147 citations · 1.8k across the 70 of their papers we have counts for
5 papers · 1 filter
Active Anomaly Detection for time-domain discoveries
Emille E. O. Ishida, Matwey V. Kornilov, Konstantin L. Malanchev +7
We present the first evidence that adaptive learning techniques can boost the discovery of unusual objects within astronomical light curve data sets. Our method follows an active l…
Machine Learning and the future of Supernova Cosmology
Emille E. O. Ishida
Machine Learning methods will play a fundamental role in our ability to optimize the science output from the next generation of large scale surveys. Given the peculiarities of astr…
Photometry of high-redshift blended galaxies using deep learning
Alexandre Boucaud, Marc Huertas-Company, Caroline Heneka +11
The new generation of deep photometric surveys requires unprecedentedly precise shape and photometry measurements of billions of galaxies to achieve their main science goals. At su…
Anomaly Detection in the Open Supernova Catalog
Maria V. Pruzhinskaya, Konstantin L. Malanchev, Matwey V. Kornilov +4
In the upcoming decade large astronomical surveys will discover millions of transients raising unprecedented data challenges in the process. Only the use of the machine learning al…
Models and Simulations for the Photometric LSST Astronomical Time Series Classification Challenge (PLAsTiCC)
R. Kessler, G. Narayan, A. Avelino +26
We describe the simulated data sample for the "Photometric LSST Astronomical Time Series Classification Challenge" (PLAsTiCC), a publicly available challenge to classify transient…