Characterizing the performance of the SPHERE exoplanet imager at the Very Large Telescope using deep learning

L. Bissot; J. Milli; E. Choquet; F. Cantalloube; P. Delorme; D. Mouillet; G. Louppe; O. Absil

doi:10.1117/12.3020236

27 August 2024 Characterizing the performance of the SPHERE exoplanet imager at the Very Large Telescope using deep learning

L. Bissot, J. Milli, E. Choquet, F. Cantalloube, P. Delorme, D. Mouillet, G. Louppe, O. Absil

Author Affiliations +

Proceedings Volume 13097, Adaptive Optics Systems IX; 130976I (2024) https://doi.org/10.1117/12.3020236
Event: SPIE Astronomical Telescopes + Instrumentation, 2024, Yokohama, Japan

Conference Poster

Abstract

The Spectro-Polarimetric High-contrast Exoplanet REsearch (SPHERE) instrument is a high-contrast imager designed for detecting exoplanets. It has been operational at the Very Large Telescope since 2014. To make the most of the extensive data generated by SPHERE, improve future observation planning, and advance instrument development, it is crucial to understand how its performance is affected by various environmental factors. The primary goal of this project is to use machine learning and deep learning techniques to predict detection limits, measured by the contrast between exoplanets and their host stars. Two types of models will be developed : random forest models and Multi-Layer Perceptron (MLP) models. The aim is to better understand the relationship between input parameters and detection limits, providing deeper insights into this field. Additionally, a neural network will be used to capture uncertainties in the input features, thus providing confidence intervals for its predictions.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

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L. Bissot, J. Milli, E. Choquet, F. Cantalloube, P. Delorme, D. Mouillet, G. Louppe, and O. Absil "Characterizing the performance of the SPHERE exoplanet imager at the Very Large Telescope using deep learning", Proc. SPIE 13097, Adaptive Optics Systems IX, 130976I (27 August 2024); https://doi.org/10.1117/12.3020236

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