HAWC and HESS Collaborations(Abdalla, H. et al), & Salesa Greus, F. (2021). TeV Emission of Galactic Plane Sources with HAWC and HESS. Astrophys. J., 917(1), 6–16pp.
Abstract: The High Altitude Water Cherenkov (HAWC) observatory and the High Energy Stereoscopic System (H.E.S.S.) are two leading instruments in the ground-based very-high-energy gamma-ray domain. HAWC employs the water Cherenkov detection (WCD) technique, while H.E.S.S. is an array of Imaging Atmospheric Cherenkov Telescopes (IACTs). The two facilities therefore differ in multiple aspects, including their observation strategy, the size of their field of view, and their angular resolution, leading to different analysis approaches. Until now, it has been unclear if the results of observations by both types of instruments are consistent: several of the recently discovered HAWC sources have been followed up by IACTs, resulting in a confirmed detection only in a minority of cases. With this paper, we go further and try to resolve the tensions between previous results by performing a new analysis of the H.E.S.S. Galactic plane survey data, applying an analysis technique comparable between H.E.S.S. and HAWC. Events above 1 TeV are selected for both data sets, the point-spread function of H.E.S.S. is broadened to approach that of HAWC, and a similar background estimation method is used. This is the first detailed comparison of the Galactic plane observed by both instruments. H.E.S.S. can confirm the gamma-ray emission of four HAWC sources among seven previously undetected by IACTs, while the three others have measured fluxes below the sensitivity of the H.E.S.S. data set. Remaining differences in the overall gamma-ray flux can be explained by the systematic uncertainties. Therefore, we confirm a consistent view of the gamma-ray sky between WCD and IACT techniques.
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Yang, W. Q., Pan, S., Di Valentino, E., Mena, O., & Melchiorri, A. (2021). 2021-H-0 odyssey: closed, phantom and interacting dark energy cosmologies. J. Cosmol. Astropart. Phys., 10(10), 008–21pp.
Abstract: Up-to-date cosmological data analyses have shown that (sigma) a closed universe is preferred by the Planck data at more than 99% CL, and (b) interacting scenarios offer a very compelling solution to the Hubble constant tension. In light of these two recent appealing scenarios, we consider here an interacting dark matter-dark energy model with a non-zero spatial curvature component and a freely varying dark energy equation of state in both the quintessential and phantom regimes. When considering Cosmic Microwave Background data only, a phantom and closed universe can perfectly alleviate the Hubble tension, without the necessity of a coupling among the dark sectors. Accounting for other possible cosmological observations compromises the viability of this very attractive scenario as a global solution to current cosmological tensions, either by spoiling its effectiveness concerning the H-0 problem, as in the case of Supernovae Ia data, or by introducing a strong disagreement in the preferred value of the spatial curvature, as in the case of Baryon Acoustic Oscillations.
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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 phenomena beyond the Standard Model in events with large b-jet multiplicity using the ATLAS detector at the LHC. Eur. Phys. J. C, 81(1), 11–29pp.
Abstract: A search is presented for new phenomena in events characterised by high jet multiplicity, no leptons (electrons or muons), and four or more jets originating from the fragmentation of b-quarks (b-jets). The search uses 139fb(-1)of s root = 13 TeV proton-proton collision data collected by the ATLAS experiment at the Large Hadron Collider during Run 2. The dominant Standard Model background originates from multijet production and is estimated using a data-driven technique based on an extrapolation from events with low b-jet multiplicity to the high b-jet multiplicities used in the search. No significant excess over the Standard Model expectation is observed and 95% confidence-level limits that constrain simplified models of R-parity-violating supersymmetry are determined. The exclusion limits reach 950 GeV in top-squark mass in the models considered.
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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.
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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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