137496 b: A Rare ‘Hot Mercury’
30 November 2021 | 3:22 pm

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We haven’t had many examples of so-called ‘hot Mercury’ planets to work with, or in this case, what might be termed a ‘hot super-Mercury’ because of its size. For HD 137496 b actually fits the ‘super-Earth’ category, at roughly 30 percent larger in radius than the Earth. What makes it stand out, of course, is the fact that as a ‘Mercury,’ it is primarily made up of iron, with its core carrying over 70 percent of the planet’s mass. It’s also a scorched world, with an orbital radius of 0.027 AU and a period of 1.6 days.

Another planet, non-transiting, turns up at HD 137496 as well. It’s a ‘cold Jupiter’ with a minimum mass calculated at 7.66 Jupiter masses, an eccentric orbit of 480 days, and an orbital distance of 1.21 AU from the host star. HD 137496 c is thus representative of the Jupiter-class worlds we’ll be finding more of as our detection methods are fine-tuned for planets on longer, slower orbits than the ‘hot Jupiters’ that were so useful in the early days of radial velocity exoplanet discovery.

The discoverers of the planetary system at HD 137496, an international group led by Tomas Silva (University of Porto, Portugal), found HD 137496 b, the hot Mercury, in K2 data, its transits apparent in the star’s light curve. The gas giant HD 137496 c was then identified in radial velocity work using the reliable HARPS and CORALIE spectrographs.

The primary is a G-class star a good bit older than the Sun, its age calculated at 8.3 billion years, but with a comparable mass (1.03 solar masses), and a radius of approximately 1.50 solar radii.

Image: HARPS (orange) and CORALIE (blue) radial velocities. In this figure, we present our RV time series. As is clearly seen, the data show a long-term and high-amplitude trend (semiamplitude of ~ 200 m s-1), typical of the signature of a long period giant planet. Credit: Silva et al.

A hot Mercury should turn out to be a useful find in a variety of ways. As the paper notes:

HD 137496 b (K2-364 b) joins the small sample of well characterized dense planets, making it an interesting target for testing planet formation theories, density enhancing mechanisms, and even the possible presence of an extended cometlike mineral rich exosphere. Together with HD 137496 c (K2-364 c), a high-mass (mass ratio…, high-eccentricity planet, this system presents an interesting architecture for planetary evolution studies. Future astrometric observations could also provide significant constraints on the relative inclination of the planetary orbits, unraveling new opportunities to discover the system’s dynamical history.

Keep in mind that most of the planets we now know about have radii somewhere between that of Earth and Neptune. In this range, numerous different system architectures are in play, and a wide variety of possible formation scenarios. As the authors note, high-density planets like HD 137496 b are distinctly under-sampled, which has been a check on theories of planet formation that would accommodate them.

And the theorists are going to have their hands full with this one. HD 137496 b’s parent star shows too little iron to form a planet with this density. I’m going to quote Sasha Warren on this. Working on a PhD at the University of Chicago, Warren focuses on how planetary atmospheres have evolved, particularly those of Mars and Venus. Of HD 137496 b, she has this to say in a recent article on astrobites about how such planets can become more iron-rich:

Firstly, the protoplanetary disks of dust and gas within which planets form around young stars can change in composition as a function of distance from the star. So, it is possible that a combination of high temperatures and magnetic interactions between the host star and the protoplanetary disk concentrated iron-rich materials where HD 137496 b originally formed. This could mean star compositions might not be very useful to help understand what short period rocky planets are made of. Secondly, planets close to their stars like HD 137496 b are so hot that their rocky surfaces can sometimes just evaporate away!

It will be fascinating to see how our theories evolve as we begin to expand the catalog of hot Mercury planets. 137496 b is only the fifth world in this category yet discovered.

The paper is Silva et al., “The HD 137496 system: A dense, hot super-Mercury and a cold Jupiter,” in process at Astronomy & Astrophysics (preprint).

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Wolf 359: Of Gravitational Lensing and Galactic Networks
26 November 2021 | 12:54 pm

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If self-reproducing probes have ever been turned loose in the Milky Way, they may well have spread throughout the galaxy. Our planet is 4.6 billion years old, but the galaxy’s age is 13 billion, offering plenty of time for this spread. A number of papers have explored the concept, including work by Frank Tipler, who in 1980 argued that even at the speed of current spacecraft, the galaxy could be completely explored within 300 million years. Because we had found no evidence of such probes, Tipler concluded that extraterrestrial technological civilizations did not exist.

Robert Freitas also explored the consequences of self-reproducing probes in that same year, reaching similar conclusions about how quickly they would spread, although not buying Tipler’s ultimate conclusion. It’s interesting that Freitas went to work on looking for evidence, reasoning that halo orbits around the Lagrangian points might be one place to search. He was, to my knowledge, the first to use the term SETA — Search for Extraterrestrial Artifacts — which has now come into common use, and is currently under examination by Jim Benford in his work on ‘lurkers.’

A new paper from Michaël Gillon (University of Liège) and Artem Burdanov (Massachusetts Institute of Technology) has now appeared that follows the implications of self-reproduction and technology, tying them to a more specific search regimen. Conversant with the work of Von Eshleman as well as Claudio Maccone, the authors ask whether using the gravitational lens offered by a star wouldn’t make the most reasonable method for ETI communications. The Sun’s huge magnifications, bending light from objects behind it as seen from a relay somewhere beyond its 550 AU lensing distance, could enable participation in a network that functioned on a galactic scale.

You probably remember Gillon as the man who led the team that discovered TRAPPIST-1’s planets. Back in 2014, he began his exploration of gravitational lensing and communications with the publication of a paper titled “A novel SETI strategy targeting the solar focal regions of the most nearby stars.” Accepting the idea that self-reproducing probes could spread through the galaxy in a span of hundreds of millions of years, the author opened the question of detectability. He drew on Maccone’s insight that links enabled by gravitational lensing could allow data-rich communications between two stars at extremely low power. It is in this 2014 paper that Gillon first proposes looking for leakage in traffic between star systems.

A civilization that has spread throughout the galaxy might set up such relays around any stars useful as network nodes. This would turn conventional SETI on its head. Rather than scanning for radio or optical signals from other stellar systems, we consider intercepting ongoing traffic between another star and the relay in our own system. A fully colonized galaxy, so the thinking goes, should have a relay around at least one nearby star.

Thus the term Focal Interstellar Communication Devices (FICDs), examples of which could be present in our own Solar System and perhaps in the focal regions of nearby stars. Several studies have already appeared on a strategy of performing intense multi-spectral monitoring of these focal regions in the hopes of snagging communication leakage from such a network. Gillon and Burdanov focus on a specific FICD. They identify Wolf 359, an M-dwarf that is the third closest stellar system to our own, as a prime candidate to receive a signal from a local FICD, and implement an optical search.

Why Wolf 359? Ponder this:

…detecting the FICD emission to a nearby star can only be done if the observer is within one of these narrow beams, putting a stringent geometrical constraint on the project concept. For an Earth-based observer, this means that the Earth’s minimum impact parameter has to be close to 1 as seen from the FICD, and thus also from the targeted nearby star. In other words, the Earth has to be a transiting (or nearly transiting) planet for one of the nearest stars to give this SETI concept a chance of success, so the target star has to be very close to the ecliptic plane. With its nearly circular orbit and its semi-major axis 215 times larger than the solar radius, the Earth has a mean transit probability < 0.5% for any random star of the solar neighborhood.

Image: An artist’s depiction of an active red dwarf star like Wolf 359 orbited by a planet. Credit: David A. Aguilar.

In other words, because the Earth transits the Sun as seen from Wolf 359, our planet would pass through any communication beam between the star and a local probe once per orbit. Thus a signal to Wolf 359 from an FICD in our Sun’s gravitational lensing region could in principle be detected. Gillon and Burdanov put the idea to the test using the TRAPPIST-South and SPECULOOS Southern Observatory in Chile, in a search “sensitive enough to detect constant emission with emitting power as small as 1W.”

The result: No detections. This could indicate that no probes exist within the Solar System using these methods, or at least that such a probe did not transmit during the observations. Indeed, the list of hypotheses to explain a null result is so large that no conclusion can be drawn. No detection simply means no detection.

But the observations lead us further to consider the spectral range of possible emissions from FICD to star. This is going to change depending on the star. Remember that using gravitational lensing to enable communications forces the receiver to face the host star, blocking its light with some kind of occulter (or perhaps a coronagraph) while enabling the signal to be received. Gillon and Burdanov note that Wolf 359 is a flare star with strong coronal activity, one with significant emission of X-ray and extreme ultraviolet light. The authors determine ‘a spectral zone of minimal emission’ that becomes interesting as a communications channel. Here let’s turn back to the paper, for this zone may be a better place to look:

While the very low emission of late-type M-dwarfs in this spectral range could be an issue for prebiotic chemistry on habitable planets (Rimmer et al. 2018), it could represent a nice spectral ’sweet spot’ for a GL-based communication to a late M-dwarf like Wolf 359 or TRAPPIST-1. Another advantage of using this wavelength range instead of the optical range is the improved emission rate, thanks to the narrower laser beams… These considerations suggest that the spectral ranges 300-920nm and 400-950nm probed by the TRAPPIST-South and SPECULOOS South observations could not correspond to the optimal spectral range for a GL-based communication [gravitational lensing] from the solar system to Wolf 359. The 150-250 nm spectral range could represent a more optimal spectral range for such GL-based interstellar communication to a cold and active late-type M-dwarf like Wolf-359.

Image: This is Figure 2 from the paper. Caption: Illustration showing the geometry of the hypothesized communication link from the solar system to the Wolf 359 system. The distances and stellar sizes are not to scale. Wolf 359 is shown at 3 different positions. Position 1 corresponds to the time of the emission of the photons that we receive from it now. Position 2 corresponds to its current position. Position 3 corresponds to the time it will receive the photons emitted now by the FICD. Credit: Gillon & Burdanov.

Probing this spectral range would require a space-based instrument, but it would be interesting to target these frequencies in a reproduction of the Wolf 359 observations. This paper recounts the first attempt to detect optical messages emitted from the Solar System to this star, and as such seems intended primarily as a way to shake out observing methods and explore how gravitational lens-based networking could be observed.

The paper is Gillon & Burdanov, “Search for an alien communication from the Solar System to a neighbor star,” submitted to Monthly Notices of the Royal Astronomical Society (preprint). Gillon’s 2014 paper is “ “A novel SETI strategy targeting the solar focal regions of the most nearby stars,” Acta Astronautica Vol. 94, Issue 2 (February 2014), 629-633 (abstract).

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Deep Learning Methods Flag 301 New Planets
24 November 2021 | 5:01 pm

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It’s no small matter to add 301 newly validated planets to an exoplanet tally already totalling 4,569. But it’s even more interesting to learn that the new planets are drawn out of previously collected data, as analyzed by a deep neural network. The ‘classifier’ in question is called ExoMiner, describing machine learning methods that learn by examining large amounts of data. With the help of the NASA supercomputer called Pleiades, ExoMiner seems to be a wizard at separating actual planetary signatures from the false positives that plague researchers.

ExoMiner is described in a paper slated for The Astrophysical Journal, where the results of an experimental study are presented, using data from the Kepler and K2 missions. The data give the machine learning tools plenty to work with, considering that Kepler observed 112,046 stars in its 115-degree square search field, identifying over 4000 candidates. More than 2300 of these have been confirmed. The Kepler extended mission K2 detected more than 2300 candidate worlds, with over 400 subsequently confirmed or validated. The latest 301 validated planets indicate that ExoMiner is more accurate than existing transit signal classifiers.

How much more accurate? According to the paper, ExoMiner retrieved 93.6% of all exoplanets in its test run, as compared to a rate of 76.3% for the best existing transit classifier.

We see many more candidate planets than can be readily confirmed or identified as false positives in all our large survey missions. TESS, the Transiting Exoplanet Survey Satellite, for example, working with an area 300 times larger than Kepler’s, has detected 2241 candidates thus far, with about 130 confirmed. Obviously, pulling false positives out of the mix is difficult using our present approaches, which is why the ExoMiner methods are so welcome.

Hamed Valizadegan is ExoMiner project lead and machine learning manager with the Universities Space Research Association at NASA Ames:

“When ExoMiner says something is a planet, you can be sure it’s a planet. ExoMiner is highly accurate and in some ways more reliable than both existing machine classifiers and the human experts it’s meant to emulate because of the biases that come with human labeling…Now that we’ve trained ExoMiner using Kepler data, with a little fine-tuning, we can transfer that learning to other missions, including TESS, which we’re currently working on. There’s room to grow.”

Image: Over 4,500 planets have been found around other stars, but scientists expect that our galaxy contains millions of planets. There are multiple methods for detecting these small, faint bodies around much larger, brighter stars. The challenge then becomes to confirm or validate these new worlds. Credit: NASA/JPL-Caltech.

The paper describes the most common approach to detecting exoplanet candidates and vetting them. Imaging data are processed to identify ‘threshold crossing events’, after which a transit model is fitted to each signal, with diagnostic tests applied to subtract non-exoplanet effects. This produces data validation reports for these crossing events, which in turn are filtered to identify likely exoplanets. The data validation reports for the most likely events are then reviewed by vetting teams and released as objects of interest for follow-up work.

Machine learning (ML) methods speed the process. As described in the paper:

ML methods are ideally suited for probing these massive datasets, relieving experts from the time-consuming task of sifting through the data and interpreting each DV report, or comparable diagnostic material, manually. When utilized properly, ML methods also allow us to train models that potentially reduce the inevitable biases of experts. Among many different ML techniques, Deep Neural Networks (DNNs) have achieved state-of-the-art performance (LeCun et al. 2015) in areas such as computer vision, speech recognition, and text analysis and, in some cases, have even exceeded human performance. DNNs are especially powerful and effective in these domains because of their ability to automatically extract features that may be previously unknown or highly unlikely for human experts in the field to grasp…

The ExoMiner software learns by using data on exoplanets that have been confirmed in the past, and also by examining the false positives thus far generated. Given the sheer numbers of threshold crossing events Kepler and K2 have produced, automated tools to examine these massive datasets greatly facilitate the confirmation process. Remember that two goals are defined here. A ‘confirmed’ planet is one that is detected via other observational techniques, as when radial velocity methods, for example, are applied to identify the same planet.

A planet is ‘validated’ statistically when it can be shown how likely the find is to be a planet based on the data. The 301 new exoplanets are considered machine-validated. They have been in candidate status until ExoMiner went to work on them to rule out false positives. As with the analysis we examined yesterday, refining filtering techniques at Proxima Centauri to screen out flare activity, this work will be applied to future catalogs from TESS and the ESA’s PLATO mission. According to Valizadegan, the team is already at work using ExoMiner with TESS data.

Usefully, ExoMiner offers what the authors call “a simple explainability framework” that provides feedback on the classifications it makes. It isn’t a ‘black box,’ according to exoplanet scientist Jon Jenkins (NASA Ames), who goes on to say: “We can easily explain which features in the data lead ExoMiner to reject or confirm a planet.”

Looking forward, the authors explain the keys to ExoMiner’s performance. The reference to Kepler Objects of Interest (KOIs) below refers to a subset defined within the paper:

[S]ince the general concept behind vetting transit signals is the same for both Kepler and TESS data, and ExoMiner utilizes the same diagnostic metrics as expert vetters do, we expect an adapted version of this model to perform well on TESS data. Our preliminary results on TESS data verify this hypothesis. Using ExoMiner, we also demonstrate that there are hundreds of new exoplanets hidden in the 1922 KOIs that require further follow-up analysis. Out of these, 301 new exoplanets are validated with confidence using ExoMiner.

The paper is Valizadegan et al., “ExoMiner: A Highly Accurate and Explainable Deep Learning Classifier to Mine Exoplanets,” accepted at The Astrophysical Journal (preprint).

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