International Conference on Machine Learning for Astrophysics - ML4Astro

Session

Supervised Learning

30 May 2022, 10:40

Catania

Conveners

Supervised Learning: Session 2

• Eva Sciacca (Istituto Nazionale di Astrofisica (INAF))

4. Stellar dating using chemical clocks and Bayesian inference

Andres Moya (University of Valencia)

30/05/2022, 10:40

Oral Presentation

Dating stars is a major challenge with a deep impact on many astrophysical fields. One of the most promising techniques for this is using chemical abundances. Recent space- and ground-based facilities have improved the number of stars with accurate observations. This has opened the door for using Bayesian inference tools to maximise the information we can extract from them. In this work, we...

5. Dust extinction inference from Random Forest regression of insterstellar spectral line widths

Santiago Gonzalez-Gaitan (CENTRA, IST, University of Lisbon)

30/05/2022, 11:00

Oral Presentation

Dust extinction is ubiquitous in the Universe and a challenge in the correction of the brightness and color in astronomical observations. Spectral absorption lines from abundant gas atoms in the interstellar medium (ISM) like sodium, potassium and calcium, or molecules like diffuse interstellar bands, among others, serve as dust indicators and have been used to estimate dust extinction....

3. QSOs selection in highly unbalanced photometric datasets: The “Michelangelo” reverse-selection method.

Giorgio Calderone (Istituto Nazionale di Astrofisica (INAF))

30/05/2022, 11:20

Oral Presentation

I will present a novel selection method aimed at efficiently identifying high-redshift QSOs in highly unbalanced photometric datasets, characterized by a very low number of QSOs with respect to other sources. The method relies on a gradient boosting algorithm, although it may be be used with any other machine learning method providing classification probabilities. I applied the selection...

162. Modelling galaxy emission-line kinematics using self-supervised, physics-aware, Bayesian neural networks

James Dawson (Rhodes University)

30/05/2022, 11:40

Oral Presentation

In the upcoming decades large facilities, such as the SKA, will provide resolved observations of the kinematics of millions of galaxies. In order to assist in the timely exploitation of these vast datasets we have explored the use of self-supervised, physics aware neural networks capable of Bayesian kinematic modelling of galaxies. I will present the network's ability to model the kinematics...

150. Presentation 1.6