Machine Learning for Astrophysics : Proceedings of the ML4Astro International Conference 30 May - 1 Jun 2022 / edited by Filomena Bufano, Simone Riggi, Eva Sciacca, Francesco Schilliro.

Format:

Book

Author/Creator:

Bufano, Filomena.

Contributor:

Riggi, Simone.

Sciacca, Eva.

Schilliro, Francesco.

Series:

Astrophysics and Space Science Proceedings, 1570-6605 ; 60

Language:

English

Subjects (All):

Astrophysics.

Machine learning.

Artificial intelligence.

Astronomy--Observations.

Astronomy.

Machine Learning.

Artificial Intelligence.

Astronomy, Observations and Techniques.

Local Subjects:

Astrophysics.

Machine Learning.

Artificial Intelligence.

Astronomy, Observations and Techniques.

Physical Description:

1 online resource (206 pages)

Edition:

1st ed. 2023.

Place of Publication:

Cham : Springer International Publishing : Imprint: Springer, 2023.

Summary:

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 and tools with applications in different fields of theoretical and observational astronomy. The book helps the reader to really understand and quantify both the opportunities and limitations of using machine learning in several fields of astrophysics.

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.

Notes:

Description based on publisher supplied metadata and other sources.

Other Format:

Print version: Bufano, Filomena Machine Learning for Astrophysics

ISBN:

9783031341670

3031341678

OCLC:

1405851028