A5101483491- Astronomy and Astrophysical Research
- Galaxies: Formation, Evolution, Phenomena
- Dark Matter and Cosmic Phenomena
- Particle physics theoretical and experimental studies
- Particle Detector Development and Performance
- Advanced Semiconductor Detectors and Materials
- Semiconductor Quantum Structures and Devices
- Stellar, planetary, and galactic studies
- Computational Physics and Python Applications
- Neutrino Physics Research
- Radio Astronomy Observations and Technology
- Photonic and Optical Devices
- Astro and Planetary Science
- Planetary Science and Exploration
- Cosmology and Gravitation Theories
- Semiconductor Lasers and Optical Devices
- Reproductive Health and Contraception
- Gamma-ray bursts and supernovae
- Astronomical Observations and Instrumentation
- Medical Image Segmentation Techniques
- Fractal and DNA sequence analysis
- Maternal and fetal healthcare
- Terahertz technology and applications
- Adolescent and Pediatric Healthcare
- Thin-Film Transistor Technologies
Universidad Autónoma de Madrid
1993-2025
Consejo Superior de Investigaciones Científicas
2023-2025
Universidad Nacional de Córdoba
2018-2019
Instituto de Astronomía Teórica y Experimental
2016-2019
Consejo Nacional de Investigaciones Científicas y Técnicas
2018-2019
Argentine National Observatory
2018-2019
Centro Científico Tecnológico - Córdoba
2016
University of Quindío
2001-2008
Universidad Nacional Autónoma de México
1987
This article presents constraints on dark-matter-electron interactions obtained from the first underground data-taking campaign with multiple SuperCDMS HVeV detectors operated in same housing. An exposure of <a:math xmlns:a="http://www.w3.org/1998/Math/MathML" display="inline"><a:mrow><a:mn>7.63</a:mn><a:mtext> </a:mtext><a:mtext> </a:mtext><a:mi mathvariant="normal">g</a:mi><a:mtext>−</a:mtext><a:mi>days</a:mi></a:mrow></a:math> is used to set upper limits scattering cross section for dark...
Abstract 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) machine learning technique. TMNRE avoids an explicit calculation likelihood, which instead is estimated from simulated data, unlike in traditional Markov Chain Monte Carlo (MCMC) algorithms. This considerably speeds up, by several orders magnitude, computation posterior distributions, allows perform otherwise...
We present a new analysis of previously published SuperCDMS data using profile likelihood framework to search for sub-GeV dark matter (DM) particles through two inelastic scattering channels: bremsstrahlung radiation and the Migdal effect. By considering these possible channels, experimental sensitivity can be extended DM masses that are undetectable DM-nucleon elastic channel, given energy threshold current experiments. exclude down $220~\textrm{MeV}/c^2$ at $2.7 \times...
Studies of galaxy populations classified according to their kinematic behaviours and dynamical state using the Projected Phase Space Diagram (PPSD) are affected by misclassification contamination, leading systematic errors in determining characteristics different classes. We propose a method statistically correct determination properties' distributions accounting for contamination caused misclassified galaxies from other Using sample massive clusters surroundings taken MultiDark Planck 2...
Studies of galaxy populations classified according to their kinematic behaviours and dynamical state using the projected phase space diagram (PPSD) are affected by misclassification contamination, leading systematic errors in determining characteristics different classes. We propose a method for statistically correcting determination properties' distributions that accounts contamination caused misclassified galaxies from other Using sample massive clusters surroundings taken MultiDark Planck...
Clusters of galaxies have proven to be efficient systems in modifying various properties galaxies, such as star formation or morphology. However, projection effects impose serious challenges determining how, when, and what extent are affected by the cluster environment. Using innovative techniques classify based on their history within cluster, we aim determine how different classes We applied ROGER code select trajectories phase space for 35 galaxy clusters from OmegaWINGS survey. A new...
The evolution of galaxies is significantly influenced by the environments they inhabit. While high-density regions, such as clusters have been widely studied, dynamics and quenching processes in intermediate remain less explored. These systems provide a valuable context for understanding transition from active star formation to quiescence. This study aims characterise astrophysical properties intermediate-mass galaxy groups ($13.5 ≤ /M_ ⊙ ) 13.7$), with focus on their evolutionary pathways...
We measured the nuclear-recoil ionization yield in silicon with a cryogenic phonon-sensitive gram-scale detector. Neutrons from monoenergetic beam scatter off of nuclei at angles corresponding to energy depositions 4 keV down 100 eV, lowest probed so far. The results show no sign an production threshold above eV. These call for further investigation theory and comprehensive determination detector response function energies below scale.
Abstract Machine-learned likelihoods (MLL) combines machine-learning classification techniques with likelihood-based inference tests to estimate the experimental sensitivity of high-dimensional data sets. We extend MLL method by including kernel density estimators (KDE) avoid binning classifier output extract resulting one-dimensional signal and background probability functions. first test our on toy models generated multivariate Gaussian distributions, where true distribution functions are...
A bstract We explore the complementarity of direct detection (DD) and spallation source (SS) experiments for study sterile neutrino physics. focus on baryonic model: an extension Standard Model that introduces a massive with couplings to quark sector via new gauge boson. In this scenario, inelastic scattering active target material in both DD SS gives rise characteristic nuclear recoil energy spectrum can allow reconstruction mass event positive detection. first derive bounds model based...
We present the ROGER (Reconstructing Orbits of Galaxies in Extreme Regions) code, which uses three different machine learning techniques to classify galaxies in, and around, clusters, according their projected phase-space position. use a sample 34 massive, $M_{200}>10^{15} h^{-1} M_{\odot}$, galaxy clusters MultiDark Planck 2 (MDLP2) simulation at redshift zero. select all with stellar mass $M_{\star} \ge 10^{8.5} h^{-1}M_{\odot}$, as computed by semi-analytic model formation SAG, that are...
Merging galaxy systems provide observational evidence of the existence dark matter and constraints on its properties. Therefore, statistically uniform samples merging would be a powerful tool for several studies. In this paper, we present new methodology identification results application to redshift surveys. We use as starting point mock catalogue systems, identified using friends-of-friends algorithms, that have experienced major merger, indicated by merger tree. By applying machine...
Merging galaxy clusters allows to study the different mass components, dark and baryonic, separately. Also their occurrence enables test $\Lambda$CDM scenario they could put constrains in self interacting cross section of matter particle. It is necessary perform an homogeneous analysis these systems. Hence, based a recently presented sample candidates for clusters, we present two cataloged In this work, first serie devoted characterize merger process, weak lensing A1204 A2029/2033 derive...
We report the first confirmed detection of galaxy cluster VVV-J144321-611754 at very low latitudes (l = 315.836$^{\circ}$, b -1.650$^{\circ}$) located in tile d015 VISTA Variables V\'ia L\'actea (VVV) survey. defined region 30$\times$ 30 $arcmin^2$ centered brightest finding 25 galaxies. For these objects, extinction-corrected median colors (H - K$_{s}$) 0.34 $\pm$ 0.05 mag, (J H) 0.57 0.08 mag and 0.87 0.06 R$_{1/2}$ 1.59 0.16 $arcsec$; C 3.01 0.08; Sersic index, n 4.63 0.39 were estimated....
We present a new analysis of previously published SuperCDMS data using profile likelihood framework to search for sub-GeV dark matter (DM) particles through two inelastic scattering channels: bremsstrahlung radiation and the Migdal effect. By considering these possible channels, experimental sensitivity can be extended DM masses that are undetectable DM-nucleon elastic channel, given energy threshold current experiments. exclude down $220\text{ }\text{ }\mathrm{MeV}/{c}^{2}$ at...
We connect galaxy properties with their orbital classification by analysing a sample of galaxies stellar mass $M_{\star} \geq 10^{8.5}h^{-1}M_\odot$ residing in and around massive isolated clusters $M_{200} > 10^{15}h^{-1}M_\odot$ at redshift $z=0$. The population is generated applying the semi-analytic model formation SAG on cosmological simulation MultiDark Planck 2. classify considering real orbits (3D) projected phase-space position using ROGER code (2D). define five categories:...
Machine-Learned Likelihood (MLL) is a method that, by combining modern machine-learning techniques with likelihood-based inference tests, allows estimating the experimental sensitivity of high-dimensional data sets. Here we extend MLL including exclusion hypothesis tests and study it first on toy model multivariate Gaussian distributions, where true probability distribution functions are known. We then apply to case interest in search for new physics at LHC, which $Z^\prime$ boson decays...
ABSTRACT We study the population of backsplash galaxies at z = 0 in outskirts massive, isolated clusters taken from mdpl2-sag semi-analytical catalogue. consider four types according to whether they are forming stars or passive three stages their lifetimes: before entering cluster, during first incursion through and after exit cluster. analyse several geometric, dynamic, astrophysical aspects stages. Galaxies that form all account for majority (58 per cent) have stellar masses typically...
We study the magnetic fields in galaxy clusters through Faraday rotation measurements crossing systems different dynamical states. confirm that are present those and analyze difference between relaxed unrelaxed samples with respect to dispersion their inherent Rotation measurements. found an increase of this RM a higher overlapping frequency for clusters. This fact suggests large scale physical process is involved nature possible depolarization effects ones. show dynamically can enhance...
ABSTRACT 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 their positions in projected phase space diagram: cluster galaxies, backsplash recent infallers, infalling and interlopers. To understand effects environment evolution we compare across classes: stellar mass, specific star formation rate, size, morphology. Following guidelines Coenda al., separate analysis is carried out for red blue galaxies. For differ...
ABSTRACT We present a novel method of inferring the dark matter (DM) content and spatial distribution within galaxies, using convolutional neural networks (CNNs) trained state-of-the-art hydrodynamical simulations (Illustris–TNG100). Within controlled environment simulation, framework we have developed is capable DM mass galaxies ∼1011–$10^{13} \, M_\odot$ from gravitationally baryon-dominated internal regions to DM-rich, baryon-depleted outskirts with mean absolute error always below ≈0.25...
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) machine learning technique. TMNRE avoids an explicit calculation likelihood, which instead is estimated from simulated data, unlike in traditional Markov Chain Monte Carlo (MCMC) algorithms. This considerably speeds up, by several orders magnitude, computation posterior distributions, allows perform otherwise...