Centauri Dreams
Imagining and Planning Interstellar Exploration
A Holiday Check-in with New Horizons
The fact that we have three functioning spacecraft outside the orbit of Pluto fills me with holiday good spirits. Of the nearest of the three, I can say that since New Horizons’ January 1, 2019 encounter with the Kuiper Belt Object now known as Arrokoth, I have associated the spacecraft with holidays of one kind or another The July 14, 2015 flyby of Pluto/Charon wasn’t that far off the US national holiday, but more to the point, I was taking a rare beach vacation during the last of the approach phase, most of my time spent indoors with multiple computers open tracking events at system’s edge. It felt celebratory, like an extended July 4, even if the big event was days later.
Also timely as the turn of the year approaches is Alan Stern’s latest PI’s Perspective, a look at what’s ahead for the plucky spacecraft. Here January becomes a significant time, with the New Horizons team working on the proposal for another mission extension, the last of which got us through Arrokoth and humanity’s first close-up look at a KBO. The new proposal aims at continued operations from 2023 through 2025, which could well include another KBO flyby, if an appropriate target can be found. That search, employing new machine learning tools, continues.
Image: Among several discoveries made during its flyby of the Kuiper Belt object Arrokoth in January 2019, New Horizons observed the remarkable and enigmatic bright, symmetric, ring-like “neck” region at the junction between Arrokoth’s two massive lobes. Credit: NASA/Johns Hopkins APL/Southwest Research Institute.
But what happens if no KBO is within reach of the spacecraft? Stern explains why the proposed extension remains highly persuasive:
If a new flyby target is found, we will concentrate on that flyby. But if no target is found, we will convert New Horizons into a highly-productive observatory conducting planetary science, astrophysics and heliospheric observations that no other spacecraft can — simply because New Horizons is the only spacecraft in the Kuiper Belt and the Sun’s outer heliosphere, and far enough away to perform some unique kinds of astrophysics. Those studies would range from unique new astronomical observations of Uranus, Neptune and dwarf planets, to searches for free floating black holes and the local interstellar medium, along with new observations of the faint optical and ultraviolet light of extragalactic space. All of this, of course, depends on NASA’s peer review evaluation of our proposal.
Our only spacecraft in the Kuiper Belt. What a reminder of how precious this asset is, and how foolish it would be to stop using it! Here my natural optimism kicks in (admittedly beleaguered by the continuing Covid news, but determined to push forward anyway). One day – and I wouldn’t begin to predict when this will be – we’ll have numerous Kuiper Belt probes, probably enabled by beamed sail technologies in one form or another as we continue the exploration of the outer system, but for now, everything rides on New Horizons.
The ongoing analysis of what New Horizons found at Pluto/Charon is a reminder that no mission slams to a halt when one or another task is completed. For one thing, it takes a long time to get data back from New Horizons, and we learn from Stern’s report that a good deal of the flyby data from Arrokoth is still on the spacecraft’s digital recorders, remaining there because of higher-priority transmission needs as well as scheduling issues with the Deep Space Network. We can expect the flow of publications to continue. 49 new scientific papers came out this year alone.
That Arrokoth image above is still a stunner, and the inevitable naming process has begun not only here but on Pluto as well. The KBO’s largest crater has been christened ‘Sky,’ while Ride Rupes (for astronaut Sally Ride) and Coleman Mons (for early aviator Bessie Coleman) likewise will begin to appear on our maps of Pluto. All three names have been approved by the International Astronomical Union. ‘Rupes’ is the Latin word for ‘cliff,’ and here refers to an enormous feature near the southern tip of Pluto’s Tombaugh Regio. Ride Rupes is between 2 and 3 kilometers high and about 250 kilometers long, while Coleman Mons is a mountain, evidently recently created and thus distinctive in a region of older volcanic domes.
Image: Close-up, outlines of Ride Rupes (left) and Coleman Mons on the surface Pluto. Credit: NASA/Johns Hopkins APL/Southwest Research Institute/SETI Institute/Ross Beyer.
As the New Horizons team completes the mission extension proposal, it also proceeds with uploading another instrument software upgrade, this one to the Pluto Energetic Particle Spectrometer Science Investigation (PEPSSI) charged-particle spectrometer. And while spacecraft power levels have continued to decline, as is inevitable given the half-life of the nuclear battery’s plutonium, Stern says the spacecraft should be able to maintain maximum data transmission rates for another five years. That new power-saving capability, currently being tested, should strengthen the upcoming proposal and bodes well for any future flyby.
Those of you with an investigative bent should remember that 2021’s data return, along with six associated datasets, is available to researchers whether professional or working in a private capacity, within NASA’s Planetary Data System. This is an active mission deeply engaged with the public as well as its natural academic audience, as I’m reminded by the image below. Here the New Horizons spacecraft has captured a view taken during departure from Pluto, seeing however faintly the ‘dark side’ that was not illuminated by the Sun during the approach.
Image: Charon-lit-Pluto: The image shows the dark side of Pluto surrounded by a bright ring of sunlight scattered by haze in its atmosphere. But for a dark crescent zone to the left, the terrain is faintly illuminated by sunlight reflected by Pluto’s moon Charon. Researchers on the New Horizons team were able to generate this image using 360 images that New Horizons captured as it looked back on Pluto’s southern hemisphere. A large portion of the southern hemisphere was in seasonal darkness similar to winters in the Arctic and Antarctica on Earth, and was otherwise not visible to New Horizons during its 2015 flyby encounter of Pluto.
Credit: NASA/Johns Hopkins APL/Southwest Research Institute/NOIRLab.
This is Pluto’s southern hemisphere during the long transition into winter darkness; bear in mind that a winter on the distant world lasts 62 years. The all too faint light reflecting off Charon’s icy surface allows researchers to extract information. Tod Lauer (National Optical Infrared Astronomy Research Observatory, Tucson), lead author of a paper on the dark side work, compares available light here to moonlight on Earth:
“In a startling coincidence, the amount of light from Charon on Pluto is close to that of the Moon on Earth, at the same phase for each. At the time, the illumination of Charon on Pluto was similar to that from our own Moon on Earth when in its first-quarter phase.”
That’s precious little to work with, but the New Horizons Long Range Reconnaissance Imager (LORRI) made the best of it despite the fierce background light and the bright ring of atmospheric haze. We’ll have to wait a long time before the southern hemisphere is in sunlight, but for now, Pluto’s south pole seems to be covered in material darker than the paler surface of the northern hemisphere, with a brighter region midway between the south pole and the equator. In that zone we may have a nitrogen or methane ice deposit similar to the Tombaugh Regio ‘heart’ that is so prominent in the flyby images from New Horizons.
For more, see Lauer et al., “The Dark Side of Pluto,” Planetary Science Journal Vol. 2, No. 5 (20 Ocober 2021), 214 (abstract).
Of course, there is another mission that will forever have a holiday connection, at least if its planned liftoff on Christmas Eve happens on schedule. Dramatic days ahead.
All Your Base Are Belong To Us! : Alien Computer Programs
If you were crafting a transmission to another civilization — and we recently discussed Alexander Zaitsev’s multiple messages of this kind — how would you put it together? I’m not speaking of what you might want to tell ETI about humanity, but rather how you could make the message decipherable. In the second of three essays on SETI subjects, Brian McConnell now looks at enclosing computer algorithms within the message, and the implications for comprehension. What kind of information could algorithms contain vs. static messages? Could a transmission contain programs sufficiently complex as to create a form of consciousness if activated by the receiver’s technnologies? Brian is a communication systems engineer and expert in translation technology. His book The Alien Communication Handbook (Springer, 2021) is now available via Amazon, Springer and other booksellers.
by Brian S McConnell
In most depictions of SETI detection scenarios, the alien transmission is a static message, like the images on the Voyager Golden Record. But what if the message itself is composed of computer programs? What modes of communication might be possible? Why might an ETI prefer to include programs and how could they do so?
As we discussed in Communicating With Aliens : Observables Versus Qualia, an interstellar communication link is essentially an extreme version of a wireless network, one with the following characteristics:
- Extreme latency due to the speed of light (eight years for round trip communication with the nearest solar system), and in the case of an inscribed matter probe, there may be no way to contact the sender (infinite latency).
- Prolonged disruptions to line of sight communication (due to the source not always being in view of SETI facilities as the Earth rotates).
- Duty cycle mismatch (it is extremely unlikely that the recipient will detect the transmission at its start and read it entirely in one pass).
Because of these factors, communication will work much better if the transmission is segmented so that parcels received out of order can be reassembled by the receiver, and so that those segments are encoded to enable the recipient to detect and correct errors without having to contact the transmitter and wait years for a response. This is known as forward error correction and is used throughout computing (to catch and fix disc read errors) and communication (to correct corrupted data from a noisy circuit).
While there are simple error correction methods, such as the N Modular Redundancy or majority vote code, these are not very robust and dramatically reduce the link’s information carrying capacity. There exist very robust error correction methods, such as the Reed Solomon coding used for storage media and space communication. These methods can correct for prolonged errors and dropouts, and the error correction codes can be tuned to compensate for an arbitrary amount of data loss.
In addition to being unreliable, the communication link’s information carrying capacity will likely be limited compared to the amount of information the transmitter may wish to send. Because of this, it will be desirable to compress data, using lossless compression algorithms, and possibly lossy compression algorithms (similar to the way JPEG and MPEG encoders work). Astute readers will notice a built-in conflict here. Data that is compressed and encoded for error correction will look like a series of random numbers to the receiver. Without knowledge about how the encoding and compression algorithms work, something that would be near impossible to guess, the receiver will be unable to recover the original unencoded data.
The iconic Blue Marble photo taken by the Apollo 17 astronauts. Credit: NASA.
The value of image compression can be clearly shown by comparing the file size for this image in several different encodings. The source image is 3000×3002 pixels. The raw uncompressed image, with three color channels with 8 bits per pixel per color channel, is 27 megabytes (216 megabits). If we apply a lossless compression algorithm, such as the PNG encoding, this is reduced to 12.9 megabytes (103 megabits), a 2.1:1 reduction. Applying a lossy compression algorithm, this is further reduced to 1.1 megabytes (8.8 megabits) for JPEG with quality set to 80, and 0.408 megabytes (3.2 megabits) for JPEG with quality set to 25, which results in a 66:1 Reduction.
Lossy compression algorithms enable impressive reductions in the amount of information needed to reconstruct an image, audio signal, or motion picture sequence, at the cost of some loss of information. If the sender is willing to tolerate some loss of detail, lossy compression will enable them to pack well over an order of magnitude more content into the same data channel. This isn’t to say they will use the same compression algorithms we do, although the underlying principles may be similar. They can also interleave compressed images, which will look like random noise to a naive viewer, with occasional uncompressed images, which will stand out, as we showed in Communicating with Aliens : Observables Versus Qualia.
So why not send programs that implement error correction and decompression algorithms? How could the sender teach us to recognize an alien programming language to implement them?
A programming language requires a small set of math and logic symbols, and is essentially a special case of a mathematical language. Let’s look at what we would need to define an interpreted language, call it ET BASIC if you like. An interpreted language is abstract, and is not tied to a specific type of hardware. Many of the most popular languages in use today, such as Python, are interpreted languages.
We’ll need the following symbols:
- Delimiter symbols (something akin to open and close parentheses, to allow for the creation of nested or n-dimensional data structures)
- Basic math operations (addition, subtraction, multiplication, division, modulo/remainder)
- Comparison operations (is equal, is not equal, is greater than, is less than)
- Branching operations (if condition A is true, do this, otherwise do that)
- Read/write operations (to read or write data to/from virtual memory, aka variables, which can also be used to create input/output interfaces for the user to interact with)
- A mechanism to define reusable functions
Each of these symbols can be taught using a “solve for x” pattern within a plaintext primer that can be interleaved with other parts of the transmission. Let’s look at an example.
1 ? 1 = 2
1 ? 2 = 3
2 ? 1 = 3
2 ? 2 = 4
1 ? 3 = 4
3 ? 1 = 4
4 ? 0 = 4
0 ? 4 = 4
We can see right away that the unknown symbol refers to addition. Similar patterns can be used to define symbols for the rest of the basic operations needed to create an extensible language.
The last of the building blocks, a mechanism to define reusable functions, is especially useful. The sine function, for example, is used in a wide variety of calculations, and can be approximated via basic math operations using the Taylor series shown below:
And in expanded form as:
This can be written in Python as:
The sine() function we just defined can later be reused without repeating the lower level instructions used to calculate the sine of an angle. Notice that the series of calculations used reduce down to basic math and branching operations. In fact any program you use, whether it is a simple tic-tac-toe game or a complex simulation, reduces down to a small lexicon of fundamental operations. This is one of the most useful aspects of computer programs. Once you know the basic operations, you can build an interpreter that can run programs that are arbitrarily complex, just as you can run a JPEG viewer without knowing a thing about how lossy image compression works.
In the same way, the transmitter could define an “unpack” function that accepts a block of encoded data from the transmission as input, and produces error corrected, decompressed data as output. This is similar to what low level functions do to read data off a storage device.
Lossless compression will significantly increase the information carrying capacity of the channel, and also allow for raw, unencoded data to be very verbose and repetitive to facilitate compression. Lossy compression algorithms can be applied to some media types to achieve order of magnitude improvements, with the caveat that some information is lost during encoding. Meanwhile, deinterleaving and forward error correction algorithms can ensure that most information is received intact, or at least that damaged segments can be detected and flagged. The technical and economic arguments for including programs in a transmission are so strong, it would be surprising if at least part of a transmission were not algorithmic in nature.
There are many ways a programming language can be defined. I chose to use a Python based example as it is easy for us to read. Perhaps the sender will be similarly inclined to define the language at a higher level like this, and will assume the receiver can work out how to implement each operation in their hardware. On the other hand, they might describe a computing system at a lower level, for example by defining operations in terms of logic gates, which would enable them to precisely define how basic operations will be carried out.
Besides their practical utility in building a reliable communication link, programs open up whole other realms of communication with the receiver. Most importantly, they can interact with the user in real-time, thereby mooting the issue of delays due to the speed of light. Even compact and relatively simple programs can explain a lot.
Let’s imagine that ET wants to describe the dynamics of their solar system. An easy way to do this is with a numerical simulation. This type of program simulates the gravitational interactionsof N number of objects by summing up gravitational forces acting on each object and steps forward an increment of time to forecast where they will be, and then repeats this process ad infinitum. The program itself might only be a few kilobytes or tens of kilobytes in length since it just repeats a simple set of calculations many times. Additional information is required to initialize the simulation, probably on the order of about 50 bytes or 400 bits per object, enough to encode position and velocity in three dimensions at 64 bit accuracy. Simulating the orbits of the 1,000 most significant objects in the solar system would require less than 100 kilobytes for the program and its starting conditions. Not bad.
This is just scratching the surface of what can be done with programs. Their degree of sophistication is really only limited by the creativity of the sender, who we can probably assume has a lot more experience with computing than we do. We are just now exploring new approaches to machine learning, and have already succeeded at creating narrow AIs that exceed human capabilities in specialized tasks. We don’t know yet if generally intelligent systems are possible to build, but an advanced civilization that has had eons to explore this might have hit on ways to build AIs that are better and more computationally efficient than our state of the art. If that’s the case, it’s possible the transmission itself may be a form of Intelligence.
How would we go about parsing this type of information, and who would be involved? Unlike the signal detection effort, which is the province of a small number of astronomers and subject experts, the process of analyzing and comprehending the contents of the transmission will be open to anyone with an Internet connection and a hypothesis to test. One of the interesting things about programming languages is that many of the most popular languages were created by sole contributors, like Guido van Rossum, the creator of Python, or by small teams working within larger companies. The implication being that the most important contributions may come from people and small teams who are not involved in SETI at all.
For an example of a fully worked out system, Paul Fitzpatrick, then with the MIT CSAIL lab, created Cosmic OS, which details the ideas explored in this article and more. With Cosmic OS, he builds a Turing complete programming language that is based on just four basic symbols: 0 and 1, plus the equivalent of open and close parentheses.
There are risks and ethical considerations to ponder as well. In terms of risk, we may be able to run programs but not understand their inner workings or purpose. Already this is a problem with narrow AIs we have built. They learn from sets of examples instead of scripted instructions. Because of this they behave like black boxes. This poses a problem because an outside observer has no way of predicting how the AI will respond to different scenarios (one reason I don’t trust the autopilot on my Tesla car). In the case of a generally intelligent AI of extraterrestrial provenance, it goes without saying that we should be cautious in where we allow it to run.
There are ethical considerations as well. Suppose the transmission includes generally intelligent programs? Should they be considered a form of artificial life or consciousness? How would we know for sure? Should terminating their operation be considered the equivalent of murder, or something else? This idea may seem far fetched, but it is worthwhile to think about issues like this before a detection event.
Into the Atmosphere of a Star
We’ve been learning about the solar wind ever since the first interplanetary probes began to leave our planet’s magnetosphere to encounter this rapidly fluctuating stream of plasma. Finding a way to harness the flow could open fast transport to the outer Solar System if we can cope with the solar wind’s variability – no small matter – but in any case learning as much as possible about its mechanisms furthers our investigation of possible propulsive techniques. On this score and for the sake of solar science, we have much reason to thank the Parker Solar Probe and its band of controllers as the spacecraft continues to tighten its approaches to the Sun.
The spacecraft’s repeated passes by the Sun, each closer than the last, take advantage of speed and a heat shield to survive each perihelion event, and the last for which we have data was noteworthy indeed. During it, the Parker Solar Probe moved three separate times into and out of the Sun’s corona. This is a region where magnetic fields dominate the movement of particles. The Alfvén critical surface, which the spacecraft repeatedly crossed, is the boundary where the solar atmosphere effectively ends and the solar wind begins. Solar material surging up from below reaches a zone where gravity and magnetic fields can no longer hold it back. Breaking free, the solar wind effectively breaks the connection with the solar corona once across the Alfvén boundary.
So, as with our Voyagers moving past the heliopause and into interstellar space, we’ve accomplished another boundary crossing of consequence. A crossing into and back out of the corona helps define the location of the Alfvén critical surface, which turns out to be close to earlier estimates. These targeted a range between 10 and 20 solar radii. In its most recent passes by the Sun, the Parker Solar Probe has been below 20 solar radii, and on April 28 of this year, at 18.8 solar radii, it penetrated the Alfvén surface.
Nour Raouafi is a Parker project scientist at the Johns Hopkins Applied Physics Laboratory (JHU/APL):
“Flying so close to the Sun, Parker Solar Probe now senses conditions in the magnetically dominated layer of the solar atmosphere – the corona – that we never could before. We see evidence of being in the corona in magnetic field data, solar wind data, and visually in images. We can actually see the spacecraft flying through coronal structures that can be observed during a total solar eclipse.”
Image: As Parker Solar Probe passed through the corona on encounter nine, the spacecraft flew by structures called coronal streamers. These structures can be seen as bright features moving upward in the upper images and angled downward in the lower row. Such a view is only possible because the spacecraft flew above and below the streamers inside the corona. Until now, streamers have only been seen from afar. They are visible from Earth during total solar eclipses. Credit: NASA/Johns Hopkins APL/Naval Research Laboratory.
It’s clear from Parker data that the Alfvén surface is anything but smooth, and the spacecraft’s crossing into the corona did not in fact occur at perihelion on this particular pass, an indication of the varied structures within the region. As seen above, streamers and so-called pseudostreamers are found here, large magnetic-field structures streaming out of regions of the same magnetic polarity that are separated by an inner zone of opposite polarity. Caltech’s Christina M. S. Cohen explains the situation this way in a useful overview of the coronal crossing that notes the spacecraft’s fleeting passage through the boundary:
The center of a pseudostreamer is a region of enhanced magnetic field and reduced plasma density. This combination can push the Alfvén surface higher up in the corona, explaining why PSP’s orbit was able to cut across it…The period of time PSP spent below the Alfvén surface was too short to fully characterize the boundary and explore the inner region. Researchers expect that such a full characterization will require multiple expeditions carried out over different magnetic configurations and solar conditions.
It’s interesting to learn that we’re behind in acquiring data from the Parker Solar Probe because its high-gain antenna cannot be pointed toward Earth until it is far enough from the Sun on its current close pass to protect the equipment. Thus while the current data are from April of 2021, there was a likely crossing of the Alfvén critical surface again in November, when the probe reached a perihelion of 13.6 solar radii. This is close enough to suggest a longer period within the corona, something we won’t know until data download of that pass in late December.
Image: The solar corona during a total solar eclipse on Monday, August 21, 2017, above Madras, Oregon. The red light is emitted by charged iron particles at 1 million degrees Celsius and the green are those at 2 million degrees Celsius. On April 28, 2021, NASA’s Parker Solar Probe crossed the so-called Alfvén surface, entering, for the first time, a part of the solar corona that is “magnetically dominated.” Credit: M. Druckmuller / Christina M. S. Cohen.
Just as the Alfvén critical surface is anything but smooth, so too is the solar wind full of structure as it moves into the realm of the planets. So-called switchbacks were first detected by the Ulysses probe in the 1990s, what NASA describes as “bizarre S-shaped kinks in the solar wind’s magnetic field lines” which deflect charged particle paths as they move away from the Sun. The Parker Solar Probe discovered just how common these switchbacks were back in 2019, with later data showing that at least some switchbacks originate in the photosphere.
Switchbacks also align with magnetic funnels that emerge out of the photosphere between the convection cell structures called supergranules. It may be, then, that we can trace the origins of the solar wind at least partially to these magnetic funnels, as Stuart Bale (University of California, Berkeley) suggests:
“The structure of the regions with switchbacks matches up with a small magnetic funnel structure at the base of the corona. This is what we expect from some theories, and this pinpoints a source for the solar wind itself. My instinct is, as we go deeper into the mission and lower and closer to the Sun, we’re going to learn more about how magnetic funnels are connected to the switchbacks.”
Whether switchbacks are produced by the process of magnetic reconnection at the boundaries of magnetic funnels, or are produced by moving waves of plasma, is a question scientists hope the Parker Solar Probe will be able to answer. Just how the solar wind connects to switchbacks may help to explain how the corona is heated to temperatures far above that of the solar surface below. Bear in mind that the corona itself will be expanding as the Sun goes through its normal eleven year activity cycle, so we’ll have more opportunities for the Probe to pass through it.
Parker will eventually reach 8.86 solar radii, a scant 6.2 million kilometers from the solar surface, so this is a story that is far from over. The next flyby will be in January of 2022.
Findings from the recent Parker Solar Probe milestone will be published in The Astrophysical Journal, and are also examined in a paper by Kasper et al., “Parker Solar Probe Enters the Magnetically Dominated Solar Corona,” Physical Review Letters 127 (14 December 2021), 255101 (abstract).
Is Surface Ice Uncommon on Habitable Worlds?
The day is not far off when we’ll be able to look at a small planet in the habitable zone of its star and detect basic features on its surface: water, ice, land. The era of the 30-meter extremely large telescope approaches, so this may even be possible from the ground, and large space telescopes will be up to the challenge as well (which is why things like aperture size and starshade prospects loom large in our discussions of current policy decisions).
Consider this: On the Earth, while the atmosphere reflects a huge amount of light from the Sun, about half the total albedo at the poles comes from polar ice. It would be useful, then, to know more about the ice and land distribution that we might find on planets around other stars. This is the purpose of a new paper in the Planetary Science Journal recounting the creation of climate simulations designed to predict how surface ice will be distributed on Earth-like exoplanets. It’s a relatively simple model, the authors acknowledge, but one that allows rapid calculation of climate on a wide population of hypothetical planets.
Image: A composite of the ice cap covering Earth’s Arctic region — including the North Pole — taken 512 miles above our planet on April 12, 2018 by the NOAA-20 polar-orbiting satellite. Credit: NOAA.
Lead author Caitlyn Wilhelm (University of Washington) began the work while an undergraduate; she is now a research scientist at the university’s Virtual Planet Laboratory:
“Looking at ice coverage on an Earth-like planet can tell you a lot about whether it’s habitable. We wanted to understand all the parameters—the shape of the orbit, the axial tilt, the type of star—that affect whether you have ice on the surface, and if so, where.”
Thus we attempt to cancel out imprecision in the energy balance model (EBM) the paper deploys by sheer numbers, looking for general patterns like the fraction of planets with ice coverage and the location of their icy regions. A ‘baseline of expectations’ emerges for planets modeled to be like the Earth (which in this case means a modern Earth), worlds of similar mass, rotation, and atmospherics. The authors simulate more than 200,000 such worlds in habitable zone orbits.
What is being modeled here is the flow of energy between equator and pole as it sets off climate possibilities for the chosen population of simulated worlds over a one million year timespan. These are planets modeled to be in orbit around stars in the F-, G- and K-classes, which takes in our G-class Sun, and all of them are placed in the habitable zone of the host star. The simulations take in circular as well as eccentric orbits, and adjust axial tilt from 0 all the way to 90 degrees. By way of contrast, Earth’s axial tilt is 23.5 degrees. That of Uranus is close to 90 degrees. The choice of axial tilt obviously drives extreme variations in climate.
But let’s pause for a moment on that figure I just gave: 23.5 degrees. Because factors like this are not fixed, and Earth’s obliquity, the tilt of its spin axis, actually isn’t static. It ranges between roughly 22 degrees and 24.5 degrees over a timescale of some 20,000 years. Nor is the eccentricity of Earth’s orbit fixed at its current value. Over a longer time period, it ranges between a perfectly circular orbit (eccentricity = zero) to an eccentricity of 6 percent. While these changes seem small enough, they have serious consequences, such as the ice ages.
Image: The three main variations in Earth’s orbit linked to Milankovitch cycles. The eccentricity is the shape of Earth’s orbit; it oscillates over 100,000 years (or 100 k.y.). The obliquity is the tilt of Earth’s spin axis, and the precession is the alignment of the spin axis. Credit: Scott Rutherford. A good entry into all this is Sean Raymond’s blog planetplanet, where he offers an exploration of life-bearing worlds and the factors that influence habitability. An astrophysicist based in Bordeaux, Raymond’s name will be a familiar one to Centauri Dreams readers. I should add that he is not involved in the paper under discussion today.
Earth’s variations in orbit and axial tilt are referred to as Milankovitch cycles, after Serbian astronomer Milutin Milankovi?, who examined these factors in light of changing climatic conditions over long timescales back in the 1920s. These cycles can clearly bring about major variations in surface ice as their effects play out. If this is true of Earth, we would expect a wide range of climates on planets modeled this way, everything from hot, moist conditions to planet-spanning ‘snowball’ scenarios of the sort Earth once experienced.
So it’s striking that even with all the variation in orbit and axial tilt and the wide range in outcomes, only about 10 percent of the planets in this study produced even partial ice coverage. Rory Barnes (University of Washington) is a co-author of the paper:
“We essentially simulated Earth’s climate on worlds around different types of stars, and we find that in 90% of cases with liquid water on the surface, there are no ice sheets, like polar caps. When ice is present, we see that ice belts—permanent ice along the equator—are actually more likely than ice caps.”
Image. This is Figure 12 from the paper. Caption: Figure 12. Range and average ice heights of ice caps as a function of latitude for stars orbiting F (top), G (middle) and K (bottom) dwarf stars. Note the different scales of the x-axes. Light grey curves show 100 randomly selected individual simulations, while black shows the average of all simulations that concluded with an ice belt. Although the averages are all symmetric about the poles, some individual ice caps are significantly displaced. Credit: Wilhelm et al.
Breaking this down, the authors show that in their simulations, planets like Earth are most likely to be ice-free. Even oscillations in orbital eccentricity and axial tilt do not prevent, however, planets orbiting the F-, G- and K-class stars in the study from developing stable ice belts on land. Moreover, ice belts turn out to be twice as common as polar ice caps for planets around G- and K-class stars. As to size, the typical extension of an ice belt is between 10 and 30 degrees, varying with host star spectral type, and this is a signal large enough to show up in photometry and spectroscopy, making it a useful observable for future instruments.
This is a study that makes a number of assumptions in the name of taking a first cut at the ice coverage question, each of them “…made in the name of tractability as current computational software and hardware limitations prevent the broad parameter sweeps presented here to include these physics and still be completed in a reasonable amount of wallclock time. Future research that addresses these deficiencies could modify the results presented above.”
Fair enough. Among the factors that will need to be examined in continued research, all of them spelled out here, are geochemical processes like the carbonate-silicate cycle, ocean heat transport as it affects the stability of ice belts, zonal winds and cloud variability, all of this not embedded in the authors’ energy balance model, which is idealized and cannot encompass the entire range of effects. Nor do the authors simulate the frequency and location of M-dwarf planet ice sheets.
But the finding about the lack of ice in so many of the simulated planets remains a striking result. Let me quote the paper’s summation of the findings. They remove the planets ending in a moist greenhouse or snowball planet, these worlds being “by definition, uninhabitable.” We’re left with this:
…we then have 39,858 habitable F dwarf planets, 37,604 habitable G dwarf planets, and 36,921 habitable K dwarf planets in our sample. For G dwarf planets, the ice state frequencies are 92% ice free, 2.7% polar cap(s), and 4.8% ice belt. For F dwarf planets, the percentages are 96.1%, 2.9%, and 0.9%, respectively. For K dwarf planets, the percentages are 88.4%, 3.5%, and 7.6%, respectively. Thus, we predict the vast majority of habitable Earth-like planets of FGK stars will be ice free, that ice belts will be twice as common as caps for G and K dwarfs planets, and that ice caps will be three times as common as belts for Earth-like planets of F dwarfs.
And note that bit about the uninhabitability of snowball worlds, which the paper actually circles back to:
Our dynamic cases highlight the importance of considering currently ice-covered planets as potentially habitable because they may have recently possessed open surface water. Such worlds could still develop life in a manner similar to Earth, e.g. in wet/dry cycles on land, but then the dynamics of the planetary system force the planet into a snowball, which in turn forces life into the ocean under a solid ice surface. Such a process may have transpired multiple times on Earth, so we should expect similar processes to function on exoplanets.
The paper is Wilhelm et al., ”The Ice Coverage of Earth-like Planets Orbiting FGK Stars,” accepted at the Planetary Science Journal (preprint). Source code available. Scripts to generate data and figures also available.
Communicating With Aliens: Observables Versus Qualia
If we ever do receive a targeted message from another star – as opposed to picking up, say, leakage radiation – will we be able to decipher it? We can’t know in advance, but it’s a reasonable assumption that any civilization wanting to communicate will have strategies in place to ease the process. In today’s essay, Brian McConnell begins a discussion on SETI and interstellar messaging that will continue in coming weeks. The limits of our understanding are emphasized by the problem of qualia; in other words, how do different species express inner experience? But we begin with studies of other Earth species before moving on to data types and possible observables. A communication systems engineer and expert in translation technology, Brian is the author of The Alien Communication Handbook — So We Received A Signal, Now What?, recently published by Springer Nature under their Astronomer’s Bookshelf imprint, and available through Amazon, Springer and other booksellers.
by Brian McConnell
Animal Communication
What do our attempts to understand animal communication have to say about our future efforts to understand an alien transmission or information-bearing artifact, should we discover one? We have long sought to communicate with “aliens” here on Earth. The process of deciphering animal communication has many similarities with the process of analyzing and comprehending an ET transmission, as well as important differences. Let’s look at the example of audio communication among animals, as this is analogous to a modulated electromagnetic transmission.
The general methodology used is to record as many samples of communication and behavior as possible. This is one of the chief difficulties in animal communication research, as the process of collecting recordings is quite labor intensive, and in the case of animals that roam over large territories it may be impossible to observe them in much of their environment. Animals that have a small territory where they can be observed continuously are ideal.
Once these observations are collected, the next step is to understand the basic elements of communication, similar to phonemes in human speech or the letters in an alphabet. This is a challenging process as many animals communicate using sounds outside the range of human hearing, and employ sounds that are very different from human speech. This typically involves studying time versus frequency plots of audio recordings, to understand the structure of different utterances, which is also very labor intensive. This is one area where AI or deep learning can help greatly, as AI systems can be designed to automate this step, though they require a large sample corpus to be effective.
Time vs frequency plot of duck calls (click to enlarge).Credit: Brian McConnell.
The next step, once the basic units of communication are known, is to use statistical methods to understand how frequently they are used in conjunction with each other, and how they are grouped together. Zipf’s Law is an example of one method that can be used to understand the sophistication of a communication system. In human communication, we observe that the probability of a word being used is inversely proportional to its overall rank.
A log-log plot of the frequency of word use (y axis) versus word rank (x axis) from the text of Mary Shelley’s Frankenstein. Notice that the relationship is almost exactly 1/x. Image credit: Brian McConnell, The Alien Communication Handbook.
Conditional probability is another target for study. This refers to the probability that a particular symbol or utterance will follow another. In English, for example, letters are not used with equal frequency, and some pairs or triplets of letters are encountered much more often than others. Even without knowing what an utterance or group of utterances means, it is possible to understand which are used most often, and are likely most important. It is also possible to quantify the sophistication of the communication system using methods like this.
A graph of the relative frequency of use of bigrams (2 letter combinations) in English text (click to enlarge). You can see right away that some bigrams are used extensively while others very rarely occur.. Credit: Peter Norvig.
With this information in hand, it is now possible to start mapping utterances or groups of utterances to meanings. The best example of this to date is Con Slobodchikoff ’s work with prairie dogs. They turned out to be an ideal subject of study as they live in colonies, known as towns, and as such could be observed for extended periods of time in controlled experiments. Con and his team observed how their calls differed as various predators approached the town, and used a solve for x pattern to work out which utterances had unique meanings.
Using this approach, in combination with audio analysis, Con and his team worked out that prairie dogs had unique “words” for humans, coyotes and dogs, as well as modifiers (adjectives) such as short, tall, fat, thin, square shaped, oval shaped and carrying a gun. They did this by monitoring how their chirps varied as different predators approached, or as team members walked through with different color shirts, etc. They also found that the vocabulary of calls varied in different towns, which suggested that the communication was not purely instinctual but had learned components (cultural transmission). While nobody would argue that prairie dogs communicate at a human level, their communication does appear to pass many of the tests for language.
The challenge in understanding communication is that unless you can observe the communication and a direct response to something, it is very difficult to work out its meaning. One would presume that if prairie dogs communicate about predators, they communicate about other less obvious aspects of their environment that are more challenging to observe in controlled experiments. The problem is that this is akin to listening to a telephone conversation and trying to work out what is being said only by watching how one party responds.
Research with other species has been even more limited, mostly because of the twin difficulties of capturing a large corpus of recordings, along with direct observations of behavior. Marine mammals are a case in point. While statistical analysis of whale and dolphin communication suggests a high degree of sophistication, we have not yet succeeded in mapping their calls to specific meanings. This should improve with greater automation and AI based analysis. Indeed, Project CETI (Cetacean Translation Initiative) aims to use this approach to record a large corpus of whale codas and then apply machine learning techniques to better understand them.
That our success in understanding animal communication has been so limited may portend that we will have great difficulty in understanding an ET transmission, at least the parts that are akin to natural communication.
The success of our own communication relies upon the fact that we all have similar bodies and experiences around which we can build a shared vocabulary. We can’t assume that an intelligent alien species will have similar modes of perception or thought, and if they are AI based, they will be truly alien.
On the other hand, a species that is capable of designing interstellar communication links will also need to understand information theory and communication systems. An interstellar communication link is essentially an extreme case of a wireless network. If the transmission is intended for us, and they are attempting to communicate or share information, they will be able to design the transmission to facilitate comprehension. That intent is key. This is where the analogy to animal communication breaks down.
Observables
An important aspect of a well designed digital communication system is that it can interleave many different types of data or media types. Photographs are an example of one media type we may be likely to encounter. A civilization that is capable of interstellar communication will, by definition, be astronomically literate. Astronomy itself is heavily dependent on photography. This isn’t to say that vision will be their primary sense or mode of communication, just that in order to be successful at astronomy, they will need to understand photography. One can imagine a species whose primary sense is via echolocation, but has learned to translate images into a format they can understand, much as we have developed ultrasound technology to translate sound into images.
Digitized images are almost trivially easy to decode, as an image can be represented as an array of numbers. One need only guess the number of bits used per pixel, the least to most significant bit order, and one dimension of the array to successfully decode an image. If there are multiple color channels, there are a few additional parameters, but even then the parameter space is very small, and it will be possible to extract images if they are there. There are some additional encoding patterns to look for, such as bitplanes, which I discuss in more detail in the book, but even then the number of combinations to cycle through remains small.
The sender can help us out even further by including images of astronomical objects, such as planets, stars and distant nebulae. The latter are especially interesting because they can be observed by both parties, and can be used to guide the receiver in fine calibrations, such as the color channels used, scaling factors (e.g. gamma correction), etc. Meanwhile, images of planets are easy to spot, even in a raw bitstream, as they usually consist of a roundish object against a mostly black background.
An example of a raw bitstream that includes an image of a planet amid what appears to be random or efficiently encoded data. All the viewer needs to do to extract the image is to work out one dimension of the array along with the number of bits per pixel. The degree to which a circular object is stretched into an ellipse also hints at the number of bits per pixel. Credit: Brian McConnell, The Alien Communication Handbook.
What is particularly interesting about images is that once you have worked out the basic encoding schemes in use, you can decode any image that uses that encoding scheme. Images can represent scenes ranging from microscopic to cosmic scales. The sender could include images of anything, from important landmarks or sites to abstract representations of scenes (a.k.a. art). Astute readers will notice that these are uncompressed images, and that the sender may wish to employ various compression schemes to maximize the information carrying capacity of the communication channel. Compressed images will be much harder to recognize, but even if a relatively small fraction of images are uncompressed, they will stand out against what appears to be random digits, as in the example bitstream above.
Semantic Networks
The sender can take this a step further by linking observables (images, audio samples) with numeric symbols to create a semantic network. You can think of a semantic network like an Internet of ideas, where each unique idea has a numeric address. What’s more, the address space (the maximum number of ideas that can be represented) can be extremely large. For example, a 64 bit address space has almost 2 x 1019 unique addresses.
An example of a semantic network representing the relationship between different animals and their environment (click to enlarge). The network is shown in English for readability but the nodes and the operators that connect them could just as easily be based on a numeric address space.
The network doesn’t need to be especially sophisticated to enable the receiver to understand the relationships between symbols. In fact, the sender can employ a simple way of saying “This image contains the following things / symbols” by labeling them with one or more binary codes within the images themselves.
An example of an image that is labeled with four numeric codes representing properties within the image. Credit: Brian McConnell, The Alien Communication Handbook.
Observables Versus Qualia
While this pattern can be used to build up a large vocabulary of symbols that can be linked to observables (images, audio samples, and image sequences), it will be difficult to describe qualia (internal experiences). How would you describe the concept of sweetness to someone who can’t experience a sweet taste? You could try linking the concept to a diagram of a sugar molecule, but would the receiver make the connection between sugar and sweetness? Emotional states such as fear and hunger may be similarly difficult to convey. How would you describe the concept of ennui?
Imagine an alien species whose nervous system is more decentralized like an octopus. They might have a whole vocabulary around the concept of “brain lock”, where different sub brains can’t reach agreement on something. Where would we even start with understanding concepts like this? It’s likely that while we might be successful in understanding descriptions of physical objects and processes, and that’s not nothing, we may be flummoxed in understanding descriptions of internal experiences and thoughts. This is something we take for granted in human language, primarily because even with differences in language, we all share similar bodies and experiences around which we build our languages.
Yet all hope is not lost. Semantic networks allow a receiver to understand how unknown symbols are related to each other, even if they don’t understand their meaning directly. Let’s consider an example where the sender is defining a set of symbol codes we have no direct understanding of, but we have previously figured out the meaning of symbol codes that define set membership (?), greater/lesser in degree (<>), and oppositeness (?) .
Even without knowing the meaning of these new symbol codes, the receiver can see how they are related and can build a graph of this network. This graph in turn can guide the receiver in learning unknown symbols. If a symbol is linked to many others in the network, there may be multiple paths toward working out its meaning in relation to symbols that have been learned previously. Even if these symbols remain unknown, the receiver has a way of knowing what they don’t know, and can map their progress in understanding.
The implication for a SETI detection is that we may find it is both easier and more difficult to understand what they are communicating than one may expect. Objects or processes that can be depicted numerically via images, audio or image sequences may enable the formation of a rich vocabulary around them and with relative ease, while communication around internal experiences, culture, etc may remain partially understood at best.
Even partial comprehension based on observables will be a significant achievement, as it will enable the communication of a wide range of subjects. And as can be shown, this can be done with static representations. An even more interesting scenario is if the transmission includes algorithms, functions from computer programs. Then it will be possible for the receiver to interact with them in real time, which enables a whole other realm of possibilities for communication.
More on that in the next article…
Looking for Plumes on Europa
A spray of organic molecules and ice particles bursting out of an outer system moon is an unforgettable sight, as Cassini showed us at Enceladus. Finding something similar at Europa would be a major help for future missions there, given the opportunity to sample a subsurface ocean that is perhaps as deep as 160 kilometers. But Lynnae Quick (NASA GSFC), who works on the science team that produced the Europa Imaging System cameras that will fly on the Europa Clipper mission, offers a cautionary note:
“A lot of people think Europa is going to be Enceladus 2.0, with plumes constantly spraying from the surface. But we can’t look at it that way; Europa is a totally different beast.”
A good thing that Europa Clipper can produce evidence of conditions beneath the ice without the need for plumes when it begins its explorations in 2031. In fact, adds Quick, every instrument aboard the spacecraft has its own role to play in the study of that global ocean. Still, potential plumes are too important to ignore, even if finding an active, erupting Europa would by no means be as straightforward as discovering the plumes of Enceladus. The Europa evidence we have indicates faint plume activity through Galileo and Hubble data and some Earth-based telescopes.
Image: These composite images show a suspected plume of material erupting two years apart from the same location on Jupiter’s icy moon Europa. The images bolster evidence that the plumes are a real phenomenon, flaring up intermittently in the same region on the satellite. Both plumes, photographed in ultraviolet light by NASA’s Hubble’s Space Telescope Imaging Spectrograph, were seen in silhouette as the moon passed in front of Jupiter. Credit: NASA/JPL.
In the image above, notice the possible plume activity. At left is a 2014 event that appears in Hubble data, a plume estimated to be 50 kilometers high. At the right, and in the same location, is an image taken two years later by the same Hubble Imaging Spectrograph, both events seen in silhouette as the moon passed in front of Jupiter. It’s noteworthy that this activity occurs at the same location as an unusually warm spot in the ice crust that turned up in Galileo mission data from the 1990s.
Let’s now cut to a second image, showing that Galileo find. Below we see the surface of Europa, focusing on what NASA calls a ‘region of interest.’
Image: The image at left traces the location of the erupting plumes of material, observed by NASA’s Hubble Space Telescope in 2014 and again in 2016. The plumes are located inside the area surrounded by the green oval. The green oval also corresponds to a warm region on Europa’s surface, as identified by the temperature map at right. The map is based on observations by the Galileo spacecraft. The warmest area is colored bright red. Researchers speculate these data offer circumstantial evidence for unusual activity that may be related to a subsurface ocean on Europa. The dark circle just below center in both images is a crater and is not thought to be related to the warm spot or the plume activity. Credit:
NASA/ESA/W. Sparks (STScI)/USGS Astrogeology Science Center.
Getting access to the realm below the surface would obviate the need to drill through kilometers of ice in some future mission, giving us a better understanding of possible habitability. An ocean churned by activity from heated rock below the seafloor could spawn the kind of life we find around hydrothermal vents here on Earth, circulating carbon, hydrogen, oxygen, nitrogen, phosphorus, and sulfur deep within. Moreover, Europa is in an elliptical orbit that generates internal heat and likely drives geology.
Does an icy plate tectonics also exist on this moon? The Europan surface is laced with cracks and ridgelines, with surface blocks having apparently shifted. Bands that show up in Galileo imagery delineate zones where fresh material from the underlying shell appears to have moved up to fill gaps as soon as they appear. A 2014 paper (citation below) by Simon Kattenhorn (University of Idaho – Moscow) and Louise Prockter (JHU/APL) found evidence of subduction in Galileo imagery, where one icy plate seems to have moved beneath another, forcing surface material into the interior.
That paper is, in fact, worth a quote. The italics are mine:
…we produce a tectonic reconstruction of geologic features across a 134,000 km2 region of Europa and find, in addition to dilational band spreading, evidence for transform motions along prominent strike-slip faults, as well as the removal of approximately 20,000 km2 of the surface along a discrete tabular zone. We interpret this zone as a subduction-like convergent boundary that abruptly truncates older geological features and is flanked by potential cryolavas on the overriding ice. We propose that Europa’s ice shell has a brittle, mobile, plate-like system above convecting warmer ice. Hence, Europa may be the only Solar System body other than Earth to exhibit a system of plate tectonics.
This is an encouraging scenario in which surface nutrients produced through interactions with radiation from Jupiter are driven into pockets in the ice shell and perhaps into the ocean below, even as chemical activity continues at the seafloor. If we find plumes, their chemical makeup could put these scenarios to the test. But as opposed to the highly visible plumes of Enceladus, any Europan plumes would be harder to detect, bound more tightly to the surface because of the higher Europan gravity, and certainly lacking the spectacular visual effects at Enceladus.
Key to the search for plumes will be Clipper’s EIS camera suite, which can scout for activity at the surface by scanning the limb of the moon as it passes in front of Jupiter. Moreover, a plume should leave a deposit on surface ice that EIS may see. Clipper’s Europa Ultraviolet Spectrograph (Europa-UVS) will look for plumes at the UV end of the spectrum, tracking the chemical makeup of any that are detected. The Europa Thermal Emission Imaging System (E-THEMIS) will be able to track hotspots that may indicate recent eruptions. A complete description of Clipper’s instrument suite is available here.
We’ve been using Galileo data for a long time now. It’s a refreshing thought that we’ll have two spacecraft – Europa Clipper and Jupiter Icy Moons Explorer (JUICE) – in place in ten years to produce what will surely be a flood of new discoveries.
The paper on Europan plate tectonics is Kattenhorn & Prockter, “Evidence for subduction in the ice shell of Europa,” Nature Geoscience 7 (2014), 762-767 (abstract).
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