International Conference on Machine Learning for Astrophysics 2nd Ed. - ML4ASTRO2

Session

Time domain

9 Jul 2024, 16:30

Aula Magna (Catania)

Conveners

Time domain: II

• Filomena Bufano (Istituto Nazionale di Astrofisica (INAF))

216. Improving ML training sets with Active Learning in Fink

Anais Möller

09/07/2024, 16:30

Oral Presentation

Current surveys are detecting thousands of supernovae (SNe) but only a small percentage is able to be classified using spectroscopy. To harness their full power, many surveys are now classifying SNe using ML algorithms. However, these algorithms rely on training sets which are usually built from spectroscopically classified SNe. In this talk, I will present an Active Learning recommendation...

218. A new Deep Learning Model for Gamma-Ray Bursts’ light curves simulation

Riccardo Falco

09/07/2024, 16:50

Oral Presentation

R. Falco, N. Parmiggiani, A. Bulgarelli, G. Panebianco, M. Lombardi, L. Castaldini, A. Di Piano, V. Fioretti, C. Pittori, M. Tavani

In Multi-Messanger astronomy the rapid and precise detection of transient events such as Gamma-Ray Bursts (GRBs) is fundamental to alert the scientific community of new transients and allow them to perform follow-up observations. For this reason, many space...

219. Signatures to help interpretability of anomalies in astronomical data

Emmanuel Gangler

09/07/2024, 17:10

Oral Presentation

Machine learning is often viewed as a black box when it comes to understanding its output, be it a decision or a score. Automatic anomaly detection is no exception to this rule, and quite often the astronomer is left to independently analyze the data in order to understand why a given event is tagged as an anomaly. Worst, the expert may end up scrutinizing over and over the same kind of rare...