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Flores, M. M., Kim, J. S., Rolbiecki, K., & Ruiz de Austri, R. (2023). Updated LHC bounds on MUED after run 2. Int. J. Mod. Phys. A, 38(1), 2350002–14pp.
Abstract: We present updated LHC limits on the minimal universal extra dimensions (MUEDs) model from the Run 2 searches. We scan the parameter space against a number of searches implemented in the public code CheckMATE and derive up-to-date limits on the MUED parameter space from 13TeV searches. The strongest constraints come from a search dedicated to squarks and gluinos with one isolated lepton, jets and missing transverse energy. In the procedure, we take into account initial state radiation and stress its importance in the MUED searches, which is not always appreciated.
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Bordes, J., Chan, H. M., & Tsou, S. T. (2023). A vacuum transition in the FSM with a possible new take on the horizon problem in cosmology. Int. J. Mod. Phys. A, 38(25), 2350124–32pp.
Abstract: The framed standard model (FSM), constructed to explain the empirical mass and mixing patterns (including CP phases) of quarks and leptons, in which it has done quite well, gives otherwise the same result as the standard model (SM) in almost all areas in particle physics where the SM has been successfully applied, except for a few specified deviations such as the W mass and the g-2 of muons, that is, just where experiment is showing departures from what SM predicts. It predicts further the existence of a hidden sector of particles some of which may function as dark matter. In this paper, we first note that the above results involve, surprisingly, the FSM undergoing a vacuum transition (VTR1) at a scale of around 17MeV, where the vacuum expectation values of the colour framons (framed vectors promoted into fields) which are all nonzero above that scale acquire some vanishing components below it. This implies that the metric pertaining to these vanishing components would vanish also. Important consequences should then ensue, but these occur mostly in the unknown hidden sector where empirical confirmation is hard at present to come by, but they give small reflections in the standard sector, some of which may have already been seen. However, one notes that if, going off at a tangent, one imagines colour to be embedded, Kaluza-Klein (KK) fashion, into a higher-dimensional space-time, then this VTR1 would cause 2 of the compactified dimensions to collapse. This might mean then that when the universe cooled to the corresponding temperature of 1011 K when it was about 10-3 s old, this VTR1 collapse would cause the three spatial dimensions of the universe to expand to compensate. The resultant expansion is estimated, using FSM parameters previously determined from particle physics, to be capable, when extrapolated backwards in time, of bringing the present universe back inside the then horizon, solving thus formally the horizon problem. Besides, VTR1 being a global phenomenon in the FSM, it would switch on and off automatically and simultaneously over all space, thus requiring seemingly no additional strategy for a graceful exit. However, this scenario has not been checked for consistency with other properties of the universe and is to be taken thus not as a candidate solution of the horizon problem but only as an observation from particle physics which might be of interest to cosmologists and experts in the early universe. For particle physicists also, it might serve as an indicator for how relevant this VTR1 can be, even if the KK assumption is not made.
Keywords: Framed standard model; phase transition; early Universe; cosmology
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Bordes, J., Chan, H. M., & Tsou, S. T. (2023). Search for new physics in semileptonic decays of K and B as implied by the g-2 anomaly in FSM. Int. J. Mod. Phys. A, 38, 2350177–24pp.
Abstract: The framed standard model (FSM), constructed to explain, with some success, why there should be three and apparently only three generations of quarks and leptons in nature falling into a hierarchical mass and mixing pattern,(10) suggests also, among other things, a scalar boson U, with mass around 17 MeV and small couplings to quarks and leptons,(11) which might explain(9) the g – 2 anomaly reported in experiment.(12) The U arises in FSM initially as a state in the predicted “hidden sector” with mass around 17 MeV, which mixes with the standard model (SM) Higgs h(W), acquiring thereby a coupling to quarks and leptons and a mass just below 17 MeV. The initial purpose of this paper is to check whether this proposal is compatible with experiment on semileptonic decays of Ks and Bs where the U can also appear. The answer to this we find is affirmative, in that the contribution of U to new physics as calculated in the FSM remains within the experimental bounds, but only if m(U) lies within a narrow range just below the unmixed mass. As a result from this, one has an estimate m(U) similar to 15-17 MeV for the mass of U, and from some further considerations the estimate Gamma(U) similar to 0.02 eV for its width, both of which may be useful for an eventual search for it in experiment. If found, it will be, for the FSM, not just the discovery of a predicted new particle, but the opening of a window into a whole “hidden sector” containing at least some, perhaps even the bulk, of the dark matter in the universe.
Keywords: Framed standard model; light scalar boson; meson decays
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Olmo, G. J., & Rubiera-Garcia, D. (2022). Some recent results on Ricci-based gravity theories. Int. J. Mod. Phys. D, 31, 2240012–15pp.
Abstract: In this paper, metric-afline theories in which the gravity Lagrangian is built using (projectively invariant) contractions of the Ricci tensor with itself and with the metric (Ricci-based gravity theories, or RBGs for short) are reviewed. The goal is to provide a contextualized and coherent presentation of some recent results. In particular, we focus on the correspondence that exists between the field equations of these theories and those of general relativity, and comment on how this can be used to build new solutions of physical interest. We also discuss the formalism of junction conditions in the f (R) case, and provide a brief summary on current experimental and observational bounds on model parameters.
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Garcilazo, H., Valcarce, A., & Vijande, J. (2020). Neutral baryonic systems with strangeness. Int. J. Mod. Phys. E, 29(1), 1930009–22pp.
Abstract: We review the status as regards to the existence of three- and four-body bound states made of neutrons and Lambda hyperons. For interesting cases, the coupling to neutral baryonic systems made of charged particles of different strangeness has been addressed. There are strong arguments showing that the Lambda nn system has no bound states. Lambda Lambda nn strong stable states are not favored by our current knowledge of the strangeness -1 and -2 baryon-baryon interactions. However, a possible Xi(-) t quasibound state decaying to Lambda Lambda nn might exist in nature. Similarly, there is a broad agreement about the nonexistence of Lambda Lambda n bound states. However, the coupling to Xi NN states opens the door to a resonance above the Lambda Lambda n threshold.
Keywords: Few-body systems; baryon-baryon interaction; hypernuclei
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Wang, D. (2023). Finslerian Universe May Reconcile Tensions Between High and Low Redshift Probes. Int. J. Theor. Phys., 62(8), 184–11pp.
Abstract: To reconcile the current tensions between high and low redshift observations, we perform the first constraints on the Finslerian cosmological models including the effective dark matter and dark energy components. We find that all the four Finslerian models could alleviate effectively the Hubble constant (H-0) tension and the amplitude of the root-mean-square density fluctu-ations (s(8)) tension between the Planck measurements and the local Universe observations at the 68% confidence level. The addition of a massless sterile neutrino and a varying total mass of active neutrinos to the base Finslerian two-parameter model, respectively, reduces the H-0 tension from 3.4s to 1.9s and alleviates the s8 tension better than the other three Finslerian models. Computing the Bayesian evidence, with respect to ACDM model, our analysis shows a weak preference for the base Finslerian model and moderate preferences for its three one-parameter extensions. Based on the model-independent Gaussian Processes, we propose a new linear relation which can describe the current redshift space distortions data very well. Using the most stringent constraints we can provide, we have also obtained the limits of typical model parameters for three one-parameter extensional models.
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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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KM3NeT Collaboration(Aiello, S. et al), Calvo, D., Coleiro, A., Colomer, M., Gozzini, S. R., Hernandez-Rey, J. J., et al. (2019). KM3NeT front-end and readout electronics system: hardware, firmware, and software. J. Astron. Telesc. Instrum. Syst., 5(4), 046001–15pp.
Abstract: The KM3NeT research infrastructure being built at the bottom of the Mediterranean Sea will host water-Cherenkov telescopes for the detection of cosmic neutrinos. The neutrino telescopes will consist of large volume three-dimensional grids of optical modules to detect the Cherenkov light from charged particles produced by neutrino-induced interactions. Each optical module houses 31 3-in. photomultiplier tubes, instrumentation for calibration of the photomultiplier signal and positioning of the optical module, and all associated electronics boards. By design, the total electrical power consumption of an optical module has been capped at seven Watts. We present an overview of the front-end and readout electronics system inside the optical module, which has been designed for a 1-ns synchronization between the clocks of all optical modules in the grid during a life time of at least 20 years. (C) 2019 Society of Photo-Optical Instrumentation Engineers (SPIE)
Keywords: front-end electronics; readout electronics; neutrino telescope; KM3NeT
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KM3NeT Collaboration(Aiello, S. et al), Alves Garre, S., Calvo, D., Carretero, V., Colomer, M., Corredoira, I., et al. (2021). Architecture and performance of the KM3NeT front-end firmware. J. Astron. Telesc. Instrum. Syst., 7(1), 016001–24pp.
Abstract: The KM3NeT infrastructure consists of two deep-sea neutrino telescopes being deployed in the Mediterranean Sea. The telescopes will detect extraterrestrial and atmospheric neutrinos by means of the incident photons induced by the passage of relativistic charged particles through the seawater as a consequence of a neutrino interaction. The telescopes are configured in a three-dimensional grid of digital optical modules, each hosting 31 photomultipliers. The photomultiplier signals produced by the incident Cherenkov photons are converted into digital information consisting of the integrated pulse duration and the time at which it surpasses a chosen threshold. The digitization is done by means of time to digital converters (TDCs) embedded in the field programmable gate array of the central logic board. Subsequently, a state machine formats the acquired data for its transmission to shore. We present the architecture and performance of the front-end firmware consisting of the TDCs and the state machine.
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Carrasco-Ribelles, L. A., Pardo-Mas, J. R., Tortajada, S., Saez, C., Valdivieso, B., & Garcia-Gomez, J. M. (2021). Predicting morbidity by local similarities in multi-scale patient trajectories. J. Biomed. Inform., 120, 103837–9pp.
Abstract: Patient Trajectories (PTs) are a method of representing the temporal evolution of patients. They can include information from different sources and be used in socio-medical or clinical domains. PTs have generally been used to generate and study the most common trajectories in, for instance, the development of a disease. On the other hand, healthcare predictive models generally rely on static snapshots of patient information. Only a few works about prediction in healthcare have been found that use PTs, and therefore benefit from their temporal dimension. All of them, however, have used PTs created from single-source information. Therefore, the use of longitudinal multi-scale data to build PTs and use them to obtain predictions about health conditions is yet to be explored. Our hypothesis is that local similarities on small chunks of PTs can identify similar patients concerning their future morbidities. The objectives of this work are (1) to develop a methodology to identify local similarities between PTs before the occurrence of morbidities to predict these on new query individuals; and (2) to validate this methodology on risk prediction of cardiovascular diseases (CVD) occurrence in patients with diabetes. We have proposed a novel formal definition of PTs based on sequences of longitudinal multi-scale data. Moreover, a dynamic programming methodology to identify local alignments on PTs for predicting future morbidities is proposed. Both the proposed methodology for PT definition and the alignment algorithm are generic to be applied on any clinical domain. We validated this solution for predicting CVD in patients with diabetes and we achieved a precision of 0.33, a recall of 0.72 and a specificity of 0.38. Therefore, the proposed solution in the diabetes use case can result of utmost utility to secondary screening.
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