Abstract: We use the latest constraints on the population of stellar origin binary black holes (SOBBH) from LIGO/Virgo/KAGRA (LVK) observations, to estimate the stochastic gravi-tational wave background (SGWB) they generate in the frequency band of LISA. In order to account for the faint and distant binaries, which contribute the most to the SGWB, we extend the merger rate at high redshift assuming that it tracks the star formation rate. We adopt different methods to compute the SGWB signal: we perform an analytical evaluation, we use Monte Carlo sums over the SOBBH population realisations, and we account for the role of the detector by simulating LISA data and iteratively removing the resolvable signals until only the confusion noise is left. The last method allows the extraction of both the expected SGWB and the number of resolvable SOBBHs. Since the latter are few for signal-to-noise ratio thresholds larger than five, we confirm that the spectral shape of the SGWB in the LISA band agrees with the analytical prediction of a single power law. We infer the probability dis-tribution of the SGWB amplitude from the LVK GWTC-3 posterior of the binary population model: at the reference frequency of 0.003 Hz it has an interquartile range of h(2 Omega)GW(f = 3 x 10(-3) Hz) is an element of [5.65, 11.5] x 10(-13), in agreement with most previous estimates. We then perform a MC analysis to assess LISA's capability to detect and characterise this signal. Ac-counting for both the instrumental noise and the galactic binaries foreground, with four years of data, LISA will be able to detect the SOBBH SGWB with percent accuracy, narrowing down the uncertainty on the amplitude by one order of magnitude with respect to the range of possible amplitudes inferred from the population model. A measurement of this signal by LISA will help to break the degeneracy among some of the population parameters, and pro-vide interesting constraints, in particular on the redshift evolution of the SOBBH merger rate.

Abstract: A neural network for software compensation was developed for the highly granular CALICE Analogue Hadronic Calorimeter (AHCAL). The neural network uses spatial and temporal event information from the AHCAL and energy information, which is expected to improve sensitivity to shower development and the neutron fraction of the hadron shower. The neural network method produced a depth-dependent energy weighting and a time-dependent threshold for enhancing energy deposits consistent with the timescale of evaporation neutrons. Additionally, it was observed to learn an energy-weighting indicative of longitudinal leakage correction. In addition, the method produced a linear detector response and outperformed a published control method regarding resolution for every particle energy studied.

Abstract: Metalenses are composed of nanostructures for focusing light and have been widely explored in many exciting applications. However, their expanding dimensions pose simulation challenges. We propose a method to simulate metalenses in a timely manner using vectorial wave and ray tracing models. We sample the metalens's radial phase gradient and locally approximate the phase profile by a linear phase response. Each sampling point is modeled as a binary blazed grating, employing the chosen nanostructure, to build a transfer function set. The metalens transmission or reflection is then obtained by applying the corresponding transfer function to the incoming field on the regions surrounding each sampling point. Fourier optics is used to calculate the scattered fields under arbitrary illumination for the vectorial wave method, and a Monte Carlo algorithm is used in the ray tracing formalism. We validated our method against finite -difference time domain simulations at 632 nm, and we were able to simulate metalenses larger than 3000 wavelengths in diameter on a personal computer.

Abstract: The MoEDAL's Apparatus for Penetrating Particles (MAPP) Experiment is designed to expand the search for new physics at the LHC, significantly extending the physics program of the baseline MoEDAL Experiment. The Phase-1 MAPP detector (MAPP-1) is currently undergoing installation at the LHC's UA83 gallery adjacent to the LHCb/MoEDAL region at Interaction Point 8 and will begin data-taking in early 2024. The focus of the MAPP experiment is on the quest for new feebly interacting particles – avatars of new physics with extremely small Standard Model couplings, such as minicharged particles (mCPs). In this study, we present the results of a comprehensive analysis of MAPP-1's sensitivity to mCPs arising in the canonical model involving the kinetic mixing of a massless dark U(1) gauge field with the Standard Model hypercharge gauge field. We focus on several dominant production mechanisms of mCPs at the LHC across the mass-mixing parameter space of interest to MAPP: Drell-Yan pair production, direct decays of heavy quarkonia and light vector mesons, and single Dalitz decays of pseudoscalar mesons. The 95% confidence level background-free sensitivity of MAPP-1 for mCPs produced at the LHC's Run 3 and the HL-LHC through these mechanisms, along with projected constraints on the minicharged strongly interacting dark matter window, are reported. Our results indicate that MAPP-1 exhibits sensitivity to sizable regions of unconstrained parameter space and can probe effective charges as low as 8 x 10 -4 e and 6 x 10 -4 e for Run 3 and the HL-LHC, respectively.

Abstract: Asearch for the associated production of a heavy resonance with a top-quark or a top-antitop-quark pair, and decaying into a t (t) over bar pair is presented. The search uses the vdata recorded by the ATLAS detector in pp collisions at root s = 13 TeV at the Large Hadron Collider during the years 2015-2018, corresponding to an integrated luminosity of 139 fb(-1). Events containing exactly one electron ormuon are selected. The two hadronically decaying top quarks from the resonance decay are reconstructed using jets clustered with a large radius parameter of R = 1. The invariant mass spectrum of the two top quark candidates is used to search for a resonance signal in the range of 1.0 TeV to 3.2 TeV. The presence of a signal is examined using an approach with minimal model dependence followed by a model-dependent interpretation. No significant excess is observed over the background expectation. Upper limits on the production cross section times branching ratio at 95% confidence level are provided for a heavy Z' boson based on a simplified model, for Z' mass between 1.0 TeV and 3.0 TeV. The observed (expected) limits range from 21 (14) fb to 119 (86) fb depending on the choice of model parameters.