Abstract: For the Belle II experiment at KEK (Tsukuba, Japan) the KEKB accelerator was upgraded to deliver a 40 times larger instantaneous luminosity than before, which requires an increased radiation hardness of the detector components. As the innermost part of the Belle II detector, the pixel detector (PXD), based on DEPFET (DEpleted P-channel Field Effect Transistor) technology, is most exposed to radiation from the accelerator. An irradiation campaign was performed to verify that the PXD can cope with the expected amount of radiation. We present the results of this measurement campaign in which an X-ray machine was used to irradiate a single PXD half-ladder to a total dose of 266 kGy. The half-ladder is from the same batch as the half-ladders used for Belle II. According to simulations, the total accumulated dose corresponds to 7-10 years of Belle II operation. While individual components have been irradiated before, this campaign is the first full system irradiation. We discuss the effects on the DEPFET sensors, as well as the performance of the front-end electronics. In addition, we present efficiency studies of the half-ladder from beam tests performed before and after the irradiation.

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: beta-decay properties of neutron-rich Ca, Ti, and Cr isotopes are studied within a deformed proton-neutron quasiparticle random-phase approximation. The underlying mean field is described self-consistently from deformed Skyrme Hartree-Fock calculations with pairing correlations. Residual spin-isospin interactions in the particle-hole and particle-particle channels are also included in the formalism. The energy distributions of the Gamow-Teller strength, the beta-decay feedings, the beta-decay half-lives, and the beta-delayed neutron emission probabilities are discussed and compared with other theoretical results, as well as with the available experimental information. The evolution of these nuclear beta-decay properties is investigated in isotopic chains in a search for structural changes. A reliable estimate of the beta-decay properties in this mass region is valuable information for evaluating decay rates in astrophysical scenarios.

Abstract: We consider the generation of a baryon asymmetry in an extension of the Standard Model with two singlet Majorana fermions that are degenerate above the electroweak phase transition. The model can explain neutrino masses as well as the observed matter-antimatter asymmetry, for masses of the heavy singlets below the electroweak scale. The only physical CP violating phases in the model are those in the PMNS mixing matrix, i.e. the Dirac phase and a Majorana phase that enter light neutrino observables. We present an accurate analytic approximation for the baryon asymmetry in terms of CP flavour invariants, and derive the correlations with neutrino observables. We demonstrate that the measurement of CP violation in neutrino oscillations as well as the mixings of the heavy neutral leptons with the electron, muon and tau flavours suffice to pin down the matter-antimatter asymmetry from laboratory measurements.

Abstract: In this paper, analogies between multiparticle production in high-energy collisions and the time evolution of the early Universe are discussed. A common explanation is put forward under the assumption of an unconventional early state: a rapidly expanding universe before recombination (last scattering surface), followed by the cosmic microwave background, later evolving up to present days, versus the formation of hidden/dark states in hadronic collisions followed by a conventional QCD parton shower yielding final-state particles. In particular, long-range angular correlations are considered pointing out deep connections between the two physical cases potentially useful for the discovery of new physics.