Abstract: In general two-Higgs-doublet models (2HDMs) without scalar flavor changing neutral couplings (SFCNC) in the lepton sector, the electron, muon, and tau interactions can be decoupled in a robust framework, stable under renormalization group evolution. In this framework, the breaking of lepton flavor universality (LFU) goes beyond the mass proportionality, opening the possibility to accommodate in a simple manner a different behavior among charged leptons. We analyze simultaneously the electron and muon (g – 2) anomalies in the context of these general flavor conserving models in the leptonic sector (gtlFC). We consider two different models, I-gtlFC and II-gelFC, in which the quark Yukawa couplings coincide, respectively, with the ones in type I and in type II 2HDMs. We find two types of solutions that fully reproduce both (g – 2) anomalies, and which are compatible with experimental constraints from LEP and LHC, from LFU, from flavor and electroweak physics, and with theoretical constraints in the scalar sector. In the first type of solution, all the new scalars have masses in the 1-2.5 TeV range, the vacuum expectation values (vevs) of both doublets are quite similar in magnitude, and both anomalies are dominated by two loop Barr-Zee contributions. This solution appears in both models. There is a second type of solution, where one loop contributions are dominant in the muon anomaly, all new scalars have masses below 1 TeV, and the ratio of vevs is in the range 10-100. The second neutral scalar H is the lighter among the new scalars, with a mass in the 210-390 GeV range while the pseudoscalar A is the heavier, with a mass in the range 400-900 GeV. The new charged scalar H-+/- is almost degenerate either with the scalar or with the pseudoscalar. This second type of solution only appears in the I-gelFC model. Both solutions require the soft breaking of the Z(2) symmetry of the Higgs potential.

Abstract: In this paper, we explore the impact of flavor violating neutral current non-standard interaction (NSI) parameter epsilon(mu tau) in the oscillation of atmospheric neutrinos and antineutrinos separately using the 50 kt magnetized ICAL detector at INO. We find that due to non-zero epsilon(mu tau), nu(mu) -> nu(mu) and (nu) over bar (mu) -> (nu) over bar (mu) transition probabilities get modified substantially at higher energies and longer baselines, where vacuum oscillation dominates. We demonstrate for the first time that by adding the hadron energy information along with the muon energy and muon direction in each event, the sensitivity of ICAL to the NSI parameter epsilon(mu tau) can be enhanced significantly. The most optimistic bound on epsilon(mu tau) that we obtain is – 0.01 < epsilon(mu tau) < 0.01 at 90% C.L. using 500 kt.yr exposure and considering E-mu, cos theta(mu), and E-had' as observables in their ranges of [1, 21] GeV, [- 1, 1], and [0, 25] GeV, respectively. We discuss for the first time the importance of the charge identification capability of the ICAL detector to have better constraints on epsilon(mu t). We also study the impact of non-zero epsilon(mu tau) on mass hierarchy determination and precision measurement of oscillation parameters.

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.

Abstract: Evidence for electroweak production of two jets in association with a Z gamma pair in root s = 13 TeV proton-proton collisions at the Large Hadron Collider is presented. The analysis uses data collected by the ATLAS detector in 2015 and 2016 that corresponds to an integrated luminosity of 36.1 fb(-1). Events that contain a Z boson candidate decaying leptonically into either e(+)e(-) or mu(+)mu(-), a photon, and two jets are selected. The electroweak component is measured with observed and expected significances of 4.1 standard deviations. The fiducial cross-section for electroweak production is measured to be sigma(Z gamma jj-Ew) = 7.8 +/- 2.0 fb, in good agreement with the Standard Model prediction.

Abstract: The beta decay of the semimagic Sn isotopes Sn-136,Sn-137,Sn-138 has been studied at the Radioactive Isotope Beam Factory at the RIKEN Nishina Center. The first experimental information on excited states was obtained for Sb-137 while, in the case of Sb-136, the established excitation scheme could be extended by ten previously unidentified levels. In the decay of the most-neutron-rich isotope Sn-138, two gamma rays were observed for the first time. The new experimental results, in combination with state-of-the-art shell-model calculations, provide the first information with respect to the evolution of the Og(7/2) and 1d(5/2) proton single-particle states with increasing neutron number beyond N = 84.