Wednesday, January 20, 2021

Application of Data Mining and Associative Learning to Search For Potentially Habitable Exoplanets.

​

The question of the existence of extraterrestrial life has intrigued scientists around the world. With over 29000 recent observations of exoplanets, planets that orbit around a star similar to our solar system, the possibility of the existence of extraterrestrial life is becoming increasingly likely. However, not all of these exoplanets are habitable like earth, as the possibility of an exoplanet being habitable is based on many factors. These factors include the density, orbital eccentricity, spectral type of the star that the planet orbits etc. A planet also must fall under the circumstellar habitable zone (also known as the Goldilocks Zone) of a star, which is the area around a star in which liquid water can exist, as water is an essential requirement of life. Previously, our group only focused on one planet, K2-18 b, but our research now revolved around the Planetary Habitability Laboratory Habitable Exoplanets Catalog. We used a combination of data mining and associative learning to find planets with similar attributes to those in the Habitable Exoplanets Catalog, in hopes that these exoplanets could also potentially sustain life. Through these last 4 months, we were able to develop potential ways in which we could identify exoplanets with the possibility of supporting life. Although we have made serious progress, we have also encountered various obstacles, requiring further research to be done in order to find possible habitable exoplanets.  

​

​​​

​

Srideep D., Irvington High School

Downing Research Group

Machine Learning, Data Science