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Schiavone, T., Montani, G., & Bombacigno, F. (2023). f(R) gravity in the Jordan frame as a paradigm for the Hubble tension. Mon. Not. Roy. Astron. Soc., 522(1), L72–L77.
Abstract: We analyse the f(R) gravity in the so-called Jordan frame, as implemented to the isotropic Universe dynamics. The goal of the present study is to show that according to recent data analyses of the supernovae Ia Pantheon sample, it is possible to account for an effective redshift dependence of the Hubble constant. This is achieved via the dynamics of a non-minimally coupled scalar field, as it emerges in the f(R) gravity. We face the question both from an analytical and purely numerical point of view, following the same technical paradigm. We arrive to establish that the expected decay of the Hubble constant with the redshift z is ensured by a form of the scalar field potential, which remains essentially constant for z less than or similar to 0.3, independently if this request is made a priori, as in the analytical approach, or obtained a posteriori, when the numerical procedure is addressed. Thus, we demonstrate that an f(R) dark energy model is able to account for an apparent variation of the Hubble constant due to the rescaling of the Einstein constant by the f(R) scalar mode.
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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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Saul-Sala, E., Sobczyk, J. E., Rafi Alam, M., Alvarez-Ruso, L., & Nieves, J. (2021). Weak kaon production off the nucleon and Watson's theorem. Phys. Lett. B, 817, 136349–7pp.
Abstract: We have improved the tree-level model of Ref.[1] for weak production of kaons off nucleons by partially restoring unitarity. This is achieved by imposing Watson's theorem to the dominant vector and axial-vector contributions in appropriate angular momentum and isospin quantum number sectors. The observable consequences of this procedure are investigated.
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Mantovani Sarti, V., Feijoo, A., Vidana, I., Ramos, A., Giacosa, F., Hyodo, T., et al. (2024). Constraining the low-energy S =-2 meson-baryon interaction with two-particle correlations. Phys. Rev. D, 110(1), L011505–8pp.
Abstract: In this paper we present a novel method to extract information on hadron-hadron interactions using for the first time femtoscopic data to constrain the low-energy constants of a QCD effective Lagrangian. This method offers a new way to investigate the nonperturbative regime of QCD in sectors where scattering experiments are not feasible, such as the multistrange and charm ones. As an example of its application, we use the very precise K-Lambda correlation function data, recently measured in pp collisions at LHC, to constrain the strangeness S = -2 meson-baryon interaction. The model obtained delivers new insights on the molecular nature of the Xi(1620) and Xi(1690) states.
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Sarriguren, P., Algora, A., & Kiss, G. (2018). beta-decay properties of neutron-rich Ca, Ti, and Cr isotopes. Phys. Rev. C, 98(2), 024311–10pp.
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.
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