arXiv subjects
Publications and source records attributed to Humberto Reyes-González.
The LHC physics programme involves a vast amount of Monte Carlo event simulation. This paper reviews current efforts towards sharing the generated events as Open Data. Open Event Generation helps reduce duplication of effort and resource consumption, and benefits the whole High Energy Physics community. We give examples of use cases and user experiences, discuss financial and environmental savings, and suggest future directions.
Jet tagging, i.e. determining the origin of high-energy hadronic jets, is a key challenge in particle physics. Machine-learning-based taggers have achieved remarkable progress, raising the question of how close current methods are to the theoretical limit of performance. Previous work addressed this question for boosted top-quark jets using transformer-based generative models that provide realistic synthetic jet data with known probability density functions. This enables a direct comparison between modern taggers and the optimal likelihood-ratio classifier. In this note, we summarize the approach and extend the study to boosted W, Z, and H$\rightarrow gg$ jets. We find that the gap to the estimated optimal limit is strongly jet dependent and is substantially reduced for these seemingly more challenging tagging tasks. Ongoing work aimed at understanding the interpretation, robustness, and scaling of these limits is also briefly discussed.
The optimization of large experiments in fundamental science, such as detectors for subnuclear physics at particle colliders, shares with the optimization of complex systems for industrial or societal applications the common issue of addressing the inter-relation between parameters describing the hardware used in data production and parameters used to analyse those data. While in many cases this coupling can be ignored -- when the problem can be successfully factored into simpler sub-tasks and the latter addressed serially -- there are situations in which that approach fails to converge to the absolute maximum of expected performance, as it results in a mis-alignment of the optimized hardware and software solutions. In this work we consider a few use cases of interest in fundamental science collected primarily from particle physics and related areas, and a pot-pourri of industrial and societal applications where the matter is similarly of relevance. We discuss the emergence of strong hardware-software coupling in some of those systems, as well as co-design procedures that may be deployed to identify the global maximum of their relevant utility functions. We observe how numerous opportunities exist to advance methods and tools for hardware-software co-design optimization, bridging fundamental science and industry through application- and challenge-driven projects, and shaping the future of scientific experiments and industrial systems.
Generative models are increasingly central to scientific workflows, yet their systematic use and interpretation require a proper understanding of their limitations through rigorous validation. Classic approaches struggle with scalability, statistical power, or interpretability when applied to high-dimensional data, making it difficult to certify the reliability of these models in realistic, high-dimensional scientific settings. Here, we propose the use of the New Physics Learning Machine (NPLM), a learning-based approach to goodness-of-fit testing inspired by the Neyman--Pearson construction, to test generative networks trained on high-dimensional scientific data. We demonstrate the performance of NPLM for validation in two benchmark cases: generative models trained on mixtures of Gaussian models with increasing dimensionality, and a public end-to-end model, known as FlowSim, developed to generate high-energy physics collision events. We demonstrate that the NPLM can serve as a powerful validation method while also providing a means to diagnose sub-optimally modeled regions of the data.
Identifying the origin of high-energy hadronic jets ('jet tagging') has been a critical benchmark problem for machine learning in particle physics. Jets are ubiquitous at colliders and are complex objects that serve as prototypical examples of collections of particles to be categorized. Over the last decade, machine learning-based classifiers have replaced classical observables as the state of the art in jet tagging. Increasingly complex machine learning models are leading to increasingly more effective tagger performance. Our goal is to address the question of convergence -- are we getting close to the fundamental limit on jet tagging or is there still potential for computational, statistical, and physical insights for further improvements? We address this question using state-of-the-art generative models to create a realistic, synthetic dataset with a known jet tagging optimum. Various state-of-the-art taggers are deployed on this dataset, showing that there is a significant gap between their performance and the optimum. Our dataset and software are made public to provide a benchmark task for future developments in jet tagging and other areas of particle physics.
We present version 2 of SModelS, a program package for the fast reinterpretation of LHC searches for new physics on the basis of simplified model results. The major novelty of the SModelS v2 series is an extended topology description with a flexible number of particle attributes, such as spin, charge, decay width, etc. This enables, in particular, the treatment of a wide range of signatures with long-lived particles. Moreover, constraints from prompt and long-lived searches can be evaluated simultaneously in the same run. The current database includes results from searches for heavy stable charged particles, disappearing tracks, displaced jets and displaced leptons, in addition to a large number of prompt searches. The capabilities of the program are demonstrated by two physics applications: constraints on long-lived charged scalars in the scotogenic model, and constraints on the electroweak-ino sector in the Minimal Supersymmetric Standard Model.
Supersymmetric dark matter has been studied extensively in the context of the MSSM, where gauginos have Majorana masses. Introducing Dirac gaugino masses, we obtain an enriched phenomenology from which considerable differences in, e.g., LHC signatures can be expected. Concretely, in the Minimal Dirac Gaugino Model (MDGSSM) we have an electroweakino sector extended by two extra neutralinos and one extra chargino. The bino- and wino-like states bring about small mass splittings leading to the frequent presence of scenarios with Long Lived Particles (LLPs). In this contribution, we delineate the parameter space of the electroweakino sector of the MDGSSM, where the lightest neutralino is a viable dark matter candidate that escapes current dark matter direct detection. We then focus on the allowed regions that contain LLPs and confront them against the corresponding LHC searches. Finally, we discuss the predominant case of long-lived neutralinos, to which no search is currently sensitive.
SModelS is an automatized tool enabling the fast interpretation of simplified model results from the LHC within any model of new physics respecting a $\mathbb{Z}_2$ symmetry. In this contribution, we report on two important updates of SModelS during 2020: the extension of the SModelS' database with 13 ATLAS and 10 CMS analyses, including 5 ATLAS and 1 CMS analyses at full Run~2 luminosity, and the ability to use full likelihoods now provided by ATLAS in the form of pyhf JSON files. Moreover, we briefly explain how to use SModelS and give an overview of ongoing developments.
Supersymmetric models with Dirac instead of Majorana gaugino masses have distinct phenomenological consequences. In this paper, we investigate the electroweakino sector of the Minimal Dirac Gaugino Supersymmetric Standard Model (MDGSSM) with regards to dark matter (DM) and collider constraints. We delineate the parameter space where the lightest neutralino of the MDGSSM is a viable DM candidate, that makes for at least part of the observed relic abundance while evading constraints from DM direct detection, LEP and lowenergy data, and LHC Higgs measurements. The collider phenomenology of the thus emerging scenarios is characterised by the richer electroweakino spectrum as compared to the Minimal Supersymmetric Standard Model (MSSM) -- 6 neutralinos and 3 charginos instead of 4 and 2 in the MSSM, naturally small mass splittings, and the frequent presence of long-lived particles, both charginos and/or neutralinos. Reinterpreting ATLAS and CMS analyses with the help of SModelS and MadAnalysis 5, we discuss the sensitivity of existing LHC searches for new physics to these scenarios and show which cases can be constrained and which escape detection. Finally, we propose a set of benchmark points which can be useful for further studies, designing dedicated experimental analyses and/or investigating the potential of future experiments.
Most SUSY searches at the LHC are optimised for the MSSM, where gauginos are Majorana particles. By introducing Dirac gauginos, we obtain an enriched phenomenology, from which considerable differences in the LHC signatures and limits are expected as compared to the MSSM. Concretely, in the minimal Dirac gaugino model (MDGSSM) we have six neutralino and three chargino states. Moreover, production cross sections are enhanced for gluinos, while for squarks they are suppressed. In this contribution, we explore the consequences of the current LHC limits on gluinos and squarks in this model.
Dirac gauginos are a well-motivated extension of the MSSM, leading to interesting phenomenological consequences. At the LHC, gluino-pair production is enhanced while squark production is suppressed as compared to the MSSM, and the decay signatures are altered by a more complex chargino and neutralino spectrum. We investigate how this impacts current gluino and squark mass limits from Run~2 of the LHC. Concretely, we compare different assumptions about the electroweak-ino spectrum through four benchmark models paying particular attention to the effect of the trilinear $λ_S$ coupling, which induces a mass splitting between the mostly bino/U(1) adjoint states. Among other results, we show that for large $λ_S$ the additional $\tildeχ^0_2\to f\bar f \tildeχ^0_1$ decays somewhat weaken the limits on gluinos (squarks) in the case of heavy squarks (gluinos). Moreover, we compare the limits in the gluino vs. squark mass plane to those obtained in equivalent MSSM scenarios.
SModelS is an automatised tool enabling the fast interpretation of simplified model results from the LHC within any model of new physics respecting a $\mathbb{Z}_2$ symmetry. With the version 1.2 we announce several new features. First, previous versions were restricted to missing energy signatures and assumed prompt decays within each decay chain. SModelS v1.2 considers the lifetime of each $\mathbb{Z}_2$-odd particle and appropriately takes into account missing energy, heavy stable charge particle and R-hadron signatures. Second, the current version allows for a combination of signal regions in efficiency map results whenever a covariance matrix is available from the experiment. This is an important step towards fully exploiting the constraining power of efficiency map results. Several other improvements increase the user-friendliness, such as the use of wildcards in the selection of experimental results, and a faster database which can be given as a URL. Finally, smodelsTools provides an interactive plots maker to conveniently visualize the results of a model scan.