Sandner, S., Hernandez, P., Lopez-Pavon, J., & Rius, N. (2023). Predicting the baryon asymmetry with degenerate right-handed neutrinos. J. High Energy Phys., 11(11), 153–37pp.
Abstract: We consider the generation of a baryon asymmetry in an extension of the Standard Model with two singlet Majorana fermions that are degenerate above the electroweak phase transition. The model can explain neutrino masses as well as the observed matter-antimatter asymmetry, for masses of the heavy singlets below the electroweak scale. The only physical CP violating phases in the model are those in the PMNS mixing matrix, i.e. the Dirac phase and a Majorana phase that enter light neutrino observables. We present an accurate analytic approximation for the baryon asymmetry in terms of CP flavour invariants, and derive the correlations with neutrino observables. We demonstrate that the measurement of CP violation in neutrino oscillations as well as the mixings of the heavy neutral leptons with the electron, muon and tau flavours suffice to pin down the matter-antimatter asymmetry from laboratory measurements.
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CMS and CALICE Collaborations(Acar, B. et al), & Irles, A. (2023). Performance of the CMS High Granularity Calorimeter prototype to charged pion beams of 20-300 GeV/c. J. Instrum., 18(8), P08014–32pp.
Abstract: The upgrade of the CMS experiment for the high luminosity operation of the LHC comprises the replacement of the current endcap calorimeter by a high granularity sampling calorimeter (HGCAL). The electromagnetic section of the HGCAL is based on silicon sensors interspersed between lead and copper (or copper tungsten) absorbers. The hadronic section uses layers of stainless steel as an absorbing medium and silicon sensors as an active medium in the regions of high radiation exposure, and scintillator tiles directly read out by silicon photomultipliers in the remaining regions. As part of the development of the detector and its readout electronic components, a section of a silicon-based HGCAL prototype detector along with a section of the CALICE AHCAL prototype was exposed to muons, electrons and charged pions in beam test experiments at the H2 beamline at the CERN SPS in October 2018. The AHCAL uses the same technology as foreseen for the HGCAL but with much finer longitudinal segmentation. The performance of the calorimeters in terms of energy response and resolution, longitudinal and transverse shower profiles is studied using negatively charged pions, and is compared to GEANT4 predictions. This is the first report summarizing results of hadronic showers measured by the HGCAL prototype using beam test data.
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Cosme, C., Figueroa, D. G., & Loayza, N. (2023). Gravitational wave production from preheating with trilinear interactions. J. Cosmol. Astropart. Phys., 05(5), 023–30pp.
Abstract: We investigate the production of gravitational waves (GWs) during preheating with monomial/polynomial inflationary potentials, considering a trilinear coupling & phi;x2 between a singlet inflaton & phi; and a daughter scalar field x. For sufficiently large couplings, the trilinear interaction leads to an exponential production of x particles and, as a result, a large stochastic GW background (SGWB) is generated throughout the process. We study the linear and non-linear dynamics of preheating with lattice simulations, following the production of GWs through all relevant stages. We find that large couplings lead to SGWBs with amplitudes today that can reach up to h2 �(0) GW <^> 5 & BULL; 10-9. These backgrounds are however peaked at high frequencies fp > 5 & BULL; 106 Hz, which makes them undetectable by current/planned GW observatories. As the amount of GWs produced is in any case remarkable, we discuss the prospects for probing the SGWB indirectly by using constraints on the effective number of relativistic species in the universe Neff.
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KM3NeT Collaboration(Aiello, S. et al), Alves Garre, S., Calvo, D., Carretero, V., Garcia Soto, A., Gozzini, S. R., et al. (2023). KM3NeT broadcast optical data transport system. J. Instrum., 18(2), T02001–22pp.
Abstract: The optical data transport system of the KM3NeT neutrino telescope at the bottom of the Mediterranean Sea will provide more than 6000 optical modules in the detector arrays with a point-to-point optical connection to the control stations onshore. The ARCA and ORCA detectors of KM3NeT are being installed at a depth of about 3500 m and 2500 m, respectively and their distance to the control stations is about 100 kilometers and 40 kilometers. In particular, the two detectors are optimised for the detection of cosmic neutrinos with energies above about 1 TeV (ARCA) and for the detection of atmospheric neutrinos with energies in the range 1 GeV-1 TeV (ORCA). The expected maximum data rate is 200 Mbps per optical module. The implemented optical data transport system matches the layouts of the networks of electro-optical cables and junction boxes in the deep sea. For efficient use of the fibres in the system the technology of Dense Wavelength Division Multiplexing is applied. The performance of the optical system in terms of measured bit error rates, optical budget are presented. The next steps in the implementation of the system are also discussed.
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Gammaldi, V., Zaldivar, B., Sanchez-Conde, M. A., & Coronado-Blazquez, J. (2023). A search for dark matter among Fermi-LAT unidentified sources with systematic features in machine learning. Mon. Not. Roy. Astron. Soc., 520(1), 1348–1361.
Abstract: Around one-third of the point-like sources in the Fermi-LAT catalogues remain as unidentified sources (unIDs) today. Indeed, these unIDs lack a clear, univocal association with a known astrophysical source. If dark matter (DM) is composed of weakly interacting massive particles (WIMPs), there is the exciting possibility that some of these unIDs may actually be DM sources, emitting gamma-rays from WIMPs annihilation. We propose a new approach to solve the standard, machine learning (ML) binary classification problem of disentangling prospective DM sources (simulated data) from astrophysical sources (observed data) among the unIDs of the 4FGL Fermi-LAT catalogue. We artificially build two systematic features for the DM data which are originally inherent to observed data: the detection significance and the uncertainty on the spectral curvature. We do it by sampling from the observed population of unIDs, assuming that the DM distributions would, if any, follow the latter. We consider different ML models: Logistic Regression, Neural Network (NN), Naive Bayes, and Gaussian Process, out of which the best, in terms of classification accuracy, is the NN, achieving around 93 . 3 per cent +/- 0 . 7 per cent performance. Other ML evaluation parameters, such as the True Ne gativ e and True Positive rates, are discussed in our work. Applying the NN to the unIDs sample, we find that the de generac y between some astrophysical and DM sources can be partially solved within this methodology. None the less, we conclude that there are no DM source candidates among the pool of 4FGL Fermi-LAT unIDs.
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