The Blending ToolKit: A simulation framework for evaluation of galaxy detection and deblending

Autor: Mendoza, Ismael, Torchylo, Andrii, Sainrat, Thomas, Guinot, Axel, Boucaud, Alexandre, Paillassa, Maxime, Avestruz, Camille, Adari, Prakruth, Aubourg, Eric, Biswas, Biswajit, Buchanan, James, Burchat, Patricia, Doux, Cyrille, Joseph, Remy, Kamath, Sowmya, Malz, Alex I., Merz, Grant, Miyatake, Hironao, Roucelle, Cécile, Zhang, Tianqing, Collaboration, the LSST Dark Energy Science

We present an open source Python library for simulating overlapping (i.e., blended) images of galaxies and performing self-consistent comparisons of detection and deblending algorithms based on a suite of metrics. The package, named Blending Toolkit

Externí odkaz: http://arxiv.org/abs/2409.06986

MaxiMask： 教師あり機械学習に基づく 天文画像中の偽天体検出法. (Japanese)

Autor: Paillassa, Maxime, 訳西澤淳

Publikováno v: Astronomical Herald; Aug2022, Vol. 115 Issue 8, p499-506, 8p

MaxiMask: Identifying Contaminants in Astronomical Images using Convolutional Neural Networks

Autor: Paillassa, Maxime, Bertin, Emmanuel, Bouy, H.

Publikováno v: Astronomical Data Analysis Software and Systems XXVIII. ASP Conference Series, Vol. 521, proceedings of a conference held (11-15 October 2018) at The Hotel at the University of Maryland, College Park, Maryland, USA. Edited by Peter J. Teuben, Marc W. Pound, Brian A. Thomas, and Elizabeth M.Warner. San Francisco: Astronomical Society of the Pacific, 2019, p.99
Astronomical Data Analysis Software and Systems XXVIII. ASP Conference Series, Vol. 521, proceedings of a conference held (11-15 October 2018) at The Hotel at the University of Maryland, College Park, Maryland, USA. Edited by Peter J. Teuben, Marc W. Pound, Brian A. Thomas, and Elizabeth M.Warner. San Francisco: Astronomical Society of the Pacific, 2019, p.99, Oct 2018, Maryland, United States

International audience; We present MaxiMask, a contaminant detector for ground-based astronomical images based on convolutional neural networks (CNNs). Once trained, Maxi-Mask is able to detect cosmic rays, hot pixels, bad pixels, saturated pixels, d

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::678d744b589d8cfd613e6c562976ed9f
http://hdl.handle.net/20.500.12278/95320

Robust detection of astronomical sources using convolutional neural networks

Autor: Paillassa, Maxime

Publikováno v: Astrophysics [astro-ph]. Université de Bordeaux, 2020. English. ⟨NNT : 2020BORD0147⟩

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 op

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::518fb52b71f23dc188f74334b1ec3cc9
http://hdl.handle.net/20.500.12278/95057