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
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