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

Session

Time domain

9 Jul 2024, 14:30

Aula Magna (Catania)

Conveners

Time domain: I

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

191. [INVITED] The ALeRCE broker: a community broker for the Vera C. Rubin Observatory

Francisco Forster

09/07/2024, 14:30

A new time domain ecosystem is developing thanks to a new generation of large aperture and large field of view telescopes, notably the Vera C. Rubin Observatory. Among the tools required are fast machine learning aided discovery and classification algorithms, interoperable tools to allow for an effective communication with the community and follow-up telescopes, and new models and tools to...

211. Using bowties to catch radio emitting transients

Sergio Belmonte Díaz

09/07/2024, 15:10

Oral Presentation

Short timescale radio-transient events provide a signature of extreme astrophysical phenomena. They are often associated with radio emitting neutron stars and Fast Radio Bursts and have been theorised to be emitted by merging black holes and neutron stars. However, discovering these single pulses is computationally challenging and often does not use all available information when selecting...

212. Bridging the Gap between Spectra and Light-Curves: Exploring Multimodal Learning for the Identification of Broad Absorption Line Quasars

Nicolás Guerra-Varas

09/07/2024, 15:30

Oral Presentation

Broad-absorption line quasars (BAL QSOs) are those that present strong absorption troughs in their spectra. These are associated with powerful winds and outflows that can reach widths of ≥2000 km/s and velocities of ≥0.1c. These outflows can trigger significant active galactic nuclei (AGN) feedback processes, which play a crucial role in the evolution of their host galaxies. So far, the...

214. Unsupervised Machine Learning Techniques for Young Stellar Object Light Curve Characterization

Máté Madarász

09/07/2024, 15:50

Analysing light curve data presents unique challenges, notably the variability in data lengths, which complicates the use of traditional methods and many machine learning techniques designed for fixed-sized input vectors. In this study, we address these challenges by attempting to characterise Young Stellar Object (YSO) candidate light curves from the Gaia Photometric Science Alerts. The...