Abstract: The reconstruction of photons in the ATLAS detector is studied with data taken during the 2004 Combined Test Beam, where a full slice of the ATLAS detector was exposed to beams of particles of known energy at the CERN SPS. The results presented show significant differences in the longitudinal development of the electromagnetic shower between converted and unconverted photons as well as in the total measured energy. The potential to use the reconstructed converted photons as a means to precisely map the material of the tracker in front of the electromagnetic calorimeter is also considered. All results obtained are compared with a detailed Monte-Carlo simulation of the test-beam setup which is based on the same simulation and reconstruction tools as those used for the ATLAS detector itself.

Abstract: We present a new way to interpret Top Standard Model measurements going beyond the SMEFT framework. Instead of the usual paradigm in Top EFT, where the main effects come from tails in momenta distributions, we propose an interpretation in terms of new physics which only shows up at loop-level. The effects of these new states, which can be lighter than required within the SMEFT, appear as distinctive structures at high momenta, but may be suppressed at the tails of distributions. As an illustration of this phenomena, we present the explicit case of a UV model with a Z \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$ \mathcal{Z} $$\end{document} 2 symmetry, including a Dark Matter candidate and a top-partner. This simple UV model reproduces the main features of this class of signatures, particularly a momentum-dependent form factor with more structure than the SMEFT. As the new states can be lighter than in SMEFT, we explore the interplay between the reinterpretation of direct searches for colored states and Dark Matter, and Top measurements, made by ATLAS and CMS in the differential t t over bar \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$ t\overline{t} $$\end{document} final state. We also compare our method with what one would expect using the SMEFT reinterpretation, finding that using the full loop information provides a better discriminating power.

Abstract: We present a novel methodology based on Physics-Informed Neural Networks (PINNs) for solving systems of partial differential equations admitting discontinuous solutions. Our method, called Gradient-Annihilated PINNs (GA-PINNs), introduces a modified loss function that forces the model to partially ignore high-gradients in the physical variables, achieved by introducing a suitable weighting function. The method relies on a set of hyperparameters that control how gradients are treated in the physical loss. The performance of our methodology is demonstrated by solving Riemann problems in special relativistic hydrodynamics, extending earlier studies with PINNs in the context of the classical Euler equations. The solutions obtained with the GA-PINN model correctly describe the propagation speeds of discontinuities and sharply capture the associated jumps. We use the relative l(2) error to compare our results with the exact solution of special relativistic Riemann problems, used as the reference ''ground truth'', and with the corresponding error obtained with a second-order, central, shock-capturing scheme. In all problems investigated, the accuracy reached by the GA-PINN model is comparable to that obtained with a shock-capturing scheme, achieving a performance superior to that of the baseline PINN algorithm in general. An additional benefit worth stressing is that our PINN-based approach sidesteps the costly recovery of the primitive variables from the state vector of conserved variables, a well-known drawback of grid-based solutions of the relativistic hydrodynamics equations. Due to its inherent generality and its ability to handle steep gradients, the GA-PINN methodology discussed in this paper could be a valuable tool to model relativistic flows in astrophysics and particle physics, characterized by the prevalence of discontinuous solutions.

Abstract: In 2004 at the ATLAS (A Toroidal LHC ApparatuS) combined test beam, one slice of the ATLAS barrel detector (including an Inner Detector set-up and the Liquid Argon calorimeter) was exposed to particles from the H8 SPS beam line at CERN. It was the first occasion to test the combined electron performance of ATLAS. This paper presents results obtained for the momentum measurement p with the Inner Detector and for the performance of the electron measurement with the LAr calorimeter (energy E linearity and resolution) in the presence of a magnetic field in the Inner Detector for momenta ranging from 20 GeV/c to 100 GeV/c. Furthermore the particle identification capabilities of the Transition Radiation Tracker, Bremsstrahlungs-recovery algorithms relying on the LAr calorimeter and results obtained for the E/p ratio and a way how to extract scale parameters will be discussed.

Abstract: Solar neutrinos induce elastic neutrino-electron scattering in dark matter direct detection experiments, resulting in detectable event rates at current facilities. We analyze recent data from the XENONnT, LUX-ZEPLIN, and PandaX-4T experiments and we derive stringent constraints on several U(1) ' extensions of the Standard Model, accommodating new neutrino-electron interactions. We provide bounds on the relevant coupling and mass of light vector mediators for a variety of models, including the anomaly-free B – L model, lepton flavor-dependent interactions like L alpha – L beta , B – 2L e – L mu,tau , B – 3L alpha , and B + 2L μ+ 2L tau models. We compare our results with other limits obtained in the literature from both terrestrial and astrophysical experiments. Finally, we present forecasts for improving current bounds with a future experiment like DARWIN.