69 citations · 538 across the 43 of their papers we have counts for
6 papers · 1 filter
Machine Learning Techniques for Astrophysics and Cosmology: Photometric Redshifts
Luca Tortorelli, Daniel Grün
The cosmological redshift of a galaxy's light is inferable from its observable properties in images. Because imaging is much easier to acquire than spectroscopic observations that…
Redshift Assessment Infrastructure Layers (RAIL): Rubin-era photometric redshift stress-testing and at-scale production
The RAIL Team, Jan Luca van den Busch, Eric Charles +31
Virtually all extragalactic use cases of the Vera C. Rubin Observatory's Legacy Survey of Space and Time (LSST) require the use of galaxy redshift information, yet the vast majorit…
UFig v1: The ultra-fast image generator
Silvan Fischbacher, Beatrice Moser, Tomasz Kacprzak +8
With the rise of simulation-based inference (SBI) methods, simulations need to be fast as well as realistic. is a public Python package that simulates astronomic…
The PAU Survey: Background light estimation with deep learning techniques
Laura Cabayol-Garcia, Martin B. Eriksen, Àlex Alarcón +17
In any imaging survey, measuring accurately the astronomical background light is crucial to obtain good photometry. This paper introduces BKGnet, a deep neural network to predict t…
The PAU Survey: Operation and orchestration of multi-band survey data
Nadia Tonello, Pau Tallada, Santiago Serrano +14
The Physics of the Accelerating Universe (PAU) Survey is an international project for the study of cosmological parameters associated with Dark Energy. PAU's 18-CCD camera (PAUCam)…
The PAU Survey: star-galaxy classification with multi narrow-band data
Laura Cabayol, Ignacio Sevilla-Noarbe, Enrique Fernández +18
Classification of stars and galaxies is a well-known astronomical problem that has been treated using different approaches, most of them relying on morphological information. In th…