Strange bedfellows
Planets with peculiar atmospheres, relative to a representative sample, should be regarded as the most likely settings for extraterrestrial life. The parameters for ‘anomalousness’ should be data-dependent, rather than being based on assumptions about life that may be Earth-centric.
“Conceptually, there must be some common thread between all things in the universe that we want to describe as being alive,” says Kinney, who co-authored the paper, published June 22 in Biology & Philosophy, outlining their theory.
In moving away from the assumption that the thread must be chemical, Kinney and Kempes hope to avoid some common pitfalls, namely abiotic processes that mimic biotic ones. “There has been a long history in exoplanet research of people finding abiotic mechanisms that produce candidate biosignature gases,” says Kinney. “Our method circumvents this issue a bit by saying ‘let the data tell us what is anomalous.’”
Still their argument does rest on a few core assumptions. First that a given sample of exoplanets can be statistically representative of all the atmospheres in the universe. While over 5,000 exoplanet candidates have been confirmed, scientists estimate that there are hundreds of billions of planets within the Milky Way alone. It also assumes that life in that set of observable exoplanets is rare and that living organisms tend to leave biosignatures in the planets they inhabit.
Although each of these assumptions can be questioned, it follows that if the chemical composition of a planet is unusual, then a possible cause of this unusual composition is that life exists on that planet. The foundation of their method comes from a paper published in Astrobiology in 2016 in which a list of roughly 14,000 compounds likely to appear as gasses in the atmospheres of extrasolar planets’ is outlined.
“A key takeaway from our paper is that when science is conducted under conditions of deep uncertainty, a scientist often must be willing to speculate,” says Kemples. “That is, they must be ready to make assumptions that go beyond their data, and to then explore the consequences of those assumptions. Whatever one discovers very likely won't verify those initial assumptions, but this method can nevertheless lead to extraordinary breakthroughs.”