Conde, D., Castillo, F. L., Escobar, C., García, C., Garcia Navarro, J. E., Sanz, V., et al. (2023). Forecasting Geomagnetic Storm Disturbances and Their Uncertainties Using Deep Learning. Space Weather, 21(11), e2023SW003474–27pp.
Abstract: Severe space weather produced by disturbed conditions on the Sun results in harmful effects both for humans in space and in high-latitude flights, and for technological systems such as spacecraft or communications. Also, geomagnetically induced currents (GICs) flowing on long ground-based conductors, such as power networks, potentially threaten critical infrastructures on Earth. The first step in developing an alarm system against GICs is to forecast them. This is a challenging task given the highly non-linear dependencies of the response of the magnetosphere to these perturbations. In the last few years, modern machine-learning models have shown to be very good at predicting magnetic activity indices. However, such complex models are on the one hand difficult to tune, and on the other hand they are known to bring along potentially large prediction uncertainties which are generally difficult to estimate. In this work we aim at predicting the SYM-H index characterizing geomagnetic storms multiple-hour ahead, using public interplanetary magnetic field (IMF) data from the Sun-Earth L1 Lagrange point and SYM-H data. We implement a type of machine-learning model called long short-term memory (LSTM) network. Our scope is to estimate the prediction uncertainties coming from a deep-learning model in the context of forecasting the SYM-H index. These uncertainties will be essential to set reliable alarm thresholds. The resulting uncertainties turn out to be sizable at the critical stages of the geomagnetic storms. Our methodology includes as well an efficient optimization of important hyper-parameters of the LSTM network and robustness tests.
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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.
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Araujo Filho, A. A., Furtado, J., Reis, J. A. A. S., & Silva, J. E. G. (2023). Thermodynamical properties of an ideal gas in a traversable wormhole. Class. Quantum Gravity, 40(24), 245001–20pp.
Abstract: In this work, we analyze the thermodynamic properties of non-interacting particles under influence of the gravitational field of a traversable wormhole. In particular, we investigate how the thermodynamic quantities are affected by the Ellis wormhole geometry, considering three different regions to our study: asymptotically far, close to the throat, and at the throat. The thermodynamic quantities turn out to depend strongly on parameter that controls the wormhole throat radius. By varying it, there exist an expressive modification in the thermodynamic state quantities, exhibiting both usual matter and dark energy-like behaviors. Finally, the interactions are regarded to the energy density and it seems to indicate that it “cures” the dark energy-like features.
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Amerio, A., Cuoco, A., & Fornengo, N. (2023). Extracting the gamma-ray source-count distribution below the Fermi-LAT detection limit with deep learning. J. Cosmol. Astropart. Phys., 09(9), 029–39pp.
Abstract: We reconstruct the extra-galactic gamma-ray source-count distribution, or dN/dS, of resolved and unresolved sources by adopting machine learning techniques. Specifically, we train a convolutional neural network on synthetic 2-dimensional sky-maps, which are built by varying parameters of underlying source-counts models and incorporate the FermiLAT instrumental response functions. The trained neural network is then applied to the Fermi-LAT data, from which we estimate the source count distribution down to flux levels a factor of 50 below the Fermi-LAT threshold. We perform our analysis using 14 years of data collected in the (1, 10) GeV energy range. The results we obtain show a source count distribution which, in the resolved regime, is in excellent agreement with the one derived from cataloged sources, and then extends as dN/dS " S-2 in the unresolved regime, down to fluxes of 5 center dot 10-12 cm-2 s-1. The neural network architecture and the devised methodology have the flexibility to enable future analyses to study the energy dependence of the source-count distribution.
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LHCb Collaboration(Aaij, R. et al), Jaimes Elles, S. J., Jashal, B. K., Martinez-Vidal, F., Oyanguren, A., Rebollo De Miguel, M., et al. (2023). Evidence for the decays B0 → (D)over-bar(*)0 φ and updated measurements of the branching fractions of the Bs0 → (D)over-bar(*)0 φ decays. J. High Energy Phys., 10(10), 123–26pp.
Abstract: Evidence for the decays B-0 -> (D) over bar (0)phi and B-0 -> (D) over bar (*0) phi is reported with a significance of 3.6 sigma and 4.3 sigma, respectively. The analysis employs pp collision data at centre-of-mass energies root s = 7, 8 and 13TeV collected by the LHCb detector and corresponding to an integrated luminosity of 9 fb(-1). The branching fractions are measured to be B(B-0 -> (D) over bar (0)phi) = (7.7 +/- 2.1 +/- 0.7 +/- 0.7) x 10(-7), B(B-0 -> (D) over bar (*0)phi) = (2.2 +/- 05 +/- 0.2 +/- 0.2) x 10(-6). In these results, the first uncertainty is statistical, the second systematic, and the third is related to the branching fraction of the B-0 -> (D) over bar K-0(+) K- decay, used for normalisation. By combining the branching fractions of the decays B-0 -> (D) over bar ((*)0)phi and B-0 -> (D) over bar ((*)0)omega, the omega-phi mixing angle delta is constrained to be tan(2)delta = (3.6 +/- 0.7 +/- 0.4) x 10(-3), where the first uncertainty is statistical and the second systematic. An updated measurement of the branching fractions of the B-s(0) -> (D) over bar ((*)0).phi decays, which can be used to determine the CKM angle gamma, leads to B(B-s(0) -> (D) over bar (0)phi) = (2.30 +/- 0.10 +/- 0.11 +/- 0.20) x 10(-5), B(B-s(0) -> (D) over bar (*0)phi) = (3.17 +/- 0.16 +/- 0.17 +/- 0.27) x 10(-5).
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