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Siciliano, M. et al, & Gadea, A. (2020). Shape coexistence in neutron-deficient Hg-188 investigated via lifetime measurements. Phys. Rev. C, 102(1), 014318–16pp.
Abstract: Background: Shape coexistence in the Z approximate to 82 region has been established in mercury, lead, and polonium isotopes. For even-even mercury isotopes with 100 <= N <= 106 multiple fingerprints of this phenomenon are observed, which seems to be no longer present for N >= 110. According to a number of theoretical calculations, shape coexistence is predicted in the Hg-188 isotope. Purpose: The aim of this work was to measure lifetimes of excited states in Hg-188 to infer their collective properties, such as the deformation. Extending the investigation to higher-spin states, which are expected to be less affected by band-mixing effects, can provide additional information on the coexisting structures. Methods: The Hg-188 nucleus was populated using two different fusion-evaporation reactions with two targets, Gd-158 and Gd-160, and a beam of S-34 provided by the Tandem-ALPI accelerator complex at the Laboratori Nazionali di Legnaro. The channels of interest were selected using the information from the Neutron Wall array, while the gamma rays were detected using the GALILEO gamma-ray spectrometer. Lifetimes of excited states were determined using the recoil-distance Doppler-shift method, employing the dedicated GALILEO plunger device. Results: Lifetimes of the states up to spin 16 (h) over bar were measured and the corresponding reduced transition probabilities were calculated. Assuming two-band mixing and adopting, as done commonly, the rotational model, the mixing strengths and the deformation parameters of the unperturbed structures were obtained from the experimental results. In order to shed light on the nature of the observed configurations in the Hg-188 nucleus, the extracted transition strengths were compared with those resulting from state-of-the-art beyond-mean-field calculations using the symmetry-conserving configuration-mixing approach, limited to axial shapes, and the five-dimensional collective Hamiltonian, including the triaxial degree of freedom. Conclusions: The first lifetime measurement for states with spin >= 6 suggested the presence of an almost spherical structure above the 12(1)(+) isomer and allowed elucidating the structure of the intruder band. The comparison of the extracted B(E2) strengths with the two-band mixing model allowed the determination of the ground-state band deformation. Both beyond-mean-field calculations predict coexistence of a weakly deformed band with a strongly prolate-deformed one, characterized by elongation parameters similar to those obtained experimentally, but the calculated relative position of the bands and their mixing strongly differ.
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Segarra, A., & Bernabeu, J. (2020). Absolute neutrino mass and the Dirac/Majorana distinction from the weak interaction of aggregate matter. Phys. Rev. D, 101(9), 093004–6pp.
Abstract: The 2 nu-mediated force has a range of microns, well beyond the atomic scale. The effective potential is built from the t-channel absorptive part of the scattering amplitude and depends on neutrino properties on shell. We demonstrate that neutral aggregate matter has a weak charge and calculate the matrix of six coherent charges for its interaction with definite-mass neutrinos. Near the range of the potential the neutrino pair is nonrelativistic, leading to observable absolute mass and Dirac/Majorana distinction via different r-dependence and violation of the weak equivalence principle.
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Schreeck, H., Paschen, B., Wieduwilt, P., Ahlburg, P., Andricek, L., Dingfelder, J., et al. (2020). Effects of gamma irradiation on DEPFET pixel sensors for the Belle II experiment. Nucl. Instrum. Methods Phys. Res. A, 959, 163522–9pp.
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.
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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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Sanchis-Lozano, M. A., Sarkisyan-Grinbaum, E. K., Domenech-Garret, J. L., & Sanchis-Gual, N. (2020). Cosmological analogies in the search for new physics in high-energy collisions. Phys. Rev. D, 102(3), 035013–7pp.
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.
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