Merle, A., Platscher, M., Rojas, N., Valle, J. W. F., & Vicente, A. (2016). Consistency of WIMP Dark Matter as radiative neutrino mass messenger. J. High Energy Phys., 07(7), 013–17pp.
Abstract: The scotogenic scenario provides an attractive approach to both Dark Matter and neutrino mass generation, in which the same symmetry that stabilises Dark Matter also ensures the radiative seesaw origin of neutrino mass. However the simplest scenario may suffer from inconsistencies arising from the spontaneous breaking of the underlying Z(2) symmetry. Here we show that the singlet-triplet extension of the simplest model naturally avoids this problem due to the presence of scalar triplets neutral under the Z(2) which affect the evolution of the couplings in the scalar sector. The scenario offers good prospects for direct WIMP Dark Matter detection through the nuclear recoil method.
|
Gariazzo, S., Giunti, C., Laveder, M., & Li, Y. F. (2018). Model-independent (nu)over-bar(e) short-baseline oscillations from reactor spectral ratios. Phys. Lett. B, 782, 13–21.
Abstract: We consider the ratio of the spectra measured in the DANSS neutrino experiment at 12.7 and 10.7 m from a nuclear reactor. These data give a new model-independent indication in favor of short-baseline (nu) over bar (e) oscillations which reinforce the model-independent indication found in the late 2016 in the NEOS experiment. The combined analysis of the NEOS and DANSS spectral ratios in the framework of 3+1 active-sterile neutrino mixing favor short-baseline (nu) over bar (e) oscillations with a statistical significance of 3.7 sigma. The two mixing parameters sin(2)2 nu ee and Delta m(41)(2) are constrained at 2 sigma a narrow-Delta m(41)(2) island at Delta m(41)(2) similar or equal to 1.3 eV(2), with sin(2)2 nu(ee)= 0.049 +/- 0.023(2 sigma). We discuss the implications of the model-independent NEOS+DANSS analysis for the reactor and Gallium anomalies. The NEOS+DANSS model-independent determination of short-baseline (nu) over bar (e) oscillations allows us to analyze the reactor rates without assumptions on the values of the main reactor antineutrino fluxes and the data of the Gallium source experiments with free detector efficiencies. The corrections to the reactor neutrino fluxes and the Gallium detector efficiencies are obtained from the fit of the data. In particular, we confirm the indication in favor of the need for a recalculation of the (235)Ureactor antineutrino flux found in previous studies assuming the absence of neutrino oscillations.
|
ATLAS Collaboration(Aad, G. et al), Aparisi Pozo, J. A., Bailey, A. J., Cabrera Urban, S., Cardillo, F., Castillo, F. L., et al. (2021). Search for dark matter in events with missing transverse momentum and a Higgs boson decaying into two photons in pp collisions at root s=13 TeV with the ATLAS detector. J. High Energy Phys., 10(10), 013–50pp.
Abstract: A search for dark-matter particles in events with large missing transverse momentum and a Higgs boson candidate decaying into two photons is reported. The search uses 139 fb(-1) of proton-proton collision data collected at root s = 13 TeV with the ATLAS detector at the CERN LHC between 2015 and 2018. No significant excess of events over the Standard Model predictions is observed. The results are interpreted by extracting limits on three simplified models that include either vector or pseudoscalar mediators and predict a final state with a pair of dark-matter candidates and a Higgs boson decaying into two photons.
|
Blanes-Selva, V., Ruiz-Garcia, V., Tortajada, S., Benedi, J. M., Valdivieso, B., & Garcia-Gomez, J. M. (2021). Design of 1-year mortality forecast at hospital admission: A machine learning approach. Health Inform. J., 27(1), 13pp.
Abstract: Palliative care is referred to a set of programs for patients that suffer life-limiting illnesses. These programs aim to maximize the quality of life (QoL) for the last stage of life. They are currently based on clinical evaluation of the risk of 1-year mortality. The main aim of this work is to develop and validate machine-learning-based models to predict the exitus of a patient within the next year using data gathered at hospital admission. Five machine-learning techniques were applied using a retrospective dataset. The evaluation was performed with five metrics computed by a resampling strategy: Accuracy, the area under the ROC curve, Specificity, Sensitivity, and the Balanced Error Rate. All models reported an AUC ROC from 0.857 to 0.91. Specifically, Gradient Boosting Classifier was the best model, producing an AUC ROC of 0.91, a sensitivity of 0.858, a specificity of 0.808, and a BER of 0.1687. Information from standard procedures at hospital admission combined with machine learning techniques produced models with competitive discriminative power. Our models reach the best results reported in the state of the art. These results demonstrate that they can be used as an accurate data-driven palliative care criteria inclusion.
|
Caron, S., Eckner, C., Hendriks, L., Johannesson, G., Ruiz de Austri, R., & Zaharijas, G. (2023). Mind the gap: the discrepancy between simulation and reality drives interpretations of the Galactic Center Excess. J. Cosmol. Astropart. Phys., 06(6), 013–56pp.
Abstract: The Galactic Center Excess (GCE) in GeV gamma rays has been debated for over a decade, with the possibility that it might be due to dark matter annihilation or undetected point sources such as millisecond pulsars (MSPs). This study investigates how the gamma-ray emission model (-yEM) used in Galactic center analyses affects the interpretation of the GCE's nature. To address this issue, we construct an ultra-fast and powerful inference pipeline based on convolutional Deep Ensemble Networks. We explore the two main competing hypotheses for the GCE using a set of-yEMs with increasing parametric freedom. We calculate the fractional contribution (fsrc) of a dim population of MSPs to the total luminosity of the GCE and analyze its dependence on the complexity of the ryEM. For the simplest ryEM, we obtain fsrc = 0.10 f 0.07, while the most complex model yields fsrc = 0.79 f 0.24. In conclusion, we find that the statement about the nature of the GCE (dark matter or not) strongly depends on the assumed ryEM. The quoted results for fsrc do not account for the additional uncertainty arising from the fact that the observed gamma-ray sky is out-of-distribution concerning the investigated ryEM iterations. We quantify the reality gap between our ryEMs using deep-learning-based One-Class Deep Support Vector Data Description networks, revealing that all employed ryEMs have gaps to reality. Our study casts doubt on the validity of previous conclusions regarding the GCE and dark matter, and underscores the urgent need to account for the reality gap and consider previously overlooked “out of domain” uncertainties in future interpretations.
|
