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Bordes, J., Chan, H. M., & Tsou, S. T. (2021). delta(CP) for leptons and a new take on CP physics with the FSM. Int. J. Mod. Phys. A, 36, 2150236–22pp.
Abstract: A bonus of the framed Standard Model (FSM), constructed initially to explain the mass and mixing patterns of quarks and leptons, is a solution (without axions) of the strong CP problem by cancelling the theta-angle term theta(I) Tr(H-mu v H-mu v*) in coloura by a chiral transformation on a quark zero mode which is inherent in FSM, and produces thereby a CP-violating phase in the CKM matrix similar in size to what is observed.' Extending here to flavour, one finds that there are two terms proportional to Tr(G(mu v) G(mu v)*): (a) in the action from flavour instantons with unknown coefficient, say theta(I)', (b) induced by the above FSM solution to the strong CP-problem with therefore known coefficient theta(C)'. Both terms can be cancelled in the FSM by a chiral transformation on the lepton zero mode to give a Jarlskog invariant J' in the PMNS matrix for leptons of order 10(-2), as is hinted by the experiment. But if, as suggested in Ref. 2, the term theta(I)' is to be cancelled by a chiral transformation in the predicted hidden sector to solve the strong CP problem therein, leaving only the term theta(C)' to be cancelled by the chiral transformation on leptons, then the following prediction results: J' similar to -0.012 (delta(CP)'similar to (1.11)pi) which is (i) of the right order, (ii) of the right sign and (iii) in the range favoured by the present experiment. Together with the earlier result for quarks, this offers an attractive unified treatment of all known CP physics.
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NEXT Collaboration(Kekic, M. et al), Benlloch-Rodriguez, J. M., Carcel, S., Carrion, J. V., Diaz, J., Felkai, R., et al. (2021). Demonstration of background rejection using deep convolutional neural networks in the NEXT experiment. J. High Energy Phys., 01(1), 189–22pp.
Abstract: Convolutional neural networks (CNNs) are widely used state-of-the-art computer vision tools that are becoming increasingly popular in high-energy physics. In this paper, we attempt to understand the potential of CNNs for event classification in the NEXT experiment, which will search for neutrinoless double-beta decay in Xe-136. To do so, we demonstrate the usage of CNNs for the identification of electron-positron pair production events, which exhibit a topology similar to that of a neutrinoless double-beta decay event. These events were produced in the NEXT-White high-pressure xenon TPC using 2.6 MeV gamma rays from a Th-228 calibration source. We train a network on Monte Carlo-simulated events and show that, by applying on-the-fly data augmentation, the network can be made robust against differences between simulation and data. The use of CNNs offers significant improvement in signal efficiency and background rejection when compared to previous non-CNN-based analyses.
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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.
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Esposito, R. et al, & Domingo-Pardo, C. (2021). Design of the third-generation lead-based neutron spallation target for the neutron time-of-flight facility at CERN. Phys. Rev. Accel. Beams, 24(9), 093001–17pp.
Abstract: The neutron time-of-flight (n_TOF) facility at the European Laboratory for Particle Physics (CERN) is a pulsed white-spectrum neutron spallation source producing neutrons for two experimental areas: the Experimental Area 1 (EAR1), located 185 m horizontally from the target, and the Experimental Area 2 (EAR2), located 20 m above the target. The target, based on pure lead, is impacted by a high-intensity 20-GeV/c pulsed proton beam. The facility was conceived to study neutron-nucleus interactions for neutron kinetic energies between a few meV to several GeV, with applications of interest for nuclear astrophysics, nuclear technology, and medical research. After the second-generation target reached the end of its lifetime, the facility underwent a major upgrade during CERN's Long Shutdown 2 (LS2, 2019-2021), which included the installation of the new third-generation neutron target. The first- and second-generation targets were based on water-cooled massive lead blocks and were designed focusing on EAR1, since EAR2 was built later. The new target is cooled by nitrogen gas to avoid erosion-corrosion and contamination of cooling water with radioactive lead spallation products. Moreover, the new design is optimized also for the vertical flight path and EAR2. This paper presents an overview of the target design focused on both physics and thermomechanical performance, and includes a description of the nitrogen cooling circuit and radiation protection studies.
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Polettini, M. et al, & Algora, A. (2021). DESPEC Phase-0 campaign at GSI. Nuovo Cim. C, 44(2-3), 67–4pp.
Abstract: This paper reports preliminary results of the DESPEC campaign at GSI, focused on the study of neutron-deficient nuclei approaching Sn-100. The results presented show the isomeric decays of excited states with I-pi = 14(+) and 8(+) in Pd-96 and Pd-94, respectively. The detailed characterisation of the DESPEC set-up and analysis methodologies, proven in this experimental run, are crucial for the future campaigns.
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