Event Title

Estimating the Number of Earth-Size Habitable Planets in the Milky-Way Galaxy

Session Number

Project ID: PHYS 13

Advisor(s)

Dr. Eric Hawker; Illinois Mathematics and Science Academy

Discipline

Physical Science

Start Date

22-4-2020 9:45 AM

End Date

22-4-2020 10:00 AM

Abstract

The number of Earth-sized habitable planets in our galaxy have been determined from the number of Earth-size habitable planets discovered by the Kepler space telescope (KSP) and by understanding Kepler’s detection efficiency of these planets. Although the number of Earth-sized planets detected by Kepler is already known, the telescope’s instruments are not perfectly precise. Simulations of fake planet transits were created through the manipulation of the Kepler’s confirmed null stars and then were analyzed by a trained machine-learning algorithm to determine the detection efficiency of the telescope. The simulated transits were created using characteristics such as planetary and orbital radii within set boundaries, while the machine-learning algorithm was trained on known null and positive stars with exoplanets from the NASA Exoplanet Archive. A scaling factor was found through the combination of the determined accuracy found through the machine-learning algorithm and geometric factor. From this, an estimate of earth-sized planets including those not detected by Kepler in the Milky Way was derived.

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Apr 22nd, 9:45 AM Apr 22nd, 10:00 AM

Estimating the Number of Earth-Size Habitable Planets in the Milky-Way Galaxy

The number of Earth-sized habitable planets in our galaxy have been determined from the number of Earth-size habitable planets discovered by the Kepler space telescope (KSP) and by understanding Kepler’s detection efficiency of these planets. Although the number of Earth-sized planets detected by Kepler is already known, the telescope’s instruments are not perfectly precise. Simulations of fake planet transits were created through the manipulation of the Kepler’s confirmed null stars and then were analyzed by a trained machine-learning algorithm to determine the detection efficiency of the telescope. The simulated transits were created using characteristics such as planetary and orbital radii within set boundaries, while the machine-learning algorithm was trained on known null and positive stars with exoplanets from the NASA Exoplanet Archive. A scaling factor was found through the combination of the determined accuracy found through the machine-learning algorithm and geometric factor. From this, an estimate of earth-sized planets including those not detected by Kepler in the Milky Way was derived.