rovelli’s intuition, a quantum mesh separates the black hole and white hole. At these minute dimensions, quantum tunneling effects surge matter away from the black hole, into the mouth of the white hole and back into the Universe.

Figure 1. Relationship between a black hole and a white hole. Credit: C. Rovelli/Aix-Marseille University; adapted by APS/Alan Stonebraker.

The outside of a black hole and a white hole are geometrically identical regardless of the direction of time. The horizon is not reversible under the flow of time. As a result the interiors of the black hole and white hole are identical.

In a paper he co-authored with Hal Haggard, Rovelli writes:

We have constructed the metric of a black hole tunneling into a white hole by using the classical equations outside the quantum region, an order of magnitude estimate for the onset of quantum gravitational phenomena, and some indirect indications on the effects of quantum gravity. [3]

Haggard and Rovelli acknowledge that the calculations do not result from first principles. A full theory of quantum gravity would supply that requirement.

Figure 2: Artist rendering of the black-to-white-hole transition. Credit: F. Vidotto/University of the Basque Country. [9]

Efforts to design a stable wormhole require buttressing the entrance or mouth of the wormhole with prodigious amounts of a hypothesized material, negative matter. Although minute amounts have been claimed to form in the narrow confines of a Casimir device, ideas on how to manufacture planetary-sized masses of negative matter are elusive. [4]

According to recent research, the stability of the WH is dependent upon which of the two major families of matter, bosons or fermions, forms the WH. Bosons are subatomic particles which obey Bose-Einstein statistics and whose spin quantum number has an integer value (0, 1, 2, …). Photons, gluons, the Z neutral weak boson and the weakly charged bosons are bosons. The graviton, if it exists, is a boson. Theoretic analysis of stable traversable WHs founded on bosonic fields demonstrates a need for vast amounts of negative matter to hold open the mouth of a WH.

The other family, the fermions, have odd half-integer (1/2, 3/2, etc.) spins. These particles, electrons, muons, neutrinos, and compound particles, obey the Pauli Exclusion Principle. It is this family that is employed by a team of researchers to describe a two fermion stable white hole [5]. Their configuration produces John Wheeler’s “charge without charge”, where an electric field is trapped within the structure without any physical electrical charge present. The opening in the white hole would be too small, a few hundred Planck lengths (a Planck length is 1.62 x 10-35 meters) to pass gamma rays.

Rovelli reenters the discussion here. [6] The James Webb Space Telescope has identified large numbers of black holes in the early Universe, more black holes than anticipated. Rovelli describes white holes forming from these black holes as Planck-length sized, chargeless entities, unable to interact with the matter except through gravity. In other words, the descendants of the early black holes manifest as the material we describe as dark matter. Rovelli is working on a quantum sensor to detect these white holes.

Once the white holes are detected, it might be possible to capture a white hole. John G. Cramer, professor emeritus of physics at the University of Washington in Seattle, Washington, suggests accelerating the wormhole to almost the speed of light. [7] Aimed at Tau Ceti, he predicts:

The arrival time as viewed through a wormhole is T’ = T/γ , where γ is the Lorentz factor [γ= (1- v/c)] and v is the wormhole-end velocity after acceleration. For reference, the maximum energy protons accelerated at CERN LHC have a Lorentz factor of 6,930. Thus, the arrival time at Tau Ceti of an LHC-accelerated wormhole-end would be 15 hours….Effectively, the accelerated wormhole becomes a time machine, connecting the present with an arrival far in the future.

Spraying accelerated electrons through the wormhole could expand the mouth to a size where it could be used as a sensor portal into another star system. The wormhole becomes a multi light-year long periscope, one that scientists could bend and twist to study up close and in detail the star and its companions. Perhaps the wormhole could be expanded enough to pass larger, physical bodies.

Constantin Aniculaesei and an international team of researchers may have overcome the need for an accelerator as large as the LHC to accelerate the white hole to useful size [8]. Developing a novel wakefield accelerator, wherein an intense laser pulse focused onto a plasma excites nonlinear plasma waves to trap electrons, the team’s machine produced 10 Giga electron Volt (GeV) electron bunches. The wakefield accelerator was only ten centimeters long, although a petawatt laser was needed to excite the wakefields.

Cramer hypothesizes that fermionic white holes formed immediately after the Big Bang and in cosmic rays. The gateways to the stars could be found in the cosmic ray bombardment of the Earth or possibly trapped in meteorites. The heavy particles, if ensnared on Earth, would probably sink to the center of the planet.

All that is needed to find a fermionic white hole, Cramer suggests, is a mass spectrometer. But let me quote him on this:

[Wormholes] might be a super-heavy components of cosmic rays….They might be trapped in rocks and minerals….In a mass spectrograph, they could in principle be pulled out of a vaporized sample by an electric potential but would be so heavy that they would move in an essentially undeflected straight line in the magnetic field. …wormholes might still be found in meteorites that formed in a gravity free environment.

The worm hole is essentially unaffected by a magnetic field. A mass detector would point to an invisible mass. The rest, as non-engineers like to say, is merely engineering.

If this line of reasoning is correct – a very large if – enlarged white holes could pass messages and matter through tunnels in the sky to distant stars.


1. Carlo Rovelli, translation by Simon Carnell, White Holes, Riverhead Books, USA, 2023

2. David Finkelstein, Past-Future Asymmetry of the Gravitational Field of a Point Particle, Physical Review, 110, 4, pages 965–967, May 1958, 10.1103/PhysRev.110.965

3. Hal M. Haggard and Carlo Rovelli, Black hole fireworks: quantum-gravity effects outside the horizon spark black to white hole tunneling, 4 July 2014, https://arxiv.org/pdf/1407.0989.pdf

4. Matt Visser, Traversable wormholes: Some simple examples, arXiv:0809.0907 [gr-qc], 4 September 2008.

5. Jose Luis Blázquez-Salcedo, Christian Knoll, and Eugen Radu, Traversable Wormholes in Einstein-Dirac-Maxwell theory, arXiv:2010.07317v2, 12 March 2022.

6. What is a white hole? – with Carlo Rovelli, The Royal Institution, https://www.youtube.com/watch?v=9VSz-hiuW9U

7. John G. Cramer, Fermionic Traversable Wormholes, Analog Science Fiction & Fact, January/February 2022.

8. Constantin Aniculaesei, Thanh Ha, Samuel Yoffe, et al, The Acceleration of a High-Charge Electron Bunch to 10 GeV in a 10-cm Nanoparticle-Assisted Wakefield Accelerator, Matter and Radiation at Extremes, 9, 014001 (2024), https://doi.org/10.1063/5.0161687

9). Rovelli, “Black Hole Evolution Traced Out with Loop Quantum Gravity,” Physics 11, 127 (December 10, 2018).

Alone in the Cosmos?

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.

Open Cluster 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.


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

Alien Life or Chemistry? A New Approach

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.


Appendix A. My experiment with the supplied data. [12]


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.


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/

2. 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

7. Han, J et al, Organic Analysis on the Pueblito de Allende Meteorite Nature 222, 364–365 (1969). doi.org/10.1038/222364a0

8. Zenobi, R et al, Spatially Resolved Organic Analysis of the Allende Meteorite. Science, 24 Nov 1989 Vol 246, Issue 4933 pp. 1026-1029 doi.org/10.1126/science.246.4933.1026

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

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

13. Gold, T. The Deep Hot Biosphere: The Myth of Fossil Fuels. Springer Science and Business Media, 2001.

14. K-means clustering en.wikipedia.org/wiki/K-means_clustering

15. Chou, L et al Planetary Mass Spectrometry for Agnostic Life Detection in the Solar System Front. Astron. Space Sci., 07 October 2021 Sec. Astrobiology Volume 8 – 2021

16. “Nasa’s hunt for signs of life on Mars divides experts as mission costs rocket“ Web access 11/13/2023 www.theguardian.com/science/2023/nov/12/experts-split-over-nasa-mission-to-mars-costs-rocket

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

18. Wang, Q., Song, H., Pan, S. et al. Initial pyrolysis mechanism and product formation of cellulose: An Experimental and Density functional theory(DFT) study. Sci Rep 10, 3626 (2020). https://doi.org/10.1038/s41598-020-60095-2

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/

Data Return from Proxima Centauri b

The challenges involved in sending gram-class probes to Proxima Centauri could not be more stark. They’re implicit in Kevin Parkin’s analysis of the Breakthrough Starshot system model, which ran in Acta Astronautica in 2018 (citation below). The project settled on twenty percent of the speed of light as a goal, one that would reach Proxima Centauri b well within the lifetime of researchers working on the project. The probe mass is 3.6 grams, with a 200 nanometer-thick sail some 4.1 meters in diameter.

The paper we’ve been looking at from Marshall Eubanks (along with a number of familiar names from the Initiative for Interstellar Studies including Andreas Hein, his colleague Adam Hibberd, and Robert Kennedy) accepts the notion that these probes should be sent in great numbers, and not only to exploit the benefits of redundancy to manage losses along the way. A “swarm” approach in this case means a string of probes launched one after the other, using the proposed laser array in the Atacama desert. The exciting concept here is that these probes can reform themselves from a string into a flat, lens-shaped mesh network some 100,000 kilometers across.

Image: Figure 16 from the paper. Caption: Geometry of swarm’s encounter with Proxima b. The Beta-plane is the plane orthogonal to the velocity vector of the probe ”at infinity” as it approaches the planet; in this example the star is above (before) the Beta-plane. To ensure that the elements of the swarm pass near the target, the probe-swarm is a disk oriented perpendicular to the velocity vector and extended enough to cover the expected transverse uncertainty in the probe-Proxima b ephemeris. Credit: Eubanks et al.

The Proxima swarm presents one challenge I hadn’t thought of. We have to be able to predict the position of Proxima b to within 10,000 kilometers at least 8.6 years before flyby – this is the time for complete information cycle between Earth, Proxima and back to Earth. Effectively, we need to figure out the planet’s velocity to a value of 1 meter per second, with a correspondingly tight angular position (0.1 microradians).

Although we already have Proxima b’s period (11.68 days), we need to determine its line of nodes, eccentricity, inclination and epoch, and also its perturbations by the other planets in the system. At the time of flyby, the most recent Earth update will be at least 8.5 years old. The Proxima b orbit state will need to be propagated over at least that interval to predict its position, and that prediction needs to be accuracy to the order of the swarm diameter.

The authors suggest that a small spacecraft in Earth orbit can refine Proxima b’s position and the star’s ephemeris, but note that a later paper will dig into this further.

In the previous post I looked at the “Time on Target” and “Velocity on Target” techniques that would make swarm coherence possible, with variations in acceleration and velocity allowing later-launched probes to reach higher speeds, but with higher drag so that as they reach the craft sent before them, they slow to match their speed. From the paper again:

A string of probes relying on the ToT technique only could indeed form a swarm coincident with the Proxima Centauri system, or any other arbitrary point, albeit briefly. But then absent any other forces it would quickly disperse afterwards. Post-encounter dispersion of the swarm is highly undesirable, but can be eliminated with the VoT technique by changing the attitude of the spacecraft such that the leading edge points at an angle to the flight direction, increasing the drag induced by the ISM, and slowing the faster swarm members as they approach the slower ones. Furthermore, this approach does not require substantial additional changes to the baseline BTS [Breakthrough Starshot] architecture.

In other words, probes launched at different times with a difference in velocity target a point on their trajectory where the swarm can cohere, as the paper puts it. The resulting formation is then retained for the rest of the mission. The plan is to adjust the attitude of the leading probes continually as they move through the interstellar medium, which means variations in their aspect ratio and sectional density. A probe can move edge-on, for instance, or fully face-on, with variations in between. The goal is that the probes lost later in the process catch up with but do not move past the early probes.

All this is going to take a lot of ‘smarts’ on the part of the individual probes, meaning we have to have ways for them to communicate not just with Earth but with each other. The structure of the probes discussed here is an innovation. The authors propose that key components like laser communications and computation should be concentrated, so that whereas the central disk is flat, the ‘heart of the device,’ as they put it, is concentrated in a 2-cm thickened rim around the outside of the sail disk.

The center of the disk is optical, or as the paper puts it, ‘a thin but large-aperture phase-coherent meta-material disk of flat optics similar to a fresnel lens…’ which will be used for imaging as well as communications. Have a look at the concept:

Image: This is Figure 3a from the paper. Caption: Oblique view of the top/forward of a probe (side facing away from the launch laser) depicting array of phase-coherent apertures for sending data back to Earth, and optical transceivers in the rim for communication with each other. Credit: Eubanks et al.

So we have a sail moving at twenty percent of lightspeed through an incoming hydrogen flux, an interesting challenge for materials science. The authors consider both aerographene and aerographite. I had assumed these were the same material, but digging into the matter reveals that aerographene consists of a three-dimensional network of graphene sheets mixed with porous aerogel, while aerographite is a sponge-like formation of interconnected carbon nanotubes. Both offer extremely low density, so much so that the paper notes the performance of aerographene for deceleration is 104 times better than conventional mylar. Usefully, both of these materials have been synthesized in the laboratory and mass production seems feasible.

Back to the probe’s shape, which is dictated by the needs not only of acceleration but survival of its electronics – remember that these craft must endure a laser launch that will involve at least 10,000 g’s. The raised rim layout reminds the authors of a red corpuscle as opposed to what has been envisioned up to now as a simple flat disk. The four-meter central disk contains 247 25-cm structures arranged, as the illustration shows, like a honeycomb. We’ll use this optical array for both imaging Proxima b but also returning data to Earth, and each of the arrays offers redundancy given that impacts with interstellar hydrogen will invariably create damage to some elements.

Remember that the plan is to build an intelligent swarm, which demands laser links between the probes themselves. Making sure each probe is aware of its neighbors is crucial here, for which purpose it will use the optical transceivers around its rim. The paper calculates that this would make each probe detectable by its closest neighbor out to something close to 6,000 kilometers. The probes transmit a pulsed beacon as they scan for neighboring probes, and align to create the needed mesh network. The alignment phase is under study and will presumably factor into the NIAC work.

The paper backs out to explain the overall strategy:

…our innovation is to use advances in optical clocks, mode-locked optical lasers, and network protocols to enable a swarm of widely separated small spacecraft or small flotillas of such to behave as a single distributed entity. Optical frequency and reliable picosecond timing, synchronized between Earth and Proxima b, is what underpins the capability for useful data return despite the seemingly low source power, very large space loss and low signal-to-noise ratio.

For what is going to happen is that the optical pulses between the probes will be synchronized, meaning that despite the sharp constraints on available energy, the same signal photons are ‘squeezed’ into a smaller transmission slot, which increases the brightness of the signal. We get data rates through this brightening that could not otherwise be achieved, and we also get data from various angles and distances. On Earth, a square kilometer array of 796 ‘light buckets’ can receive the pulses.

Image: This is Figure 13 from the paper. Caption: Figure 13: A conceptual receiver implemented as a large inflatable sphere, similar to widely used inflatable antenna domes; the upper half is transparent, the lower half is silvered to form a half-sphere mirror. At the top is a secondary mirror which sends the light down into a cone-shaped accumulator which gathers it into the receiver in the base. The optical signals would be received and converted to electrical signals – most probably with APDs [avalanche photo diodes] at each station and combined electrically at a central processing facility. Each bucket has a 10-nm wide band-pass filter, centered on the Doppler-shifted received laser frequency. This could be made narrower, but since the probes will be maneuvering and slowing in order to meet up and form the swarm, and there will be some deceleration on the whole swarm due to drag induced by the ISM, there will be some uncertainty in the exact wavelength of the received signal. Credit: Eubanks et al.

If we can achieve a swarm that is in communication with its members using micro-miniaturized clocks to keep operations synchronous, we can thus use all of the probes to build up a single detectable laser pulse bright enough to overcome the background light of Proxima Centauri and reach the array on Earth. The concept is ingenious and the paper so rich in analysis and conjecture that I keep going back to it, but don’t have time today to do more than cover these highlights. The analysis of enroute and approach science goals and methods alone would make for another article. But it’s probably best that I simply send you to the paper itself, one which anyone interested in interstellar mission design should download and study.

The paper is Eubanks et al., “Swarming Proxima Centauri: Optical Communication Over Interstellar Distances,” submitted to the Breakthrough Starshot Challenge Communications Group Final Report and available online. Kevin Parkin’s invaluable analysis of Starshot is Parkin, K.L.G., “The Breakthrough Starshot system model,” Acta Astronautica 152 (2018), 370–384 (abstract / preprint).


In Centauri Dreams, Paul Gilster looks at peer-reviewed research on deep space exploration, with an eye toward interstellar possibilities. For many years this site coordinated its efforts with the Tau Zero Foundation. It now serves as an independent forum for deep space news and ideas. In the logo above, the leftmost star is Alpha Centauri, a triple system closer than any other star, and a primary target for early interstellar probes. To its right is Beta Centauri (not a part of the Alpha Centauri system), with Beta, Gamma, Delta and Epsilon Crucis, stars in the Southern Cross, visible at the far right (image courtesy of Marco Lorenzi).

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