Search

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., Comment: 24 pages + references, 8 figures, 3 tables, 3 appendices. Version updated with published LZ results, appendix reserved for preliminary LZ results deleted

Quantum computing proposes a revolutionary paradigm that can radically transform numerous scientific and industrial application domains. To realize this promise, new capabilities need software solutions that are able to effectively harness its power. However, developers face significant challenges when developing quantum software due to the high computational demands of simulating quantum computers on classical systems. In this paper, we investigate the potential of using an accessible and cost-efficient manner remote computational capabilities to improve the experience of quantum software developers.

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., Comment: 25 pages, 7 figures, 2 tables

We report the first detection of the metal-bearing molecules sodium sulfide (NaS) and magnesium sulfide (MgS) and the tentative detection of calcium monoxide (CaO) in the interstellar medium (ISM) towards the Galactic Center molecular cloud G+0.693-0.027. The derived column densities are (5.0+-1.1) x 10$^{10}$ cm$^{-2}$, (6.0+-0.6) x $^{10}$ cm$^{-2}$, and (2.0+-0.5) x $^{10}$ cm$^{-2}$, respectively. This translates into fractional abundances with respect to H$_2$ of (3.7+-1.0) x $10^{-13}$, (4.4+-0.8) x $10^{-13}$, and (1.5+-0.4) x $10^{-13}$, respectively. We have also searched for other Na-, Mg- and Ca-bearing species towards this source but none of them have been detected and thus we provide upper limits for their abundances. We discuss the possible chemical routes involved in the formation of these molecules containing metals under interstellar conditions. Finally, we compare the ratio between sulfur-bearing and oxygen-bearing molecules with and without metals, finding that metal-bearing sulfur molecules are much more abundant than metal-bearing oxygen ones, in contrast with the general trend found in the ratios between other non metal- oxygen- and sulfur-bearing molecules. This further strengthen the idea that sulfur may be little depleted in G+0.693-0.027 as a result of the low velocity shocks present in this source sputtering large amounts of material from dust grains., Comment: 18 pages, 7 tables and 7 figures, final version accepted by ApJ

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, 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., Comment: 26 pages, 7 figures, 2 tables

The abundance of galaxy clusters with mass and redshift is a well-known cosmological probe. The cluster mass is a key parameter for studies that aim to constrain cosmological parameters using galaxy clusters, making it critical to understand and properly account for the errors in its estimates. Subsequently, it becomes important to correctly calibrate scaling relations between observables like the integrated Compton parameter and the mass of the cluster. The NIKA2 Sunyaev-Zeldovich Large program (LPSZ) enables one to map the intracluster medium profiles in the mm-wavelength band with great details (resolution of $11 \ \mathrm{\&}\ 17^{\prime \prime}$ at $1.2 \ \mathrm{\&}\ 2 $ mm, respectively) and hence, to estimate the cluster hydrostatic mass more precisely than previous SZ observations. However, there are certain systematic effects which can only be accounted for with the use of simulations. For this purpose, we employ THE THREE HUNDRED simulations which have been modelled with a range of physics modules to simulate galaxy clusters. The so-called twin samples are constructed by picking synthetic clusters of galaxies with properties close to the observational targets of the LPSZ. In particular, we use the Compton parameter maps and projected total mass maps of these twin samples along 29 different lines of sight. We investigate the scatter that projection induces on the total masses. Eventually, we consider the statistical values along different lines of sight to construct a kind of 3D scaling law between the integrated Compton parameter, total mass, and overdensity of the galaxy clusters to determine the overdensity that is least impacted by the projection effect., Comment: to appear in Proc. of the mm Universe 2023 conference, Grenoble (France), June 2023, published by F. Mayet et al. (Eds), EPJ Web of conferences, EDP Sciences

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., Comment: 25 pages, 7 figures, 2 tables (version published in PRD). Unbinned method code with approach to systematic uncertainty inclusion available from https://github.com/AndresDanielPerez/

Within the project Evolution of Chemistry in the envelope of HOt corinoS (ECHOS), we present a study of sulphur chemistry in the envelope of the Class 0 source B335 through observations in the spectral range 7, 3, and 2 mm. We have modelled observations assuming LTE and LVG approximation. We have also used the code Nautilus to study the time evolution of sulphur species. We have detected 20 sulphur species with a total gas-phase S abundance similar to that found in the envelopes of other Class 0 objects, but with significant differences in the abundances between sulphur carbon chains and sulphur molecules containing oxygen and nitrogen. Our results highlight the nature of B335 as a source especially rich in sulphur carbon chains unlike other Class 0 sources. The low presence or absence of some molecules, such as SO and SO+, suggests a chemistry not particularly influenced by shocks. We, however, detect a large presence of HCS+ that, together with the low rotational temperatures obtained for all the S species (<15 K), reveals the moderate or low density of the envelope of B335. We also find that observations are better reproduced by models with a sulphur depletion factor of 10 with respect to the sulphur cosmic elemental abundance. The comparison between our model and observational results for B335 reveals an age of 10$^4$$<$t$<$10$^5$ yr, which highlights the particularly early evolutionary stage of this source. B335 presents a different chemistry compared to other young protostars that have formed in dense molecular clouds, which could be the result of accretion of surrounding material from the diffuse cloud onto the protostellar envelope of B335. In addition, the analysis of the SO2/C2S, SO/CS, and HCS+/CS ratios within a sample of prestellar cores and Class 0 objects show that they could be used as good chemical evolutionary indicators of the prestellar to protostellar transition.

Catastrophic tornadoes cause severe damage and are a threat to human wellbeing, making it critical to determine mitigation strategies to reduce their impact. One such strategy, following recent research, is to retrofit existing structures. To this end, in this article we propose a model that considers a decision-maker (a government agency or a public-private consortium) who seeks to allocate resources to retrofit and recover wood-frame residential structures, to minimize the population dislocation due to an uncertain tornado. In the first stage the decision-maker selects the retrofitting strategies, and in the second stage the recovery decisions are made after observing the tornado. As tornado paths cannot be forecast reliably, we take a worst-case approach to uncertainty where paths are modeled as arbitrary line segments on the plane. Under the assumption that an area is affected if it is sufficiently close to the tornado path, the problem is framed as a two-stage robust optimization problem with a mixed-integer non-linear uncertainty set. We solve this problem by using a decomposition column-and-constraint generation algorithm that solves a two-level integer problem at each iteration. This problem, in turn, is solved by a decomposition branch-and-cut method that exploits the geometry of the uncertainty set. To illustrate the model's applicability, we present a case study based on Joplin, Missouri. Our results show that there can be up to 20 percent reductions in worst-case population dislocation by investing 15 million dollars in retrofitting and recovery; that our approach outperforms other retrofitting policies, and that the model is not over-conservative.

The determination of the mass of galaxy clusters from observations is subject to systematic uncertainties. Beyond the errors due to instrumental and observational systematic effects, in this work we investigate the bias introduced by modelling assumptions. In particular, we consider the reconstruction of the mass of galaxy clusters from convergence maps employing spherical mass density models. We made use of The Three Hundred simulations, selecting clusters in the same redshift and mass range as the NIKA2 Sunyaev-Zel'dovich Large Programme sample: $3 \leq M_{500}/ 10^{14} \mathrm{M}_{\odot} \leq 10$ and $0.5 \leq z \leq 0.9$. We studied different modelling and intrinsic uncertainties that should be accounted for when using the single cluster mass estimates for scaling relations. We confirm that the orientation of clusters and the radial ranges considered for the fit have an important impact on the mass bias. The effect of the projection adds uncertainties to the order of $10\%$ to $16\%$ to the mass estimates. We also find that the scatter from cluster to cluster in the mass bias when using spherical mass models is less than $9\%$ of the true mass of the clusters.

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., Comment: 24 pages, 8 figures, 3 tables, 1 appendix (version published in EPJC). MLL+KDE code available from https://github.com/AndresDanielPerez/2211.04806-ML-Likelihood-with-KDE

We develop a machine learning algorithm to infer the 3D cumulative radial profiles of total and gas mass in galaxy clusters from thermal Sunyaev-Zel'dovich effect maps. We generate around 73,000 mock images along various lines of sight using 2,522 simulated clusters from the \thethreehundred{} project at redshift $z< 0.12$ and train a model that combines an autoencoder and a random forest. Without making any prior assumptions about the hydrostatic equilibrium of the clusters, the model is capable of reconstructing the total mass profile as well as the gas mass profile, which is responsible for the SZ effect. We show that the recovered profiles are unbiased with a scatter of about $10\%$, slightly increasing towards the core and the outskirts of the cluster. We selected clusters in the mass range of $10^{13.5} \leq M_{200} /(\hMsun) \leq 10^{15.5}$, spanning different dynamical states, from relaxed to disturbed halos. We verify that both the accuracy and precision of this method show a slight dependence on the dynamical state, but not on the cluster mass. To further verify the consistency of our model, we fit the inferred total mass profiles with an NFW model and contrast the concentration values with those of the true profiles. We note that the inferred profiles are unbiased for higher concentration values, reproducing a trustworthy mass-concentration relation. The comparison with a widely used mass estimation technique, such as hydrostatic equilibrium, demonstrates that our method recovers the total mass that is not biased by non-thermal motions of the gas., Comment: Accepted for publication on Monthly Notices of the Royal Astronomical Society, 8 pages, 10 figures

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 $m_{\tilde{\nu}_R} \sim 10-800$ GeV. If we take $M_3=10$ TeV, then the limits on $T_R$ are relaxed by one order of magnitude., Comment: Discussions improved and expanded, version published in JCAP, 35 pages, 7 figures, 3 tables

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., Comment: 24 pages, 8 figures; matches version published in Eur. Phys. J. C

We show that any finite triangulation of the real projective plane or the dunce hat is partitionable. To prove this, we introduce simple yet useful gluing tools that allow us to reduce partitionability of a given complex to that of smaller constituent relative subcomplexes such as the disk or the open M\"obius strip. The gluing process generates partitioning schemes with a distinctive shelling/constructible flavor. We also give a tool to lift partitionability of relative simplicial complexes to that of the preimages of certain simplicial quotient maps., Comment: 15 pages, 6 figures

In this paper we investigate the $(g-2)_\mu$ 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 $\delta a_{\mu}$ 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 $10 \lesssim m_{\tilde{\nu}_{R}} \lesssim 600$ GeV are fully compatible with current experimental constraints. Remarkably, not only spectra with light sleptons are needed, but we obtain solutions with $m_{\tilde{\mu}} \gtrsim 600$ GeV in the entire dark matter mass range that could be probed by new $(g-2)_\mu$ 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 $m_{\tilde{\nu}_{R}} \lesssim 300$ GeV, while indirect detection experiments will be able to probe a much smaller fraction within $200 \lesssim m_{\tilde{\nu}_{R}} \lesssim 350$ GeV., Comment: Version published in Nucl. Phys. B. Discussions expanded, appendix B and references added. 29 pages, 9 figures, 2 tables. Analysis of the viability of RH sneutrino as dark matter carried out in arXiv:2102.08986

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., Comment: 52 pages, 29 figures, 3 tables. v2: Added a subsection to briefly discuss and compare event-by-event combination versus histogram or ensemble classifiers. v3: minor changes

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., Comment: Version published in JCAP, 40 pages, 8 figures, 6 tables

Can we infer all the failed components of an infrastructure network, given a sample of reachable nodes from supply nodes? One of the most critical post-disruption processes after a natural disaster is to quickly determine the damage or failure states of critical infrastructure components. However, this is non-trivial, considering that often only a fraction of components may be accessible or observable after a disruptive event. Past work has looked into inferring failed components given point probes, i.e. with a direct sample of failed components. In contrast, we study the harder problem of inferring failed components given partial information of some `serviceable' reachable nodes and a small sample of point probes, being the first often more practical to obtain. We formulate this novel problem using the Minimum Description Length (MDL) principle, and then present a greedy algorithm that minimizes MDL cost effectively. We evaluate our algorithm on domain-expert simulations of real networks in the aftermath of an earthquake. Our algorithm successfully identify failed components, especially the critical ones affecting the overall system performance., Comment: Appears in IEEE BigData 2020. Preliminary version in NeurIPS 2020 AI + HADR

We give a formulation of classical mechanics in the language of operators acting on a Hilbert space. The formulation given comes from a unitary irreducible representation of the Galilei group that is compatible with the basic postulates of classical mechanics, particularly the absence of an uncertainty principle between the position and momentum of a particle. It is shown that the theory exposed in the article contains the Koopman-von Neumann formalism of classical mechanics as a particular case.