Fortela, D.L.; Travis, A.; Mikolajczyk, A.; Sharp, W. Exoplanet Atmosphere Characterization via Transit Spectra Classification. Preprints2023, 2023081385. https://doi.org/10.20944/preprints202308.1385.v1
APA Style
Fortela, D.L., Travis, A., Mikolajczyk, A., & Sharp, W. (2023). Exoplanet Atmosphere Characterization via Transit Spectra Classification. Preprints. https://doi.org/10.20944/preprints202308.1385.v1
Chicago/Turabian Style
Fortela, D.L., Ashley Mikolajczyk and Wayne Sharp. 2023 "Exoplanet Atmosphere Characterization via Transit Spectra Classification" Preprints. https://doi.org/10.20944/preprints202308.1385.v1
Abstract
This study focused on demonstrating the potential of classification algorithm in the chemical composition characterization of transiting exoplanets. The Python-based module PLATON 5.3 for forward modelling of transiting planet spectra was used to simulate a set of transmission spectra of an exoplanet with size Rp = 1.40*Rjupiter, and mass Mp = 0.73*Mjupiter orbiting around the host star of size Ms = 1.16*Msun and surface temperature of 1200 Kelvin. The gas composition of the exoplanet atmosphere was varied at low and high levels of 3-gas mix of CO2, O2, N2 and CH4 resulting to eight classes of spectra. The transit spectra were then used as input data to a forward neural network classifier with the eight gas composition classes as target outputs. The trained classifier achieved at most 97.9% overall accuracy.
Environmental and Earth Sciences, Atmospheric Science and Meteorology
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
