In 1950, while sitting down to lunch with colleagues at the Los Alamos Laboratory, famed physicist and nuclear scientist Enrico Fermi asked his famous question: “Where is Everybody?” In short, Fermi was addressing the all-important question that has plagued human minds since they first realized planet Earth was merely a speck in an infinite Universe. Given the size and age of the Universe and the way the ingredients for life are seemingly everywhere in abundance, why haven’t we found any evidence of intelligent life beyond Earth?
This question has spawned countless proposed resolutions since Fermi’s time, including the infamous Hart-Tipler Conjecture (i.e., they don’t exist). Other interpretations emphasize how space travel is hard and extremely time and energy-consuming, which is why species are likely to settle in clusters (rather than a galactic empire) and how we are more likely to find examples of their technology (probes and AI) rather than a species itself. In a recent study, mathematician Daniel Vallstrom examined how artificial intelligence might be similarly motivated to avoid spreading across the galaxy, thus explaining why we haven’t seen them either!
Since time immemorial, humans have gazed up at the stars and wondered if we’re alone in the universe. We have asked if there are other intelligent beings out there in the vastness of the cosmos, also known as extraterrestrial intelligence (ET). Yet, despite our best efforts, we have yet to confirm the existence of ET outside of the Earth. While the search continues, it’s fair to speculate if they might look “human” or humanoid in appearance, or if they could look like something else entirely. Here, we present a general examination and discussion with astrobiologists pertaining to what ET might look like and what environmental parameters (e.g., gravity, atmospheric makeup, stellar activity) might cause them to evolve differently than humans.
In 2017, humanity got its first glimpse of an interstellar object (ISO), known as 1I/’Oumuamua, which buzzed our planet on its way out of the Solar System. Speculation abound as to what this object could be because, based on the limited data collected, it was clear that it was like nothing astronomers had ever seen. A controversial suggestion was that it might have been an extraterrestrial probe (or a piece of a derelict spacecraft) passing through our system. Public fascination with the possibility of “alien visitors” was also bolstered in 2021 with the release of the UFO Report by the ODNI.
This move effectively made the study of Unidentified Aerial Phenomena (UAP) a scientific pursuit rather than a clandestine affair overseen by government agencies. With one eye on the skies and the other on orbital objects, scientists are proposing how recent advances in computing, AI, and instrumentation can be used to assist in the detection of possible “visitors.” This includes a recent study by a team from the University of Strathclyde that proposes how hyperspectral imaging paired with machine learning could lead to an advanced data pipeline for characterizing UAP.
Astronomy has entered the era of big data, where astronomers find themselves inundated with information thanks to cutting-edge instruments and data-sharing techniques. Facilities like the Vera Rubin Observatory (VRO) are collecting about 20 terabytes (TB) of data on a daily basis. Others, like the Thirty-Meter Telescope (TMT), are expected to gather up to 90 TB once operational. As a result, astronomers are dealing with 100 to 200 Petabytes of data every year, and astronomy is expected to reach the “exabyte era” before long.
In response, observatories have been crowdsourcing solutions and making their data open-access so citizen scientists can assist with the time-consuming analysis process. In addition, astronomers have been increasingly turning to machine learning algorithms to help them identify objects of interest (OI) in the Universe. In a recent study, a team led by the University of Georgia revealed how artificial intelligence could distinguish between false positives and exoplanet candidates simultaneously, making the job of exoplanet hunters that much easier.
Gamma-ray bursts come in two main flavors, short and long. While astronomers believe that they understand what causes these two kinds of bursts, there is still significant overlap between them. A team of researchers have proposed a new way to classify gamma-ray bursts using the aid of machine learning algorithms. This new classification scheme will help astronomers better understand these enigmatic explosions.
Astronomers have been assessing a new machine learning algorithm to determine how reliable it is for finding gravitational lenses hidden in images from all sky surveys. This type of AI was used to find about 5,000 potential gravitational lenses, which needed to be confirmed. Using spectroscopy for confirmation, the international team has now determined the technique has a whopping 88% success rate, which means this new tool could be used to find thousands more of these magical quirks of physics.
Looking to the future, astronomers are excited to see how machine learning – aka. deep learning and artificial intelligence (AI) – will enhance surveys. One field that is already benefitting in the search for extrasolar planets, where researchers rely on machine-learning algorithms to distinguish between faint signals and background noise. As this field continues to transition from discovery to characterization, the role of machine intelligence is likely to become even more critical.
Take the KeplerSpace Telescope, which accounted for 2879 confirmed discoveries (out of the 4,575 exoplanets discovered made to date) during its nearly ten years of service. After examining the data collected by Kepler using a new deep-learning neural network called ExoMiner, a research team at NASA’s Ames Research Center was able to detect 301 more planetary signals and add them to the growing census of exoplanets.
When it comes to Mars exploration, NASA has more success than any other agency. This week, they’ll attempt to land another sophisticated rover on the Martian surface to continue the search for evidence of ancient life. The Mars Perseverance rover will land on Mars on Thursday, February 18th, and it’s bringing some very ambitious technologies with it.
Does the life of an astronomer or planetary scientists seem exciting?
Sitting in an observatory, sipping warm cocoa, with high-tech tools at your disposal as you work diligently, surfing along on the wavefront of human knowledge, surrounded by fine, bright people. Then one day—Eureka!—all your hard work and the work of your colleagues pays off, and you deliver to humanity a critical piece of knowledge. A chunk of knowledge that settles a scientific debate, or that ties a nice bow on a burgeoning theory, bringing it all together. Conferences…tenure…Nobel Prize?
Well, maybe in your first year of university you might imagine something like that. But science is work. And as we all know, not every minute of one’s working life is super-exciting and gratifying.