Titlebook: Machine Learning for Astrophysics; Proceedings of the M Filomena Bufano,Simone Riggi,Francesco Schilliro Conference proceedings 2023 The Ed

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Detection of Quasi-Periodic Oscillations in Time Series of a Cataclysmic Variable Using Support Vecance than what the reality is. In our observed object, MV Lyrae, we focus on such QPO‘s. We simulated the QPO according to Timmer and Koenig (Astron. Astrophys. 300:707–710, 1995) and estimated its confidence intervals. Some known (not obvious) QPO‘s fell under 1-. and therefore are not significant

Kaleidoscope

Dust Extinction from RF Regression of Interstellar Lines,orption 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. However, several caveats and limitations exist like line sat

Genteel

,QSOs Selection in Highly Unbalanced Photometric Datasets: The “Michelangelo” Reverse-Selection Methn a gradient boosting algorithm, although it may be be used with any other machine learning method providing classification probabilities. We applied the selection method on a photometric dataset and compared its performances to its direct-selection method counterpart, showing that the former privil

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New Applications of Graph Neural Networks in Cosmology,exploitation. Standard cosmological analyses based on abundances, two-point and higher-order statistics of cosmic tracers have been widely used to investigate the properties of the . and Large Scale Structure. However, these statistics can only exploit a subset of the entire information content avai

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Reconstruction and Particle Identification with CYGNO Experiment,rojection chamber. The difference in the topological features can be learned by the deep learning models by training over thousand of events. These networks can be further used for the discrimination of background and signal events. The networks trained in this study performs better than the convent

一窩小鳥

Event Reconstruction for Neutrino Telescopes,om particle interactions within creates signals of varying shapes and sparsity. For the reconstruction of such physics events, one aims to infer quantities like the interaction vertex, the deposited energy, the angles, and the topology of the interaction. This reconstruction step is of central relev

FEMUR

Classification of Evolved Stars with (Unsupervised) Machine Learning,wavelength photometric measurements. The foundation is a custom made reference dataset compiled from available stellar catalogues for target sources—AGB, Wolf Rayet, luminous blue variable and red supergiant stars. Our results indicate that applying HDBSCAN to UMAP’s feature representation seems to

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