The search for extraterrestrial life in the universe has gotten much more interesting in the last few years. Astronomers have made significant strides in identifying earth-like planets throughout the galaxy and beyond. In fact, there are an estimated one quadrillion earth-like planets in the observable universe.  

And as you may have guessed, the process for seeking out signs of life is a highly data-intensive one. Scientists must analyze patterns in the cosmos and bursts of energy that happen at particular frequencies and particular times. Telescopes around the world collect astonishingly large volumes of data, and it’s up to astrobiologists to draw patterns from those raw datasets, capture statistics and identify anomalies across radio signals, light waves, and advanced thermodynamics.

It has fallen to the field of data science to help these astrobiologists extract and analyze both structured and unstructured data to determine the likelihood of any planets that might harbor life. It’s a fascinating field of study that finally has the tools required to succeed. 

Data Science in Astrobiology: Searching for Relevant Data

The type of data that astrobiologists search for (both outside our planet and on our planet in extreme environments), falls into two key categories: biosignatures and technosignatures. The types of data analysis for each one varies, one involving simulations and creating new data, while the second involves analyzing anomalies. 

  1. Biosignatures demonstrate the presence of a substance or feature that indicates that life may be there, or may have been there in the past. It represents evidence of life in the context of the system that is being observed. For example, the biosignature of a moon or planet might include chemical or organic compounds, visible macroscopic patterns, or gasses on the surface or in the atmosphere that indicate signs of life. 
  2. Technosignatures, on the other hand, are identified from anomalies in astronomical data, such as radio or visual data. They are potentially detectable signatures that may indicate the presence of advanced civilizations. They might include things like infrared emissions that could be intentional signals from a distant planet, or the presence of certain pollutant chemicals in a planet’s atmosphere that might be an indication of an advanced civilization like ours.

Simulating Planetary Environments

It turns out that AI-driven data science is also extremely good at helping to build simulated atmospheric models of exoplanets. Astronomers can provide inputs to the computer models on observed gasses in an atmosphere based on temperature, boundary conditions, and other elements. These atmospheres can then be used to simulate telescopic observations of similar exoplanets in order to predict what types of atmospheres are the best to look for in potential exoplanets. 

Building a database of different simulated ‘worlds’ can help astronomers predict which specific properties to observe in a diverse set of exoplanets. Once they’ve identified the right properties in the simulated environments, they will have the data and technological requirements to build future telescopes that specialize in exoplanet detection, for example. 

NASA Takes on UFOs With Data

It might seem like a bite out of science fiction, but NASA is now taking a very scientific approach to examining the world of data on UFOs. A new nine-month study will not be focused on the potential presence of UFOs (now called Unidentified Aerial Phenomena or UAPs), but rather to understand what types of unclassified data may be available and what additional resources will be needed to learn exactly what the public is seeing (or thinks they are seeing) when they report UAP sightings. 

In its current form, the study of UAPs is “data poor,” and NASA hopes to transform it into a data-rich endeavor. NASA will use the same data science tools it uses for astrobiology research to extract the data needed to run more stringent analysis. The NASA program, which combines the efforts of the Science Mission Directorate and Aeronautics Research Mission Directorate, has identified the key data-related tasks for the mission. They include: 

  • Determining which types of scientific data currently collected and archived by NASA, non-profits, companies, and other agencies can be synthesized to analyze the nature of UAPs. 
  •  Understanding scientific analysis techniques being used today to assess the origins of UAPs, and which ones should be developed further.
  • What basic physical constraints can be placed on the nature of UAPs.
  • What reporting protocols that air traffic management data systems use today can be modified to improve analysis. 
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Conclusion: The Data is Out There, Up There!

Data scientists are becoming invaluable resources in the search for extraterrestrial life, and there are a number of fields of study that are empowering them to excel. As researchers continue to leverage data science in astrobiology, they will need to upskill on the latest tools and technologies. Check out Simplilearn’s Data Scientist Master’s program as an option to begin your journey.

Data Science & Business Analytics Courses Duration and Fees

Data Science & Business Analytics programs typically range from a few weeks to several months, with fees varying based on program and institution.

Program NameDurationFees
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Data Scientist11 Months$ 1,449
Data Analyst11 Months$ 1,449

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