12 citations · 58 across the 16 of their papers we have counts for
18 papers
Linking X-ray emission to galaxy populations in GAMA groups
Selene Levis, Hernán Muriel, Valeria Coenda +3
We study how the presence of a detectable hot intragroup medium affects galaxy colour and star-formation properties, and how these trends depend on dynamical stage within the group…
Two-Stage Machine Learning Strategy for Scalar and Vector Leptoquark Discrimination at the LHC
Ernesto Arganda, Martín de los Rios, Andres D. Perez +2
We present a machine learning framework for the characterization of leptoquark (LQ) signals at the Large Hadron Collider, focusing on the discrimination between scalar (SLQ) and ve…
Reconstructing orbits of galaxies in extreme regions (roger v2.0): an extension to intermediate mass systems
Martín de los Rios, Héctor J. Martínez, Andrés N. Ruiz +3
In this paper, we present an updated version of the roger code, called roger v2.0, developed to perform the orbital classification of galaxies residing in and around galaxy groups…
Dark Matter profiles of "in silico" galaxies: deep learning inference
Martín de los Rios, Serafina Di Gioia, Fabio Iocco +1
Machine learning has the potential to improve the reconstruction of the dark matter profile of galaxies with respect to traditional methods, like rotation curves. We demonstrate on…
Shedding Light on Dark Matter at the LHC with Machine Learning
Ernesto Arganda, Martín de los Rios, Andres D. Perez +3
We investigate a WIMP dark matter (DM) candidate in the form of a singlino-dominated lightest supersymmetric particle (LSP) within the -symmetric Next-to-Minimal Supersymmetri…
Rotary Masked Autoencoders are Versatile Learners
Uros Zivanovic, Serafina Di Gioia, Andre Scaffidi +3
Applying Transformers to irregular time-series typically requires specializations to their baseline architecture, which can result in additional computational overhead and increase…
Galaxy evolution in groups: Transition galaxies in the IllustrisTNG simulations
Selene Levis, Valeria Coenda, Hernán Muriel +4
The evolution of galaxies is significantly influenced by the environments they inhabit. While high-density regions, such as clusters of galaxies have been widely studied, the dynam…
Reconstructing orbits of galaxies in extreme regions (ROGER). IV. Unveiling galaxy evolution patterns in OmegaWINGS clusters
Hernán Muriel, David Pérez-Millán, Martín de los Rios +6
Clusters of galaxies have proven to be efficient systems in modifying various properties of galaxies, such as star formation or morphology. However, projection effects impose serio…
Machine-Learning Analysis of Radiative Decays to Dark Matter at the LHC
Ernesto Arganda, Marcela Carena, Martín de los Rios +4
The search for weakly interacting matter particles (WIMPs) is one of the main objectives of the High Luminosity Large Hadron Collider (HL-LHC). In this work we use Machine-Learning…
Bayesian technique to combine independently-trained Machine-Learning models applied to direct dark matter detection
David Cerdeno, Martin de los Rios, Andres D. Perez
We carry out a Bayesian analysis of dark matter (DM) direct detection data to determine particle model parameters using the Truncated Marginal Neural Ratio Estimation (TMNRE) machi…
Measuring the Sterile Neutrino Mass in Spallation Source and Direct Detection Experiments
David Alonso-González, Dorian W. P. Amaral, Adriana Bariego-Quintana +2
We explore the complementarity of direct detection (DD) and spallation source (SS) experiments for the study of sterile neutrino physics. We focus on the sterile baryonic neutrino…
Backsplash galaxies and their impact on galaxy evolution: a three-stage, four-type perspective
Andrés N. Ruiz, Héctor J. Martínez, Valeria Coenda +4
We study the population of backsplash galaxies at in the outskirts of massive, isolated clusters of galaxies taken from the MDPL2-SAG semi-analytic catalogue. We consider fou…
Reconstructing Orbits of Galaxies in Extreme Regions (ROGER) III: galaxy evolution patterns in projected phase space around massive X-ray clusters
Hector J. Martinez, Valeria Coenda, Hernan Muriel +2
We use the ROGER code by de los Rios et al. to classify galaxies around a sample of X-ray clusters into five classes according to their positions in the projected phase space diagr…
Machine-Learned Exclusion Limits without Binning
Ernesto Arganda, Andres D. Perez, Martin de los Rios +1
Machine-Learned Likelihoods (MLL) combines machine-learning classification techniques with likelihood-based inference tests to estimate the experimental sensitivity of high-dimensi…
Reconstructing Orbits of Galaxies in Extreme Regions (ROGER) II: reliability of projected phase-space in our understanding of galaxy populations
Valeria Coenda, Martín de los Rios, Hernán Muriel +4
We connect galaxy properties with their orbital classification by analysing a sample of galaxies with stellar mass residing in and around mas…
Determining the Dark Matter distribution in galaxies with Deep Learning
Martín Emilio de los Rios, Mihael Petač, Bryan Zaldivar +3
We present a novel method to infer the Dark Matter (DM) content and spatial distribution within galaxies, based on convolutional neural networks trained within state-of-the-art hyd…
I. Analysis of candidates for interacting galaxy clusters
Elizabeth J. Gonzalez, Martín de los Rios, Gabriel A. Oio +6
Merging galaxy clusters allows to study the different mass components, dark and baryonic, separately. Also their occurrence enables to test the CDM scenario and they could put c…
The MeSsI (Merging Systems Identification) Algorithm & Catalogue
Martín de los Rios, Mariano J. Domínguez R., Dante Paz +1
Merging galaxy systems provides observational evidence of the existence of dark matter and constraints on its properties. Therefore, statistical uniform samples of merging systems…