Abstract: This letter documents a search for flavour-changing neutral currents (FCNCs), which are strongly sup-pressed in the Standard Model, in events with a photon and a top quark with the ATLAS detector. The analysis uses data collected in pp collisions at & RADIC;s =13 TeV during Run 2 of the LHC, corresponding to an integrated luminosity of 139 fb-1. Both FCNC top-quark production and decay are considered. The final state consists of a charged lepton, missing transverse momentum, a b-tagged jet, one high-momentum photon and possibly additional jets. A multiclass deep neural network is used to classify events either as signal in one of the two categories, FCNC production or decay, or as background. No significant ex-cess of events over the background prediction is observed and 95% CL upper limits are placed on the strength of left-and right-handed FCNC interactions. The 95% CL bounds on the branching fractions for the FCNC top-quark decays, estimated (expected) from both top-quark production and decay, are B(t & RARR; u & gamma; ) < 0.85 (0.88+0.37 -0.25) x 10-5 and B(t & RARR; c & gamma; ) < 4.2 (3.40+1.35-0.95) x 10-5 for a left-handed tq & gamma; cou-pling, and B(t & RARR; u & gamma; ) < 1.2 (1.20+0.50 -0.33) x10-5 and B(t & RARR; c & gamma; ) < 4.5 (3.70+1.47 -1.03) x10-5 for a right-handed coupling.

Abstract: We have studied the meson-baryon interaction in the neutral S = -2 sector using an extended Unitarized Chiral Perturbation Theory, which takes into account not only the leading Weinberg-Tomozawa term (as all the previous studies in S = -2 sector), but also the Born terms and next-to-leading order contribution. Based on the SU(3) symmetry of the chiral Lagrangian we took most of the model parameters from the BCN model [1], where these were fitted to a large amount of experimental data in the neutral S = -1 sector. We have shown that our approach is able to generate dynamically both Xi(1620) and Xi(1690) states in very reasonable agreement with the data, and can naturally explain the puzzle with the decay branching ratios of Xi(1690). Our results clearly illustrate the reliability of chiral models implementing unitarization in coupled channels and the importance of considering Born and NLO contributions for precise calculations.

Abstract: We reexamine the regularization of the two-point function of a scalar field in a Friedmann-Lemaitre-Robertson-Walker (FLRW) spacetime. Adiabatic regularization provides a set of subtraction terms in momentum space that successfully remove its ultraviolet divergences at coincident points, but can significantly distort the power spectrum at infrared scales, especially for light fields. In this work we propose, by using the intrinsic ambiguities of the renormalization program, a new set of subtraction terms that minimize the distortions for scales k less than or similar to M, with M an arbitrary mass scale. Our method is consistent with local covariance and equivalent to general regularization methods in curved spacetime. We apply our results to the regularization of the power spectrum in de Sitter space: while the adiabatic scheme yields exactly Delta((reg))(phi) = 0 for a massless field, our proposed prescription recovers the standard scale-invariant result Delta((reg))(phi) similar or equal to H-2/(4 pi(2)) at super-horizon scales.

Abstract: In this work, we study the thermodynamic properties on a non-commutative background via gravitational gauge field potentials. This procedure is accomplished after contracting de Sitter (dS) group, SO(4, 1), with the Poincare group, ISO(3, 1). Particularly, we focus on a static spherically symmetric black hole. In this manner, we calculate the modified Hawking temperature and the other deformed thermal state quantities, namely, entropy, heat capacity, Helmholtz free energy and pressure. Finally, we also investigate the black hole evaporation process in such a context.

Abstract: Aims. In astronomy, machine learning has been successful in various tasks such as source localisation, classification, anomaly detection, and segmentation. However, feature regression remains an area with room for improvement. We aim to design a network that can accurately estimate sources' features and their uncertainties from single-band image cutouts, given the approximated locations of the sources provided by the previously developed code AutoSourceID-Light (ASID-L) or other external catalogues. This work serves as a proof of concept, showing the potential of machine learning in estimating astronomical features when trained on meticulously crafted synthetic images and subsequently applied to real astronomical data.Methods. The algorithm presented here, AutoSourceID-FeatureExtractor (ASID-FE), uses single-band cutouts of 32x32 pixels around the localised sources to estimate flux, sub-pixel centre coordinates, and their uncertainties. ASID-FE employs a two-step mean variance estimation (TS-MVE) approach to first estimate the features and then their uncertainties without the need for additional information, for example the point spread function (PSF). For this proof of concept, we generated a synthetic dataset comprising only point sources directly derived from real images, ensuring a controlled yet authentic testing environment.Results. We show that ASID-FE, trained on synthetic images derived from the MeerLICHT telescope, can predict more accurate features with respect to similar codes such as SourceExtractor and that the two-step method can estimate well-calibrated uncertainties that are better behaved compared to similar methods that use deep ensembles of simple MVE networks. Finally, we evaluate the model on real images from the MeerLICHT telescope and the Zwicky Transient Facility (ZTF) to test its transfer learning abilities.