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Ares, F., Esteve, J. G., Falceto, F., & Uson, A. (2020). Complex behavior of the density in composite quantum systems. Phys. Rev. B, 102(16), 165121–13pp.
Abstract: In this paper, we study how the probability of presence of a particle is distributed between the two parts of a composite fermionic system. We uncover that the difference of probability depends on the energy in a striking way and show the pattern of this distribution. We discuss the main features of the latter and explain analytically those that we understand. In particular, we prove that it is a nonperturbative property and we find out a large/small coupling constant duality. We also find and study features that may connect our problem with certain aspects of nonlinear classical dynamics, such as the existence of resonances and sensitive dependence on the state of the system. We show that the latter has, indeed, a similar origin than in classical mechanics: the appearance of small denominators in the perturbative series. Inspired by the proof of the Kolmogorov-Arnold-Moser theorem, we are able to deal with this problem by introducing a cutoff in energies that eliminates these small denominators. We also formulate some conjectures that we are not able to prove at present but can be supported by numerical experiments.
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Arganda, E., Marcano, X., Martin Lozano, V., Medina, A. D., Perez, A. D., Szewc, M., et al. (2022). A method for approximating optimal statistical significances with machine-learned likelihoods. Eur. Phys. J. C, 82(11), 993–14pp.
Abstract: 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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Arguelles, C. A. et al, & Barenboim, G. (2023). Snowmass white paper: beyond the standard model effects on neutrino flavor. Eur. Phys. J. C, 83(1), 15–57pp.
Abstract: Neutrinos are one of the most promising messengers for signals of new physics Beyond the Standard Model (BSM). On the theoretical side, their elusive nature, combined with their unknown mass mechanism, seems to indicate that the neutrino sector is indeed opening a window to new physics. On the experimental side, several long-standing anomalies have been reported in the past decades, providing a strong motivation to thoroughly test the standard three-neutrino oscillation paradigm. In this Snowmass21 white paper, we explore the potential of current and future neutrino experiments to explore BSM effects on neutrino flavor during the next decade.
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Arguelles, C. A., Coloma, P., Hernandez, P., & Muñoz, V. (2020). Searches for atmospheric long-lived particles. J. High Energy Phys., 02(2), 190–34pp.
Abstract: Long-lived particles are predicted in extensions of the Standard Model that involve relatively light but very weakly interacting sectors. In this paper we consider the possibility that some of these particles are produced in atmospheric cosmic ray showers, and their decay intercepted by neutrino detectors such as IceCube or Super-Kamiokande. We present the methodology and evaluate the sensitivity of these searches in various scenarios, including extensions with heavy neutral leptons in models of massive neutrinos, models with an extra U(1) gauge symmetry, and a combination of both in a U(1)(B-L) model. Our results are shown as a function of the production rate and the lifetime of the corresponding long-lived particles.
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Arguelles, C. A., Kelly, K. J., & Muñoz, V. M. (2021). Millicharged particles from the heavens: single- and multiple-scattering signatures. J. High Energy Phys., 11(11), 099–34pp.
Abstract: For nearly a century, studying cosmic-ray air showers has driven progress in our understanding of elementary particle physics. In this work, we revisit the production of millicharged particles in these atmospheric showers and provide new constraints for XENON1T and Super-Kamiokande and new sensitivity estimates of current and future detectors, such as JUNO. We discuss distinct search strategies, specifically studies of single-energy-deposition events, where one electron in the detector receives a relatively large energy transfer, as well as multiple-scattering events consisting of (at least) two relatively small energy depositions. We demonstrate that these atmospheric search strategies especially the multiple-scattering signature – provide significant room for improvement beyond existing searches, in a way that is complementary to anthropogenic, beam-based searches for MeV-GeV millicharged particles. Finally, we also discuss the implementation of a Monte Carlo simulation for millicharged particle detection in large-volume neutrino detectors, such as IceCube.
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