Machine learning techniques to separate the cosmic from the telluric

Frederick Dauphin; Andreea Petric; Michelle Ntampaka; Swara Ravindranath; Jennifer Marshall; Étienne Artigau; Steven Businger; Laurie Rousseau-Nepton; Andrew W. Stephens; Takahiro Morishita

doi:10.1117/12.2642961

27 August 2022 Machine learning techniques to separate the cosmic from the telluric

Frederick Dauphin, Andreea Petric, Michelle Ntampaka, Swara Ravindranath, Jennifer Marshall, Étienne Artigau, Steven Businger, Laurie Rousseau-Nepton, Andrew W. Stephens, Takahiro Morishita

Author Affiliations +

Proceedings Volume 12180, Space Telescopes and Instrumentation 2022: Optical, Infrared, and Millimeter Wave; 121804Y (2022) https://doi.org/10.1117/12.2642961
Event: SPIE Astronomical Telescopes + Instrumentation, 2022, Montréal, Québec, Canada

Conference Poster

Abstract

In the Roman era, wide-field, deep, visible-to-near infrared images will revolutionize our understanding of galaxy evolution (e.g. environments, morphologies, masses, colors). The legacy value of Roman images and low-resolution spectra (with Roman’s prism and grism) will be greatly enhanced by massively multiplexed ground-based observations in the near – future and simultaneously allow us to leverage an impressive bounty of archived spectra from Maunakea facilities. We plan to enhance ground-based NIR spectra of astrophysically interesting objects with ground-sky spectra, atmospheric data, HST spectra and images, and machine learning techniques proven to predict galaxy spectra from images.

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Frederick Dauphin, Andreea Petric, Michelle Ntampaka, Swara Ravindranath, Jennifer Marshall, Étienne Artigau, Steven Businger, Laurie Rousseau-Nepton, Andrew W. Stephens, and Takahiro Morishita "Machine learning techniques to separate the cosmic from the telluric", Proc. SPIE 12180, Space Telescopes and Instrumentation 2022: Optical, Infrared, and Millimeter Wave, 121804Y (27 August 2022); https://doi.org/10.1117/12.2642961

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