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

Session

Astroparticle Physics / Space Weather

12 Jul 2024, 11:10

Aula Magna (Catania)

Conveners

Astroparticle Physics / Space Weather

• Simone Riggi (Istituto Nazionale di Astrofisica (INAF))

200. CHEREN-ZOO: an educational project for automatic routines

Angelo Adamo (Istituto Nazionale di Astrofisica (INAF))

12/07/2024, 11:10

Oral Presentation

The main goal of this study is to implement a Citizen Science game designed specifically for children and based on the images that the Cherenkov Telescope Array (CTA) instrument will capture. The proposed objective was to start an active dissemination project ("learning by doing") both for humans - while children will simply play a game, humans interested in this project will take part in a...

201. Extending Cosmic Ray Background in the LiteBIRD Experiment using Generative Adversarial Networks

Giovanni Cavallotto (INFN Milano Bicocca)

12/07/2024, 11:30

Oral Presentation

Cosmic rays (CR) reaching telescope detectors in outer space are known to induce glitches and background noise, as the High-Frequency Telescope (HFT) of the LiteBIRD experiment, designed to measure the B modes polarization of the Cosmic Microwave Background (CMB). The presence of CR noise significantly influenced the Planck experiment, which shared similarities in detector design with...

202. The application of the Stack-CNN algorithm for the analysis of Mini-EUSO data

Antonio Giulio Coretti (INFN / University of Turin)

12/07/2024, 11:50

Oral Presentation

Machine learning algorithms are widely recognized as powerful and highly efficient methods in a broad range of classification tasks. In particular, Convolutional Neural Networks (CNN) are specifically suitable for the classification of images. In this work, a novel methodology that makes use of a CNN was tested on data from Mini-EUSO, a space telescope currently on board the International...

203. ML Methods For Space Debris Detection in Bistatic Radar Data

Miguel Andrea Zammit (University of Malta, Institute of Space Sciences & Astronomy)

12/07/2024, 12:10

Oral Presentation

Over the last half-century, the exponential increase of satellite launches has led to a significantly large population of debris objects in Earth's orbit, particularly in the Low Earth Orbit (LEO) and Geostationary Earth Orbit (GEO) regimes. Their subsequent detection and monitoring have thus become ever more pertinent, with facilities such as the BIstatic RAdar for LEo Survey (BIRALES) space...

204. Convolutional Neural Networks for Detecting Moving Objects in Wide-Field Surveys

Belén Yu Irureta-Goyena (École Polytechnique Fédérale de Lausanne)

12/07/2024, 12:30

Oral Presentation

When not tracked by the telescope, moving objects can leave trails of light in long-exposure astronomical images as they cross the sky. These objects can either be natural (asteroids) or artificial (satellites and space debris). We discuss their detection using machine-learning methods by analysing the data of two wide-field surveys: the Zwicky Transient Facility (ZTF) and the VLT Survey...