Numerical Astrobiology – surprisingly, not an oxymoron…

This was going to be an exclusive scoop for an exciting, current science item that was making waves in the blogosphere, and sparking passionate responses of every shape and hue.  Instead, this post comes to you after the fact, a retrospective look at research that made the headlines…

The story begins with an idea. It came to me as most ideas do, in a place and time when you should be thinking about something else.  I was sitting in a lecture about the Search for Extraterrestrial Intelligence (SETI), and its current successes/failures (feel free to comment on them!).  I had been working on Monte Carlo methods at the time: for those not in the know, Monte Carlo methods use stochastic (probability-driven) techniques to attack problems where other analytic or numerical methods cannot gain a foothold.

It then occurred to me that Monte Carlo methods could be applied to questions of SETI.  We could create a mock Galaxy, with stars and planets, and watch life’s progress on a planet-by-planet basis.  Of course, we don’t have a complete census of every star and planet in the Galaxy, but we do have enough information to construct probability distribution functions (PDFs) for each variable: stellar mass, galactocentric radius, planetary system architecture, etc.  Every time we create a new star system, we sample from these PDFs so that the mock Galaxy is statistically identical to ours.  Then, we can evolve life using stochastic equations, which lets us account for extinction events and other probabilistic elements of the evolutionary process.

By running the code several times, we could ascribe sampling errors to our results (albeit massively underestimating them).  Of course, we don’t know how life forms on other worlds, but we can compare different theories of Life and Intelligence on a level playing field, with realistic potential niches and environments (see the paper for more).

You may have spotted the main weakness of the code: because the method relies on observational statistics as input (the distribution of stellar masses, the structure of the Galaxy, exoplanet statistics, etc.), the simulation of potential niches is biased by our ignorance.  At the time of press, we’ve identified less than 400 exoplanets, and the identification process itself is fraught with bias.  That aside, even if we could input every star and planet in the Galaxy individually, we still clutch at straws when it comes to simulating Life’s origin, and are forced to make broad assumptions that, although sensible and based on good science, could easily be wrong.

However, this crisis is also an opportunity: the results will improve as our data improves.  As we learn more about the planets (and how life on Earth came to be) we can quickly feed that knowledge into the code, and gradually move towards the truth.  The algorithm is an alternative to the Drake Equation, with the added advantage that spatio-temporal information (great phrase) is more intuitively incorporated.  An alternative, not a replacement – we need to use all the tools at our disposal, and the Drake Equation is beautifully simple, and easy to use (and life is being breathed into it by Claudio Maccone in the form of the Statistical Drake Equation).

This work resulted in a maelstrom of media attention: I’ll save further thoughts on my experiences with the public/media for another post.  The field of astrobiology is not without controversy: I encourage you to debate with me via comments.  Thanks for reading!


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