LHCb Collaboration(Aaij, R. et al), Garcia Martin, L. M., Henry, L., Jashal, B. K., Martinez-Vidal, F., Oyanguren, A., et al. (2020). Observation of the semileptonic decay B+-> p(p)over-bar mu(+)nu(mu). J. High Energy Phys., 03(3), 146–22pp.
Abstract: The Cabibbo-suppressed semileptonic decay B+-> pp over bar mu+nu μis observed for the first time using a sample of pp collisions corresponding to an integrated luminosity of 1.0, 2.0 and 1.7 fb at centre-of-mass energies of 7, 8 and 13TeV, respectively. The differential branching fraction is measured as a function of the pp invariant mass using the decay mode B+ ! J= K+ for normalisation. The total branching fraction is measured to be B (B+ ! pp+) = (5:27+0:23 0:21 0:15) 10 where the first uncertainty is statistical, the second systematic and the third is from the uncertainty on the branching fraction of the normalisation channel.
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ATLAS Collaboration(Aad, G. et al), Alvarez Piqueras, D., Aparisi Pozo, J. A., Bailey, A. J., Cabrera Urban, S., Castillo, F. L., et al. (2020). Measurement of isolated-photon plus two-jet production in pp collisions at root s=13 TeV with the ATLAS detector. J. High Energy Phys., 03(3), 179–49pp.
Abstract: The dynamics of isolated-photon plus two-jet production in pp collisions at a centre-of-mass energy of 13 TeV are studied with the ATLAS detector at the LHC using a dataset corresponding to an integrated luminosity of 36.1 fb(-1). Cross sections are measured as functions of a variety of observables, including angular correlations and invariant masses of the objects in the final state, gamma + jet + jet. Measurements are also performed in phase-space regions enriched in each of the two underlying physical mechanisms, namely direct and fragmentation processes. The measurements cover the range of photon (jet) transverse momenta from 150 GeV (100 GeV) to 2 TeV. The tree-level plus parton-shower predictions from Sherpa and Pythia as well as the next-to-leading-order QCD predictions from Sherpa are compared with the measurements. The next-to-leading-order QCD predictions describe the data adequately in shape and normalisation except for regions of phase space such as those with high values of the invariant mass or rapidity separation of the two jets, where the predictions overestimate the data.
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Caputo, A., Regis, M., & Taoso, M. (2020). Searching for sterile neutrino with X-ray intensity mapping. J. Cosmol. Astropart. Phys., 03(3), 001–21pp.
Abstract: The cosmological X-ray emission associated to the possible radiative decay of sterile neutrinos is composed by a collection of lines at different energies. For a given mass, each line corresponds to a given redshift. In this work, we cross correlate such line emission with catalogs of galaxies tracing the dark matter distribution at different redshifts. We derive observational prospects by correlating the X-ray sky that will be probed by the eROSITA and Athena missions with current and near future photometric and spectroscopic galaxy surveys. A relevant and unexplored fraction of the parameter space of sterile neutrinos can be probed by this technique.
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Schaffter, T. et al, Albiol, F., & Caballero, L. (2020). Evaluation of Combined Artificial Intelligence and Radiologist Assessment to Interpret Screening Mammograms. JAMA Netw. Open, 3(3), e200265–15pp.
Abstract: Importance Mammography screening currently relies on subjective human interpretation. Artificial intelligence (AI) advances could be used to increase mammography screening accuracy by reducing missed cancers and false positives. Objective To evaluate whether AI can overcome human mammography interpretation limitations with a rigorous, unbiased evaluation of machine learning algorithms. Design, Setting, and Participants In this diagnostic accuracy study conducted between September 2016 and November 2017, an international, crowdsourced challenge was hosted to foster AI algorithm development focused on interpreting screening mammography. More than 1100 participants comprising 126 teams from 44 countries participated. Analysis began November 18, 2016. Main Outcomes and Measurements Algorithms used images alone (challenge 1) or combined images, previous examinations (if available), and clinical and demographic risk factor data (challenge 2) and output a score that translated to cancer yes/no within 12 months. Algorithm accuracy for breast cancer detection was evaluated using area under the curve and algorithm specificity compared with radiologists' specificity with radiologists' sensitivity set at 85.9% (United States) and 83.9% (Sweden). An ensemble method aggregating top-performing AI algorithms and radiologists' recall assessment was developed and evaluated. Results Overall, 144231 screening mammograms from 85580 US women (952 cancer positive <= 12 months from screening) were used for algorithm training and validation. A second independent validation cohort included 166578 examinations from 68008 Swedish women (780 cancer positive). The top-performing algorithm achieved an area under the curve of 0.858 (United States) and 0.903 (Sweden) and 66.2% (United States) and 81.2% (Sweden) specificity at the radiologists' sensitivity, lower than community-practice radiologists' specificity of 90.5% (United States) and 98.5% (Sweden). Combining top-performing algorithms and US radiologist assessments resulted in a higher area under the curve of 0.942 and achieved a significantly improved specificity (92.0%) at the same sensitivity. Conclusions and Relevance While no single AI algorithm outperformed radiologists, an ensemble of AI algorithms combined with radiologist assessment in a single-reader screening environment improved overall accuracy. This study underscores the potential of using machine learning methods for enhancing mammography screening interpretation. Question How do deep learning algorithms perform compared with radiologists in screening mammography interpretation? Findings In this diagnostic accuracy study using 144231 screening mammograms from 85580 women from the United States and 166578 screening mammograms from 68008 women from Sweden, no single artificial intelligence algorithm outperformed US community radiologist benchmarks; including clinical data and prior mammograms did not improve artificial intelligence performance. However, combining best-performing artificial intelligence algorithms with single-radiologist assessment demonstrated increased specificity. Meaning Integrating artificial intelligence to mammography interpretation in single-radiologist settings could yield significant performance improvements, with the potential to reduce health care system expenditures and address resource scarcity experienced in population-based screening programs. This diagnostic accuracy study evaluates whether artificial intelligence can overcome human mammography interpretation limits with a rigorous, unbiased evaluation of machine learning algorithms.
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LHCb Collaboration(Aaij, R. et al), Henry, L., Jashal, B. K., Martinez-Vidal, F., Oyanguren, A., Remon Alepuz, C., et al. (2021). Observation of the Bs0 -> (DD -/+)-D-*+/- decay. J. High Energy Phys., 03(3), 099–19pp.
Abstract: A search for the B-s(0) -> D*(+/-) D--/+ decay is performed using proton-proton collision data at centre-of-mass energies of 7, 8 and 13TeV collected by the LHCb experiment, corresponding to an integrated luminosity of 9 fb(-1). The decay is observed with a high significance and its branching fraction relative to the B-0 -> D*(+/-) D--/+ decay is measured to be B(B-s(0) -> D*D-+/-(-/+))/B(B-0 -> D*D-+/-(-/+)) = 0.137 +/- 0.017 +/- 0.002 +/- 0.006, where the first uncertainty is statistical, the second systematic and the third is due to the uncertainty on the ratio of the B-s(0) and B-0 hadronisation fractions.
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