61 citations · 108 across the 7 of their papers we have counts for
8 papers · 1 filter
Finding active galactic nuclei through Fink
Etienne Russeil, Emille E. O. Ishida, Roman Le Montagner +2
We present the Active Galactic Nuclei (AGN) classifier as currently implemented within the Fink broker. Features were built upon summary statistics of available photometric points,…
Debunking Generalization Error or: How I Learned to Stop Worrying and Love My Training Set
Viviana Acquaviva, Chistopher Lovell, Emille Ishida
We aim to determine some physical properties of distant galaxies (for example, stellar mass, star formation history, or chemical enrichment history) from their observed spectra, us…
Active learning with RESSPECT: Resource allocation for extragalactic astronomical transients
Noble Kennamer, Emille E. O. Ishida, Santiago Gonzalez-Gaitan +12
The recent increase in volume and complexity of available astronomical data has led to a wide use of supervised machine learning techniques. Active learning strategies have been pr…
Fink, a new generation of broker for the LSST community
Anais Möller, Julien Peloton, Emille E. O. Ishida +33
Fink is a broker designed to enable science with large time-domain alert streams such as the one from the upcoming Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST).…
Periodic Astrometric Signal Recovery through Convolutional Autoencoders
Michele Delli Veneri, Louis Desdoigts, Morgan A. Schmitz +8
Astrometric detection involves a precise measurement of stellar positions, and is widely regarded as the leading concept presently ready to find earth-mass planets in temperate orb…
21st Century Statistical and Computational Challenges in Astrophysics
Eric D. Feigelson, Rafael S. de Souza, Emille E. O. Ishida +1
Modern astronomy has been rapidly increasing our ability to see deeper into the universe, acquiring enormous samples of cosmic populations. Gaining astrophysical insights from thes…