Searh results

Copertina volume box-s-2

From Dark Matter to Machine Learning: A collection of EAS 2022 proceedings from the S3 and S11 symposia

Volume 94, n. 3, 2023

Index
Foreword

S3: The dark matter multi-messenger challenge

S. CebriánDirect Detection of Dark Matter

DOI: https://doi.org/10.36116/MEMSAIT_94N3.2023.11
Raghuveer GaraniStellar probes of dark matter

DOI: https://doi.org/10.36116/MEMSAIT_94N3.2023.17
Sisk-Reynes et al.Current and Future constraints on Very-Light Axion-Like Particles from X-ray observations of cluster-hosted Active Galaxies

DOI: https://doi.org/10.36116/MEMSAIT_94N3.2023.26
Gauri Sharma and Jonathan FreundlichDark Matter Halos of Disk-like Galaxies at z ∼ 1

DOI: https://doi.org/10.36116/MEMSAIT_94N3.2023.33
L. CiottiRotation curves of galaxies in General Relativity

DOI: https://doi.org/10.36116/MEMSAIT_94N3.2023.40
Bílek et al.Imprint of the galactic acceleration scale on globular cluster systems: Galaxies in the Fornax Cluster

DOI: https://doi.org/10.36116/MEMSAIT_94N3.2023.44
Lucas M. ValenzuelaLights in the Dark: Globular clusters as dark matter tracers

DOI: https://doi.org/10.36116/MEMSAIT_94N3.2023.47
Oliver Manzanilla Carretero, Adriana Bariego-Quintana, and Felipe J. Llanes-EstradaDark Matter Cigars

DOI: https://doi.org/10.36116/MEMSAIT_94N3.2023.52
Marcel S. PawlowskiWhat new observations tell us about Planes of Satellite Galaxies

DOI: https://doi.org/10.36116/MEMSAIT_94N3.2023.55
Lucas C. KimmigAnd Yet There is Mass: How Projection Effects Can Solve the Apparent Lack of Mass in Substructures of Simulated Galaxy Clusters

DOI: https://doi.org/10.36116/MEMSAIT_94N3.2023.60
G. Granata et al.Investigating the discrepancy in sub-halo compactness between observed and simulated galaxy clusters with improved strong lensing modelling

DOI: https://doi.org/10.36116/MEMSAIT_94N3.2023.63
M. Meneghetti et al.Too many galaxy-galaxy strong lenses observed in galaxy clusters

DOI: https://doi.org/10.36116/MEMSAIT_94N3.2023.67
Nemani et al.Reconstructing blended galaxies with Machine Learning

DOI: https://doi.org/10.36116/MEMSAIT_94N3.2023.111

S11: Machine Learning, a giant leap towards space discovery in the era of peta and exabyte scale surveys

Yun Cheng et al.CNN Lesson Learned from Two Largest Galaxy Morphological Classification Catalogues

DOI: https://doi.org/10.36116/MEMSAIT_94N3.2023.77
Domínguez Sánchez et al.Revisiting the SFR-Mass relation at z = 0 with detailed deep learning based morphologies

DOI: https://doi.org/10.36116/MEMSAIT_94N3.2023.82
Tohill et al.Exploring the Morphologies of High Redshift Galaxies with Machine Learning

DOI: https://doi.org/10.36116/MEMSAIT_94N3.2023.89
Buitrago et al.Machine Learning disclosing the edges of galaxies

DOI: https://doi.org/10.36116/MEMSAIT_94N3.2023.94
Pearson et al.Pitfalls of AI classification of rare objects Galaxy Mergers

DOI: https://doi.org/10.36116/MEMSAIT_94N3.2023.98
A. Lavrukhina and K. MalanchevPerformant feature extraction for photometric time series

DOI: https://doi.org/10.36116/MEMSAIT_94N3.2023.102
Sheng et al.Stochastic Recurrent Neural Networks for Modelling Astronomical Time Series: Advantages and Limitations

DOI: https://doi.org/10.36116/MEMSAIT_94N3.2023.106
Diego-Palazuelos et al.Machine learning approach to the detection of point sources in maps of the CMB temperature anisotropies

DOI: https://doi.org/10.36116/MEMSAIT_94N3.2023.116
C. HenekaLearning the Radio 21cm Signal – From Dawn till Dusk, from Tomography to Sources

DOI: https://doi.org/10.36116/MEMSAIT_94N3.2023.119
Yun Cheng et al.Harvesting the Lyman alpha forest with convolutional neural networks

DOI: https://doi.org/10.36116/MEMSAIT_94N3.2023.121
Dubois et al.Clustering of galaxy spectra: an unsupervised approach with Fisher-EM

DOI: https://doi.org/10.36116/MEMSAIT_94N3.2023.124
Kyritsis et al.A versatile classification tool for galactic activity using optical and infrared colors

DOI: https://doi.org/10.36116/MEMSAIT_94N3.2023.128
Awad et al.Swarm Intelligence-based Extraction and Manifold Crawling along the Large Scale Structure

DOI: https://doi.org/10.36116/MEMSAIT_94N3.2023.131

MemSAIt Editors

Memorie della SAIT - Osservatorio Astronomico di Roma, via Frascati 33, 00078, Monte Porzio Catone, Roma