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Escudero, M., & Witte, S. J. (2020). A CMB search for the neutrino mass mechanism and its relation to the Hubble tension. Eur. Phys. J. C, 80(4), 294–10pp.
Abstract: The majoron, a pseudo-Goldstone boson arising from the spontaneous breaking of global lepton number, is a generic feature of many models intended to explain the origin of the small neutrino masses. In this work, we investigate potential imprints in the cosmic microwave background (CMB) arising from massive majorons, should they thermalize with neutrinos after Big Bang Nucleosynthesis via inverse neutrino decays. We show that Planck2018 measurements of the CMB are currently sensitive to neutrino-majoron couplings as small as lambda similar to 10-13, which if interpreted in the context of the type-I seesaw mechanism correspond to a lepton number symmetry breaking scale vL similar to O(100)GeV Additionally, we identify parameter space for which the majoron-neutrino interactions, collectively with an extra contribution to the effective number of relativistic species Neff, can ameliorate the outstanding H0 tension.
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NEXT Collaboration(Navarro, K. E. et al), Carcel, S., Carrion, J. V., Lopez, F., Lopez-March, N., Martin-Albo, J., et al. (2023). A compact dication source for Ba2+ tagging and heavy metal ion sensor development. J. Instrum., 18(7), P07044–19pp.
Abstract: We present a tunable metal ion beam that delivers controllable ion currents in the picoamp range for testing of dry-phase ion sensors. Ion beams are formed by sequential atomic evaporation and single or multiple electron impact ionization, followed by acceleration into a sensing region. Controllability of the ionic charge state is achieved through tuning of electrode potentials that influence the retention time in the ionization region. Barium, lead, and cadmium samples have been used to test the system, with ion currents identified and quantified using a quadrupole mass analyzer. Realization of a clean Ba2+ ion beam within a bench-top system represents an important technical advance toward the development and characterization of barium tagging systems for neutrinoless double beta decay searches in xenon gas. This system also provides a testbed for investigation of novel ion sensing methodologies for environmental assay applications, with dication beams of Pb2+ and Cd2+ also demonstrated for this purpose.
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n_TOF Collaboration(Bacak, M. et al), Domingo-Pardo, C., & Tain, J. L. (2020). A compact fission detector for fission-tagging neutron capture experiments with radioactive fissile isotopes. Nucl. Instrum. Methods Phys. Res. A, 969, 163981–10pp.
Abstract: In the measurement of neutron capture cross-sections of fissile isotopes, the fission channel is a source of background which can be removed efficiently using the so-called fission-tagging or fission-veto technique. For this purpose a new compact and fast fission chamber has been developed. The design criteria and technical description of the chamber are given within the context of a measurement of the U-233(n, gamma) cross-section at the nTOF facility at CERN, where it was coupled to the nTOF Total Absorption Calorimeter. For this measurement the fission detector was optimized for time resolution, minimization of material in the neutron beam and for alpha-fission discrimination. The performance of the fission chamber and its application as a fission tagging detector are discussed.
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Albiol, A., Albiol, F., Paredes, R., Plasencia-Martinez, J. M., Blanco Barrio, A., Garcia Santos, J. M., et al. (2022). A comparison of Covid-19 early detection between convolutional neural networks and radiologists. Insights Imaging, 13(1), 122–12pp.
Abstract: Background The role of chest radiography in COVID-19 disease has changed since the beginning of the pandemic from a diagnostic tool when microbiological resources were scarce to a different one focused on detecting and monitoring COVID-19 lung involvement. Using chest radiographs, early detection of the disease is still helpful in resource-poor environments. However, the sensitivity of a chest radiograph for diagnosing COVID-19 is modest, even for expert radiologists. In this paper, the performance of a deep learning algorithm on the first clinical encounter is evaluated and compared with a group of radiologists with different years of experience. Methods The algorithm uses an ensemble of four deep convolutional networks, Ensemble4Covid, trained to detect COVID-19 on frontal chest radiographs. The algorithm was tested using images from the first clinical encounter of positive and negative cases. Its performance was compared with five radiologists on a smaller test subset of patients. The algorithm's performance was also validated using the public dataset COVIDx. Results Compared to the consensus of five radiologists, the Ensemble4Covid model achieved an AUC of 0.85, whereas the radiologists achieved an AUC of 0.71. Compared with other state-of-the-art models, the performance of a single model of our ensemble achieved nonsignificant differences in the public dataset COVIDx. Conclusion The results show that the use of images from the first clinical encounter significantly drops the detection performance of COVID-19. The performance of our Ensemble4Covid under these challenging conditions is considerably higher compared to a consensus of five radiologists. Artificial intelligence can be used for the fast diagnosis of COVID-19.
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Balazs, C. et al, Mamuzic, J., & Ruiz de Austri, R. (2021). A comparison of optimisation algorithms for high-dimensional particle and astrophysics applications. J. High Energy Phys., 05(5), 108–46pp.
Abstract: Optimisation problems are ubiquitous in particle and astrophysics, and involve locating the optimum of a complicated function of many parameters that may be computationally expensive to evaluate. We describe a number of global optimisation algorithms that are not yet widely used in particle astrophysics, benchmark them against random sampling and existing techniques, and perform a detailed comparison of their performance on a range of test functions. These include four analytic test functions of varying dimensionality, and a realistic example derived from a recent global fit of weak-scale supersymmetry. Although the best algorithm to use depends on the function being investigated, we are able to present general conclusions about the relative merits of random sampling, Differential Evolution, Particle Swarm Optimisation, the Covariance Matrix Adaptation Evolution Strategy, Bayesian Optimisation, Grey Wolf Optimisation, and the PyGMO Artificial Bee Colony, Gaussian Particle Filter and Adaptive Memory Programming for Global Optimisation algorithms.
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