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Terol-Calvo, J., Tortola, M., & Vicente, A. (2020). High-energy constraints from low-energy neutrino nonstandard interactions. Phys. Rev. D, 101(9), 095010–14pp.
Abstract: Many scenarios of new physics predict the existence of neutrino nonstandard interactions, new vector contact interactions between neutrinos, and first generation fermions beyond the Standard Model. We obtain model-independent constraints on the Standard Model effective field theory at high energies from bounds on neutrino nonstandard interactions derived at low energies. Our analysis explores a large set of new physics scenarios and includes full one-loop running effects below and above the electroweak scale. Our results show that neutrino nonstandard interactions already push the scale of new physics beyond the TeV. We also conclude that bounds derived by other experimental probes, in particular by low-energy precision measurements and by charged lepton flavor violation searches, are generally more stringent. Our study constitutes a first step toward the systematization of phenomenological analyses to evaluate the impact of neutrino nonstandard interactions for new physics scenarios at high energies.
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Ikeno, N., Toledo, G., & Oset, E. (2020). Molecular picture for the Omega(2012) revisited. Phys. Rev. D, 101(9), 094016–9pp.
Abstract: We conduct a study of the interaction of the (K) over bar Xi*, eta Omega(s-wave), and (K) over bar Xi(d-wave) channels within a coupled channel unitary approach where the transition potential between the (K) over bar Xi* and eta Omega channels is obtained from chiral Lagrangians. The transition potential between (K) over bar Xi*, eta Omega, and (K) over bar Xi is taken in terms of free parameters, which together with a cutoff to regularize the meson-baryon loops are fitted to the Omega(2012) data. We find that all data including the recent Belle experiment on Gamma(Omega*->pi(K) over bar Xi)/Gamma(Omega*->(K) over bar Xi), are compatible with the molecular picture stemming from meson baryon interaction of these channels.
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Segarra, A., & Bernabeu, J. (2020). Absolute neutrino mass and the Dirac/Majorana distinction from the weak interaction of aggregate matter. Phys. Rev. D, 101(9), 093004–6pp.
Abstract: The 2 nu-mediated force has a range of microns, well beyond the atomic scale. The effective potential is built from the t-channel absorptive part of the scattering amplitude and depends on neutrino properties on shell. We demonstrate that neutral aggregate matter has a weak charge and calculate the matrix of six coherent charges for its interaction with definite-mass neutrinos. Near the range of the potential the neutrino pair is nonrelativistic, leading to observable absolute mass and Dirac/Majorana distinction via different r-dependence and violation of the weak equivalence principle.
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Arbelaez, C., Cottin, G., Helo, J. C., & Hirsch, M. (2020). Long-lived charged particles and multilepton signatures from neutrino mass models. Phys. Rev. D, 101(9), 095033–13pp.
Abstract: Lepton number violation (LNV) is usually searched for by the LHC collaborations using the same-sign dilepton plus jet signature. In this paper, we discuss multilepton signals of LNV that can arise with experimentally interesting rates in certain loop models of neutrino mass generation. Interestingly, in such models, the observed smallness of the active neutrino masses, together with the high multiplicity of the final states, leads in large parts of the viable parameter space of such models to the prediction of long-lived charged particles, which leave highly ionizing tracks in the detectors. We focus on one particular one-loop neutrino mass model in this class and discuss its LHC phenomenology in some detail.
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Khosa, C. K., Mars, L., Richards, J., & Sanz, V. (2020). Convolutional neural networks for direct detection of dark matter. J. Phys. G, 47(9), 095201–20pp.
Abstract: The XENON1T experiment uses a time projection chamber (TPC) with liquid xenon to search for weakly interacting massive particles (WIMPs), a proposed dark matter particle, via direct detection. As this experiment relies on capturing rare events, the focus is on achieving a high recall of WIMP events. Hence the ability to distinguish between WIMP and the background is extremely important. To accomplish this, we suggest using convolutional neural networks (CNNs); a machine learning procedure mainly used in image recognition tasks. To explore this technique we use XENON collaboration open-source software to simulate the TPC graphical output of dark matter signals and main backgrounds. A CNN turns out to be a suitable tool for this purpose, as it can identify features in the images that differentiate the two types of events without the need to manipulate or remove data in order to focus on a particular region of the detector. We find that the CNN can distinguish between the dominant background events (ER) and 500 GeV WIMP events with a recall of 93.4%, precision of 81.2% and an accuracy of 87.2%.
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