16 Octobre – Thesis defense - Maxime Paillassa

11 h Room Univers - Laboratoire d'Astrophysique de Bordeaux (Pessac)

Robust detection of astronomical sources using convolutional neural networks.

Extracting reliable source catalogs from images is crucial for a broad range of astronomical research topics.
However, the efficiency of current source detection methods becomes severely limited in crowded fields, or when images are contaminated by optical, electronic and environmental defects.
Performance in terms of reliability and completeness is now often insufficient with regard to the scientific requirements of large imaging surveys.
In this thesis, we develop new methods to produce more robust and reliable source catalogs.
We leverage recent advances in deep supervised learning to design generic and reliable models based on convolutional neural networks (CNNs).
We present MaxiMask and MaxiTrack, two convolutional neural networks that we trained to automatically identify 13 different types of image defects in astronomical exposures.
We also introduce a prototype of a multi-scale CNN-based source detector robust to image defects, which we show to significantly outperform existing algorithms.
We discuss the current limitations and potential improvements of our approach in the scope of forthcoming large scale surveys such as Euclid.

Event localization