Abstract: High-energy muon and electron neutrinos yield a non-negligible flux of tau neutrinos as they propagate through Earth. In this Letter, we address the impact of this additional component in the PeV and EeV energy regimes for the first time. Above 300 TeV, this contribution is predicted to be significantly larger than the atmospheric background, and it alters current and future neutrino telescopes' capabilities to discover a cosmic tau-neutrino flux. Further, we demonstrate that Earth-skimming neutrino experiments, designed to observe tau neutrinos, will be sensitive to cosmogenic neutrinos even in extreme scenarios without a primary tau-neutrino component.

Abstract: i-TED is an innovative detection system which exploits Compton imaging techniques to achieve a superior signal-to-background ratio in (n, gamma) cross-section measurements using time-of-flight technique. This work presents the first experimental validation of the i-TED apparatus for high-resolution time-of-flight experiments and demonstrates for the first time the concept proposed for background rejection. To this aim, the Au-197(n, gamma) and Fe-56(n, gamma) reactions were studied at CERN n_TOF using an i-TED demonstrator based on three position-sensitive detectors. Two C6D6 detectors were also used to benchmark the performance of i-TED. The i-TED prototype built for this study shows a factor of similar to 3 higher detection sensitivity than state-of-the-art C6D6 detectors in the 10 keV neutron-energy region of astrophysical interest. This paper explores also the perspectives of further enhancement in performance attainable with the final i-TED array consisting of twenty position-sensitive detectors and newanalysis methodologies based on Machine-Learning techniques.

Abstract: If neutrinos get mass through the exchange of lepton mediators, as in seesaw schemes, the neutrino appearance probabilities in oscillation experiments are modified due to effective nonunitarity of the lepton mixing matrix. This also leads to new CP phases and an ambiguity in underpinning the “conventional” phase of the three-neutrino paradigm. We study the CP sensitivities of various setups based at the European Spallation Source neutrino super-beam (ESSnuSB) experiment in the presence of nonunitarity. We also examine its potential in constraining the associated new physics parameters. Moreover, we show how the combination of DUNE and ESSnuSB can help further improve the sensitivities on the nonunitarity 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: In a general framework, valid for any H -> H' l(-)(v) over bar (l) semileptonic decay, we analyze the d(2)Gamma/(d omega d cos theta(l)) and d(2)Gamma/(d omega dE(l)) distributions, with omega being the product of the hadron four-velocities, theta(l) the angle made by the three-momenta of the charged lepton and the final hadron in the W- center of mass frame and E-l the charged lepton energy in the decaying hadron rest frame. Within the Standard Model (SM), d(2)Gamma/(d omega dE(l)) proportional to (c(0) (omega) c(1) (omega)E-l/M + c(2) (omega)E-l(2)/M-2), with M the initial hadron mass. We find that c(2) (omega) is independent of the lepton flavor and thus it is an ideal candidate to look for lepton flavor universality (LFU) violations. We also find a correlation between the a(2) (omega) structure function, which governs the (cos theta(l))(2) dependence of d(2)Gamma/(d omega d cos theta(l)), and c(2) (omega). Apart from trivial kinematical and mass factors, the ratio of a(2) (omega)/c(2) (omega) is a universal function that can be measured in any semileptonic decay, involving not only b -> c transitions. These two SM predictions can be used as new tests in the present search for signatures of LFU violations. We also generalize the formalism to account for some new physics (NP) terms, and show that neither c(2) nor a(2) are modified by left and right scalar NP terms, being however sensitive to left and right vector corrections. We also find that the a(2)/c(2) ratio is not modified by these latter NP contributions. Finally, and in order to illustrate our findings, we apply our general framework to the Lambda(b) -> Lambda(c)l (v) over bar (l) decay. We show that a measurement of c(2) (or a(2)) for tau decay would not only be a direct measurement of the possible existence of NP, but it would also allow to distinguish from NP fits to b -> c tau(v) over bar (tau) anomalies in the meson sector, which otherwise give the same total and differential d Gamma/d omega widths. We show that the same occurs for the other two terms, c(0) and c(1), that appear in d(2)Gamma/(d omega dE(l)), and for the cos theta(l) linear term of the angular distribution.