arXiv subjects

Andres D. Perez

Publications and source records attributed to Andres D. Perez.

At least 19 recordsLinked to original sources

arXiv2026

Two-Stage Machine Learning Strategy for Scalar and Vector Leptoquark Discrimination at the LHC

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 vector (VLQ) hypotheses. The method is based on a two-stage inference pipeline that combines a classifier trained to separate Standard Model backgrounds from a mixed LQ signal with a second classifier designed to distinguish between SLQ and VLQ scenarios, using the signal yield inferred from the first-stage classifier to guide the corresponding scalar and vector mass hypotheses. A test statistic is constructed from the classifier outputs and interpreted using reference probability density functions. The approach is applied to realistic LHC final states with hadronically decaying tau leptons, multiple jets, and missing transverse momentum, and its performance is assessed using simulated pseudo-experiments. We show that the proposed strategy provides a robust and statistically consistent procedure to discriminate between SLQ and VLQ signals across a wide range of masses and signal stregths. We find that the spin identification power closely follows the discovery potential, demonstrating that determining the spin nature of a newly discovered LQ does not require substantially larger datasets than those needed for discovery itself.

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arXiv2026

Shedding Light on Dark Matter at the LHC with Machine Learning

We investigate a WIMP dark matter (DM) candidate in the form of a singlino-dominated lightest supersymmetric particle (LSP) within the $Z_3$-symmetric Next-to-Minimal Supersymmetric Standard Model (NMSSM). This framework gives rise to regions of parameter space where DM is obtained via co-annihilation with nearby higgsino-like electroweakinos and DM direct detection~signals are suppressed, the so-called ``blind spots''. On the other hand, collider signatures remain promising due to enhanced radiative decay modes of higgsinos into the singlino-dominated LSP and photons, rather than into leptons or hadrons. Compared to MSSM scenarios with light bino- and wino-like electroweakinos, the NMSSM allows for final states with multiple photons arising from cascade radiative decays, providing a distinctive collider signature. This motivates searches for radiatively decaying neutralinos, however, these signals face substantial background challenges, as the decay products are typically soft due to the small mass-splits ($Δm$) between the LSP and the higgsino-like coannihilation partners. We apply a data-driven Machine Learning (ML) analysis that improves sensitivity to these subtle signals, offering a powerful complement to traditional search strategies to discover a new physics scenario. Using an LHC integrated luminosity of $100~\mathrm{fb}^{-1}$ at $14~\mathrm{TeV}$, the method achieves a $5σ$ discovery reach for higgsino masses up to $225~\mathrm{GeV}$ with $Δm\!\lesssim\!12~\mathrm{GeV}$, and a $2σ$ exclusion up to $285~\mathrm{GeV}$ with $Δm\!\lesssim\!20~\mathrm{GeV}$. These results highlight~the power of collider searches to probe DM candidates that remain hidden from current~direct detection experiments, and provide a motivation for a search by the LHC collaborations using ML methods.

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arXiv2026

Stochastic galactic supernova flux of semi-relativistic particles

New exotic particles with MeV masses, such as axion-like particles or light dark matter, can be emitted from core-collapse supernovae (SNe) with semi-relativistic velocities. Due to their speed dispersion, they would arrive at Earth as an extended packet with a time spread that can be as large as tens of millennia for typical detectors. It has been argued in the literature that the superposition of packets from all galactic SNe would give rise to a smooth and stationary diffuse flux that could be observable on terrestrial experiments. In this article, we critically examine this hypothesis by carrying out a numerical simulation of the galactic history of SN explosions. We show that, although the particle packets do overlap, due to the short observational time window, each of them only contributes with a very narrow range of energies and with an intensity that depends on the SN distance. As a consequence, the energy dependence of the resulting flux is extremely sensitive to the stochastic nature of the SN population and far from smooth. This has profound implications for the expected signature in terrestrial experiments, which displays a spectral shape that is not properly described by the smooth approximation. We develop a numerical tool to compute this stochastic galactic flux for generic semi-relativistic particles, which also allows us to explore sub-MeV particles, where the smooth diffuse flux approach does not hold. To test this framework, we revisit existing bounds on axion-like particles and fermionic dark matter, finding weaker constraints than previously reported.

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arXiv2025

Machine-Learning Analysis of Radiative Decays to Dark Matter at the LHC

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 (ML) techniques to explore WIMP radiative decays into a Dark Matter (DM) candidate in a supersymmetric framework. The minimal supersymmetric WIMP sector includes the lightest neutralino that can provide the observed DM relic density through its co-annihilation with the second lightest neutralino and lightest chargino. Moreover, the direct DM detection cross section rates fulfill current experimental bounds and provide discovery targets for the same region of model parameters in which the radiative decay of the second lightest neutralino into a photon and the lightest neutralino is enhanced. This strongly motivates the search for radiatively decaying neutralinos which, however, suffers from strong backgrounds. We investigate the LHC reach in the search for these radiatively decaying particles by means of cut-based and ML methods and estimate its discovery potential in this well-motivated, new physics scenario. We demonstrate that using ML techniques would enable access to most of the parameter space unexplored by other searches.

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arXiv2025

Probing a diffuse flux of axion-like particles from galactic supernovae with neutrino water Cherenkov detectors

In this article, we claim that axion-like particles (ALPs) with MeV masses can be produced with semi-relativistic velocities in core-collapse supernovae (SNe), generating a diffuse galactic flux. We show that these ALPs can be detected in neutrino water Cherenkov detectors via $a \, p \rightarrow p \, γ$ interactions. Using Super-Kamiokande data, we derive new constraints on the ALP parameter space, excluding a region spanning one order of magnitude in the ALP-proton coupling above cooling bounds for ALP masses in the range of $1-70$~MeV and ALP-proton couplings between $\sim 2\times10^{-5}-2\times10^{-4}$. We show that the future Hyper-Kamiokande will be able to probe couplings as small as $\sim 10^{-5}$, considerably constraining the allowed region above SN 1987A cooling bounds.

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arXiv2025

Bayesian technique to combine independently-trained Machine-Learning models applied to direct dark matter detection

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 of the likelihood, which instead is estimated from simulated data, unlike in traditional Markov Chain Monte Carlo (MCMC) algorithms. This considerably speeds up, by several orders of magnitude, the computation of the posterior distributions, which allows to perform the Bayesian analysis of an otherwise computationally prohibitive number of benchmark points. In this article we demonstrate that, in the TMNRE framework, it is possible to include, combine, and remove different datasets in a modular fashion, which is fast and simple as there is no need to re-train the machine learning algorithm or to define a combined likelihood. In order to assess the performance of this method, we consider the case of WIMP DM with spin-dependent and independent interactions with protons and neutrons in a xenon experiment. After validating our results with MCMC, we employ the TMNRE procedure to determine the regions where the DM parameters can be reconstructed. Finally, we present CADDENA, a Python package that implements the modular Bayesian analysis of direct detection experiments described in this work.

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arXiv2025

Insights into Dark Matter Direct Detection Experiments: Decision Trees versus Deep Learning

The detection of Dark Matter (DM) remains a significant challenge in particle physics. This study exploits advanced machine learning models to improve detection capabilities of liquid xenon time projection chamber experiments, utilizing state-of-the-art transformers alongside traditional methods like Multilayer Perceptrons and Convolutional Neural Networks. We evaluate various data representations and find that simplified feature representations, particularly corrected S1 and S2 signals as well as a few shape-related features including the time difference between signals, retain critical information for classification. Our results show that while transformers offer promising performance, simpler models like XGBoost can achieve comparable results with optimal data representations. We also derive exclusion limits in the cross-section versus DM mass parameter space, showing minimal differences between XGBoost and the best performing deep learning models. The comparative analysis of different machine learning approaches provides a valuable reference for future experiments by guiding the choice of models and data representations to maximize detection capabilities.

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arXiv2024

Disentangling axion-like particle couplings to nucleons via a delayed signal in Super-Kamiokande from a future supernova

In this work, we show that, if axion-like particles (ALPs) from core-collapse supernovae (SNe) couple to protons, they would produce very characteristic signatures in neutrino water Cherenkov detectors through their scattering off free protons via $a \, p \rightarrow p \, γ$ interactions. Specifically, sub-MeV ALPs would generate photons with energies $\sim 30$ MeV, which could be observed by Super-Kamiokande and Hyper-Kamiokande as a delayed signal after a future detection of SN neutrinos. We apply this to a hypothetical neighbouring SN (at a maximum distance of 100 kpc) and demonstrate that the region in the parameter space with ALP masses between $10^{-4}$ MeV and $1$ MeV and ALP-proton couplings in the range $3 \times 10^{-6}-4 \times 10^{-5}$ could be probed. We argue that this new signature, combined with the one expected at $\sim 7$ MeV from oxygen de-excitation, would allow us to disentangle ALP-neutron and ALP-proton couplings.

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arXiv2024

LHC Study of Third-Generation Scalar Leptoquarks with Machine-Learned Likelihoods

We study the impact of machine-learning algorithms on LHC searches for leptoquarks in final states with hadronically decaying tau leptons, multiple $b$-jets, and large missing transverse momentum. Pair production of scalar leptoquarks with decays only into third-generation leptons and quarks is assumed. Thanks to the use of supervised learning tools with unbinned methods to handle the high-dimensional final states, we consider simple selection cuts which would possibly translate into an improvement in the exclusion limits at the 95$\%$ confidence level for leptoquark masses with different values of their branching fraction into charged leptons. In particular, for intermediate branching fractions, we expect that the exclusion limits for leptoquark masses extend to $\sim$1.3 TeV. As a novelty in the implemented unbinned analysis, we include a simplified estimation of some systematic uncertainties with the aim of studying their possible impact on the stability of the results. Finally, we also present the projected sensitivity within this framework at 14 TeV for 300 and 3000 fb$^{-1}$ that extends the upper limits to $\sim$1.6 and $\sim$1.8 TeV, respectively.

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arXiv2023

Machine-Learned Exclusion Limits without Binning

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 the MLL method by including Kernel Density Estimators (KDE) to avoid binning the classifier output to extract the resulting one-dimensional signal and background probability density functions. We first test our method on toy models generated with multivariate Gaussian distributions, where the true probability distribution functions are known. Later, we apply the method to two cases of interest at the LHC: a search for exotic Higgs bosons, and a $Z'$ boson decaying into lepton pairs. In contrast to physical-based quantities, the typical fluctuations of the ML outputs give non-smooth probability distributions for pure-signal and pure-background samples. The non-smoothness is propagated into the density estimation due to the good performance and flexibility of the KDE method. We study its impact on the final significance computation, and we compare the results using the average of several independent ML output realizations, which allows us to obtain smoother distributions. We conclude that the significance estimation turns out to be not sensible to this issue.

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arXiv2023

Right-handed sneutrino and gravitino multicomponent dark matter in light of neutrino detectors

We investigate the possibility that right-handed (RH) sneutrinos and gravitinos can coexist and explain the dark matter (DM) problem. We compare extensions of the minimal supersymmetric standard model (MSSM) and the next-to-MSSM (NMSSM) adding RH neutrinos superfields, with special emphasis on the latter. If the gravitino is the lightest supersymmetric particle (LSP) and the RH sneutrino the next-to-LSP (NLSP), the heavier particle decays to the former plus left-handed (LH) neutrinos through the mixing between the scalar partners of the LH and RH neutrinos. However, the interaction is suppressed by the Planck mass, and if the LH-RH sneutrino mixing parameter is small, $\ll O(10^{-2})$, a long-lived RH sneutrino NLSP is possible even surpassing the age of the Universe. As a byproduct, the NLSP to LSP decay produces monochromatic neutrinos in the ballpark of current and planned neutrino telescopes like Super-Kamiokande, IceCube and Antares that we use to set constraints and show prospects of detection. In the NMSSM+RHN, assuming a gluino mass parameter $M_3 = 3$ TeV we found the following lower limits for the gravitino mass $m_{3/2} \gtrsim 1-600$ GeV and the reheating temperature $T_R \gtrsim 10^5 - 3 \times 10^7$ GeV, for $m_{\tildeν_R} \sim 10-800$ GeV. If we take $M_3=10$ TeV, then the limits on $T_R$ are relaxed by one order of magnitude.

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arXiv2022

A method for approximating optimal statistical significances with machine-learned likelihoods

Machine-learning techniques have become fundamental in high-energy physics and, for new physics searches, it is crucial to know their performance in terms of experimental sensitivity, understood as the statistical significance of the signal-plus-background hypothesis over the background-only one. We present here a simple method that combines the power of current machine-learning techniques to face high-dimensional data with the likelihood-based inference tests used in traditional analyses, which allows us to estimate the sensitivity for both discovery and exclusion limits through a single parameter of interest, the signal strength. Based on supervised learning techniques, it can perform well also with high-dimensional data, when traditional techniques cannot. We apply the method to a toy model first, so we can explore its potential, and then to a LHC study of new physics particles in dijet final states. Considering as the optimal statistical significance the one we would obtain if the true generative functions were known, we show that our method provides a better approximation than the usual naive counting experimental results.

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arXiv2021

The new $(g-2)_μ$ and Right-Handed Sneutrino Dark Matter

In this paper we investigate the $(g-2)_μ$ discrepancy in the context of the R-parity conserving next-to-minimal supersymmetric Standard Model plus right-handed neutrinos superfields. The model has the ability to reproduce neutrino physics data and includes the interesting possibility to have the right-handed sneutrino as the lightest supersymmetric particle and a viable dark matter candidate. Since right-handed sneutrinos are singlets, no new contributions for $δa_μ$ with respect to the MSSM and NMSSM are present. However, the possibility to have the right-handed sneutrino as the lightest supersymmetric particle opens new ways to escape Large Hadron Collider and direct detection constraints. In particular, we find that dark matter masses within $10 \lesssim m_{\tildeν_{R}} \lesssim 600$ GeV are fully compatible with current experimental constraints. Remarkably, not only spectra with light sleptons are needed, but we obtain solutions with $m_{\tildeμ} \gtrsim 600$ GeV in the entire dark matter mass range that could be probed by new $(g-2)_μ$ data in the near future. In addition, dark matter direct detection experiments will be able to explore a sizable portion of the allowed parameter space with $m_{\tildeν_{R}} \lesssim 300$ GeV, while indirect detection experiments will be able to probe a much smaller fraction within $200 \lesssim m_{\tildeν_{R}} \lesssim 350$ GeV.

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arXiv2021

Towards a method to anticipate dark matter signals with deep learning at the LHC

We study several simplified dark matter (DM) models and their signatures at the LHC using neural networks. We focus on the usual monojet plus missing transverse energy channel, but to train the algorithms we organize the data in 2D histograms instead of event-by-event arrays. This results in a large performance boost to distinguish between standard model (SM) only and SM plus new physics signals. We use the kinematic monojet features as input data which allow us to describe families of models with a single data sample. We found that the neural network performance does not depend on the simulated number of background events if they are presented as a function of $S/\sqrt{B}$, where $S$ and $B$ are the number of signal and background events per histogram, respectively. This provides flexibility to the method, since testing a particular model in that case only requires knowing the new physics monojet cross section. Furthermore, we also discuss the network performance under incorrect assumptions about the true DM nature. Finally, we propose multimodel classifiers to search and identify new signals in a more general way, for the next LHC run.

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arXiv2021

Dark matter candidates in the NMSSM with RH neutrino superfields

R-parity conserving supersymmetric models with right-handed (RH) neutrinos are very appealing since they could naturally explain neutrino physics and also provide a good dark matter (DM) candidate such as the lightest supersymmetric particle (LSP). In this work we consider the next-to-minimal supersymmetric standard model (NMSSM) plus RH neutrino superfields, with effective Majorana masses dynamically generated at the electroweak scale (EW). We perform a scan of the relevant parameter space and study both possible DM candidates: RH sneutrino and neutralino. Especially for the case of RH sneutrino DM we analyse the intimate relation between both candidates to obtain the correct amount of relic density. Besides the well-known resonances, annihilations through scalar quartic couplings and coannihilation mechanisms with all kind of neutralinos, are crucial. Finally, we present the impact of current and future direct and indirect detection experiments on both DM candidates.

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arXiv2020

MeV-GeV $γ$-ray telescopes probing gravitino LSP with coexisting axino NLSP as dark matter in the $μν$SSM

In $R$-parity violating supersymmetry, the gravitino as the lightest supersymmetric particle (LSP) is a good candidate for dark matter, with the interesting characteristic to be detectable through $γ$-ray telescopes. We extend this analysis considering an axino next-to-LSP (NLSP) as a coexisting dark matter particle contributing with a detectable signal in the $γ$-ray spectrum. The analysis is carried out in the framework of the $μν$SSM, which solves the $μ$ problem reproducing simultaneously neutrino data only with the addition of right-handed neutrinos. We find that important regions of the parameter space can be tested by future MeV-GeV $γ$-ray telescopes through the line signal coming from the decay of the axino NLSP into photon-neutrino. In a special region, a double-line signal from axino NLSP and gravitino LSP is possible with both contributions detectable.

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arXiv2020

Fenomenología de modelos supersimétricos: partículas y materia oscura

To elucidate the composition of dark matter (DM) is one of the most important open questions in particle and astroparticle phenomenology. Within the framework of minimal supersymmetric extensions of the standard model of fundamental particles, the `mu-from-nu supersymmetric standard model', $μν$SSM, solves the $μ$-problem and reproduces neutrino data, only adding couplings involving right-handed neutrinos. In this context, the supersymmetric partner of the graviton, known as gravitino, becomes a natural DM candidate. Working in the mentioned model, in this thesis we have analyzed the detection of gamma-ray signals arising from decaying gravitino DM considering experiments such as $\textit{Fermi}$-LAT. For this purpose all gravitino decays have been taken into account, complementing previous works. The possibility of an upcoming new generation of gamma-ray detectors has motivated the inclusion of axino, the supersymmetric partner of the axion, as a DM candidate. In this thesis, axino DM has been analyzed in the context of the $μν$SSM for the first time. Finally, scenarios with multicomponent DM have been studied, focusing on gravitinos and axinos coexisting as DM constituents. This novel scenario includes two very different cases with distinctive features: axino lighter than gravitino and vice versa. Considering the sensitivity of future MeV-GeV gamma-ray telescopes, axino and gravitino prospects of detection were analyzed in the mixed scenarios mentioned previously. In some parameter space regions, both candidates can produce a signal, therefore a double-line feature arises as a smoking gun that would uncover the DM composition.

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arXiv2020

MeV-GeV $γ$-ray telescopes probing axino LSP/gravitino NLSP as dark matter in the $μν$SSM

Axino and gravitino are promising candidates to solve the dark matter (DM) problem in the framework of supersymmetry. In this work, we assume that the axino is the lightest supersymmetric particle (LSP), and therefore contributes to DM. In the case of R-parity violating models, the axino can decay into a neutrino-photon pair with a lifetime much longer than the age of the Universe, yielding a potentially detectable signal. Interestingly, a gravitino next-to-LSP (NLSP) can live enough as to contribute to the relic density. We study both scenarios, only axino LSP as DM, and axino LSP with gravitino NLSP as DM. We carry out the analysis in the context of the $μν$SSM, which solves the $μ$ problem and reproduces neutrino data, only adding couplings involving right-handed neutrinos. In particular, we perform a complete analysis of the relevant parameter space of the model considering constraints from neutrino physics, cosmological observations, and $γ$-ray detection. We find that the axino or the gravitino can produce a signal detectable by future MeV-GeV $γ$-ray telescopes. In addition, in a parameter region where we get a well-tempered mixture of both particles, a double-line signal arises as a smoking gun.

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