The field of extrasolar planet studies is changing. 4,940 exoplanets have been confirmed in 3,711 planetary systems, with another 8,709 candidates waiting to be confirmed. With so many planets available for study, the focus is transitioning from discovery to characterization. Astrobiologists will look for potentially-habitable worlds instead of looking for more planets.
One of the most important of which is water is the chemical signatures associated with life and biological processes. Water is considered the divining rod for finding life because it is the only solvent that can't exist. The presence of water, snow, and clouds on distant exoplanets can be discerned by future surveys and machine learning.
Dang Pham is a graduate student in the David A. Dunlap Department of Astronomy and Astrophysics at the University of Toronto. Lisa Kaltenegger is an Associate Professor in Astronomy at Cornell University and the Director of the Carl Sagan Institute.
All life on Earth depends on water, which is why it is important for surveys. This importance is reflected in NASA's slogan "just follow the water", which inspired the title of their paper, according to Lisa Kaltenegger.
“Liquid water on a planet’s surface is one of the smoking guns for potential life – I say potential here because we don’t know what else we need to get life started. But liquid water is a great start. So we used NASA’s slogan of “Just follow the water” and asked, how can we find water on the surface of rocky exoplanets in the Habitable Zone? Doing spectroscopy is time intensive, thus we are searching for a faster way to initially identify promising planets – those with liquid water on it.”
Calculating the presence of hydrogen gas in an exoplanet's atmosphere is limited to looking for a certain type of line absorption. This is a result of atmospheric water vapor being exposed to solar ultraviolet radiation, which causes it to become hydrogen and O 2, which are lost to space.
Next-generation telescopes like the James Webb (JWST) and Nancy Grace Roman Space Telescopes (RST), as well as next-next-generation observatories like the Origins Space Telescope (HabEx), are about to change this. There are also ground-based telescopes that can be used.
Thanks to their large primary mirrors and advanced suite of spectrographs, these instruments will be able to conduct Direct Imaging studies of exoplanets. Astronomers can see what chemical elements are present in the light reflected from an exoplanet. This is a lengthy process according to their paper.
Astronomers begin by observing thousands of stars for periodic dips in brightness, and then look at the light curves for signs of chemical signatures. amateur astronomer and machine algorithms are used to sort through the volumes of data their telescopes get. More advanced machine learning will be crucial.
Astronomers will be able to prioritize targets for follow-up observations with the help of MI techniques. Astronomers will be able to dedicate more of an observatory's valuable survey time to exoplanets that are more likely to provide significant returns.
Kaltenegger said that next- generation telescopes will look for water on the surface of planets.
Machine learning allows us to quickly identify optimal filters, as well as the trade-off in accuracy at various signal-to-noise ratios. In the second task, we can see how much better the algorithm is. We can draw a line where getting more signal wouldn't correspond to better accuracy.
To make sure they were up to the task, they did a lot of calibrating. This consisted of creating 53,130 profiles of a cold Earth with various surface components. They created a simulation of the water's atmosphere, surface reflectivity, and color profiles. As Pham explained.
“The atmosphere was modeled using Exo-Prime2 — Exo-Prime2 has been validated by comparison to Earth in various missions. The reflectivity of surfaces like snow and water are measured on Earth by USGS. We then create colors from these spectra. We train XGBoost on these colors to perform three separate goals: detecting the existence of water, the existence of clouds, and the existence of snow.”
Clouds and snow have a higher albedo than water so it is easier to identify them. They found five optimal filters that were 0.2 micrometers broad and in the visible light range. The final step was to perform a mock probability assessment to evaluate their planet model regarding liquid water, snow, and clouds from a set of five optimal filters.
We used a non- machine learning method to do the same task on the five optimal filters as a brief Bayesian analysis.
They were surprised to see how well the trained XGBoost could identify water on the surface of rocky planets. Imagine a bin for all red light, that's what filters really are, according to Kaltenegger.
Their proposed method doesn't identify water in exoplanet atmospheres, but on an exoplanet's surface via photometry. The transit method will not work with it. The most widely-used and effective means of exoplanet detection is transit photometry. The method involves observing distant stars for periodic dips in luminosity attributed to exoplanets passing in front of them.
As the sun passes through an exoplanet's atmosphere, it can give a glimpse of its atmosphere. The JWST will rely on this method to determine the atmospheres of exoplanets.
The reflected light from the exoplanets will be the only source of data for the algorithm. It's good news that the results of the studies will reveal more about exoplanets than just the chemical composition of their atmospheres. According to Kaltenegger, this creates a lot of opportunities.
“This is opening up the opportunity for smaller space missions like the Nancy Roman telescope to help identify worlds that could host life. And for larger upcoming telescopes – as recommended by the decadal survey – it allows them to scan the rocky planets in the Habitable Zone for the most promising candidates – those with water on their surface, so we spend the time characterizing the most interesting ones – and effectively search for life on planets that have great conditions for it to get started.”
The paper about their findings was published in the monthly Notices of the Royal Astronomical Society.
Further reading: arXiv
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