Does alien life exist? Astronomers have traditionally sought biosignatures — analyzing light from distant exoplanets for signs of things like oxygen — to find out if a planet could be habitable. However, a weakness of this approach is how we know of just one planet with life in the universe—our own—which means that scientists could overlook biosignatures from extraterrestrial life that is totally unlike any on Earth.
Now scientists at the California Institute of Technology and their colleagues have devised a way they suggest could find aliens even if they are not life as we know it. This new strategy depends on so-called “epsilon machines.”
WHAT IS AN EPSILON MACHINE? — An epsilon machine is a series of sophisticated algorithms designed to compute the statistical complexity of data. (Read: Not a physical machine.) The aim of the new paper, published Monday in Nature Astronomy, is to look at several factors — including how complex the systems in the world seem to be — to use it as a sign of life, even if the conditions don’t quite seem like Earth.
Epsilon machines come from the field of complexity science, which investigates the ways in which complexity in systems can often make the whole appear greater than the sum of its parts, such as the behavior of flocks from individual birds, or consciousness from individual brain cells.
"In the case of life, we can see intuitively that Earth appears to be more complex than other planets," Stuart Bartlett, an astrobiologist and complexity scientist at the California Institute of Technology and lead author of the study, tells Inverse. One example of the dramatic effects that life can have on a world's surface was the Great Oxygenation Event, when early life on Earth flooded the atmosphere with oxygen and "changed the face of the entire planet," Bartlett says.
What does this mean? In order to find life that, for example, is based on silicon instead of carbon, we should look at if it has richer dynamics of the world as a whole — a complex atmosphere, for instance — and see it as a potential biosignature.
This could help scientists piece together a picture of a world observed by the James Webb Space Telescope, which will break down the components of an exoplanet’s atmosphere. An epsilon machine could step in and determine if it has the kind of complexity that — even in the absence of traditional biomarkers — is conducive to some form of life.
Epsilon machines were originally designed to probe chaotic systems, ones that might appear completely random but actually possess hidden levels of structure, Bartlett says. Previously, they have found use investigating everything from fluid dynamics to stock markets.
In calculus, epsilon refers to anything small. Epsilon machines get their name from how one can imagine all of nature storing and processing information like a computer, with epsilon machines designed to analyze computations "in each and every location in space," down to the most infinitesimal point, James Crutchfield, co-developer of the epsilon machine and director of the Complexity Sciences Center at the University of California at Davis, tells Inverse.
WHAT DID THEY DO? — The scientists investigated the way in which light reflected or emitted from planets varied over time. Specifically, they analyzed images of Earth from the Deep Space Climate Observatory (DSCOVR) satellite to help examine a planet with life and data of Jupiter from the Cassini spacecraft’s 44-day encounter with the world on its way to Saturn as an example of a lifeless world.
The researchers also took DSCOVR data to create a number of alternate Earths that lacked clouds, land, oceans, flora, or some combination thereof. The aim was to see how the apparent complexity of Earth changed as its surface or atmosphere was simplified.
WHAT DID THEY FIND? — On average, Earth's statistical complexity appeared 50 percent higher than Jupiter’s. In addition, Earth-like planets were more statistically complex the more surface types and atmospheric features they possessed. This suggests that statistical complexity may indeed be an effective measure of the complexity of a planet's features. "I would say the result is preliminary, but exciting," says Crutchfield, who did not take part in this work.
All in all, "I think the most important implication of these results is that we do in fact have options when it comes to searching for life as we don't know it," Bartlett says. "Even if we can't predict what alien life will be like—how does one imagine the unimaginable?—we can make progress in extracting more general properties from the one example of life that we know. We can then use those properties to inform our search for life."
WHAT ARE THE CAVEATS? — As with any other technique for hunting biosignatures, there is the potential for false positives — planets that are very complex but with no biosphere — and false negatives, such as planets with low apparent complexity but hidden or dormant biospheres, Bartlett notes.
In addition, one might argue that many features contributing to Earth's complexity have nothing to do with life, such as clouds and variations on its surface.
"In response to this valid criticism, I suggest the following thought experiment, in which Earth goes through the last 4 billion years of its history in the complete absence of life," Bartlett says. "That means no biological influence on the hydrological, carbon, nitrogen or sulfur cycles, no biological influence on greenhouse gases, no biological influence on plate tectonics through biogeochemistry, no Great Oxygenation Event, and the complete absence of the myriad other, potentially unknown, influences of life on its planetary environment. If we could run such an experiment is it likely that the Earth's clouds and surface features would be the same as what we see today, in the presence of the biosphere? I think everyone would agree that such a sterile Earth would be very different."
In Bartlett's opinion, "in the absence of biological feedbacks, Earth would have settled into one of two climate extremes—a snowball, frozen world, or a sweltering desert world. In either case, I would not expect to see a similar complexity of cloud features and variations in surface types. Indeed, Jupiter has a complex cloud system and had significantly lower complexity."
Ultimately, the only real way to see if planets with life are more complex than lifeless ones is to find alien life. "The association between life and complexity is still a hypothesis, because we only have one example of life," Bartlett says.
WHAT'S NEXT? — The scientists aim to apply epsilon machines to analyze data from Venus, Mars, Saturn, and possibly other bodies in the solar system. They are also currently exploring other complexity-based approaches to detecting biosignatures "that may provide even stronger distinctions between planet types," Bartlett says.
Abstract — We present a new approach to exoplanet characterization using techniques from complexity science, with potential applications to biosignature detection. This agnostic method makes use of the temporal variability of light reflected or emitted from a planet. We use a technique known as epsilon machine reconstruction to compute the statistical complexity, a measure of the minimal model size for time series data. We demonstrate that statistical complexity is an effective measure of the complexity of planetary features. Increasing levels of qualitative planetary complexity correlate with increases in statistical complexity and Shannon entropy, demonstrating that our approach can identify planets with the richest dynamics. We also compare Earth time series with Jupiter data, and find that for the three wavelengths considered Earth’s average complexity and entropy rate are approximately 50% and 43% higher than Jupiter’s, respectively. The majority of schemes for the detection of extraterrestrial life rely upon biochemical signatures and planetary context. However, it is increasingly recognized that extraterrestrial life could be very different from life on Earth. Under the hypothesis that there is a correlation between the presence of a biosphere and observable planetary complexity, our technique offers an agnostic and quantitative method for the measurement thereof.
