Machine learning for astrophysics proceedings of the ML4Astro International Conference 30 May-1 June 2022 /

This book reviews the state of the art in the exploitation of machine learning techniques for the astrophysics community and gives the reader a complete overview of the field. The contributed chapters allow the reader to easily digest the material through balanced theoretical and numerical methods a...

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Corporate Authors: International Conference on Machine Learning for Astrophysics Catania, Italy ; Online)
Other Authors: International Conference on Machine Learning for Astrophysics, Bufano, Filomena,, Riggi, Simone,, Sciacca, Eva,, Schilliro, Francesco,, SpringerLink (Online service)
Format: eBook
Language: English
Published: Cham : Springer, 2023.
Physical Description: 1 online resource (180 pages) : illustrations (black and white, and color).
Series: Astrophysics and space science proceedings ; v. 60.
Subjects:
Table of Contents:
  • Machine Learning for H? Emitters Classification
  • Stellar Dating Using Chemical Clocks and Bayesian Inference
  • Detection of Quasi-Periodic Oscillations in Time Series of a Cataclysmic Variable Using Support Vector Machine
  • Dust Extinction from Random Forest Regression of Interstellar Lines
  • QSOs Selection in Highly Unbalanced Photometric Datasets: The "Michelangelo" Reverse-Selection Method
  • Radio Galaxy Detection Prediction with Ensemble Machine Learning
  • A Machine Learning Suite to Halo-Galaxy Connection
  • New Applications of Graph Neural Networks in Cosmology
  • Detection of Point Sources in Maps of the Temperature Anisotropies of the Cosmic Microwave Background
  • Reconstruction and Particle Identification with CYGNO Experiment
  • Event Reconstruction for Neutrino Telescopes
  • Classification of Evolved Stars with (Unsupervised) Machine Learning Post Proceedings
  • Patterns in the Chaos: An Unsupervised View of Galactic Supernova Remnants
  • Clustering of Galaxy Spectra: An Unsupervised Approach with Fisher-EM
  • Unsupervised Classification Reveals New Evolutionary Pathways
  • In Search of the Peculiar: An Unsupervised Approach to Anomaly Detection in the Transient Universe
  • Classifying Gamma-Ray Burst X-Ray Afterglows with a Variational Autoencoder
  • Reconstructing Blended Galaxies with Machine Learning
  • Time Domain Astroinformatics
  • A Convolutional Neural Network to Characterise the Internal Structure of Stars
  • Finding Stellar Flares with Recurrent Deep Neural Networks
  • Planetary Markers in Stellar Spectra: Jupiter-Host Star Classification
  • Using Convolutional Neural Networks to Detect and Confirm Exoplanets
  • Machine Learning Applied to X-Ray Spectra: Separating Stars from Active Galactic Nuclei
  • Classification of System Variability Using A CNN
  • Deep Learning Processing and Analysis of Mock Astrophysical Observations
  • Deep Neural Networks for Source Detection in Radio Astronomical Maps
  • Radio Image Segmentation with Autoencoders
  • Citizen Science and Machine Learning: Towards a Robust Large-Scale Automatic Classification in Astronomy
  • Background Estimation in Fermi Gamma-Ray Burst Monitor Lightcurves Through a Neural Network
  • Machine Learning Investigations for LSST: Strong Lens Mass Modeling and Photometric Redshift Estimation
  • Multi-Band Photometry and Photometric Redshifts from Astronomical Images
  • Inference of Galaxy Clusters Mass Radial Profiles from Compton-? Maps with Deep Learning Technique
  • Deep Learning 21cm Lightcones in 3D
  • ConvNets for Enhanced Background Discrimination in the Diffuse Supernova Neutrino-Background (DSNB) Search
  • Deep Neural Networks for Single-Line Event Direction Reconstruction in ANTARES
  • Cats Vs Dogs, Photons Vs Hadrons
  • Events Classification in MAGIC Through Convolutional Neural Network Trained with Images of Observed Gamma-Ray Events
  • Federated Learning Meets HPC and Cloud
  • Integration and Deployment of Model Serving Framework at Production Scale
  • Predictive Maintenance for Array of Cherenkov Telescopes.