68 citations · 143 across the 6 of their papers we have counts for
8 papers
Astronomical image time series classification using CONVolutional attENTION (ConvEntion)
Anass Bairouk, Marc Chaumont, Dominique Fouchez +3
Aims. The treatment of astronomical image time series has won increasing attention in recent years. Indeed, numerous surveys following up on transient objects are in progress or un…
Photometric Redshift Estimation with Convolutional Neural Networks and Galaxy Images: A Case Study of Resolving Biases in Data-Driven Methods
Q. Lin, D. Fouchez, J. Pasquet +4
Deep Learning models have been increasingly exploited in astrophysical studies, yet such data-driven algorithms are prone to producing biased outputs detrimental for subsequent ana…
Galaxy Image Translation with Semi-supervised Noise-reconstructed Generative Adversarial Networks
Qiufan Lin, Dominique Fouchez, Jérôme Pasquet
Image-to-image translation with Deep Learning neural networks, particularly with Generative Adversarial Networks (GANs), is one of the most powerful methods for simulating astronom…
PhotoWeb redshift: boosting photometric redshift accuracy with large spectroscopic surveys
Marko Shuntov, J. Pasquet, S. Arnouts +10
Improving distance measurements in large imaging surveys is a major challenge to better reveal the distribution of galaxies on a large scale and to link galaxy properties with thei…
PELICAN: deeP architecturE for the LIght Curve ANalysis
Johanna Pasquet, Jérôme Pasquet, Marc Chaumont +1
We developed a deeP architecturE for the LIght Curve ANalysis (PELICAN) for the characterization and the classification of light curves. It takes light curves as input, without any…
A CNN adapted to time series for the classification of Supernovae
Anthony Brunel, Johanna Pasquet, Jérôme Pasquet +4
Cosmologists are facing the problem of the analysis of a huge quantity of data when observing the sky. The methods used in cosmology are, for the most of them, relying on astrophys…