Abstract: Aims. Traditional star-galaxy classification techniques often rely on feature estimation from catalogs, a process susceptible to introducing inaccuracies, thereby potentially jeopardizing the classification's reliability. Certain galaxies, especially those not manifesting as extended sources, can be misclassified when their shape parameters and flux solely drive the inference. We aim to create a robust and accurate classification network for identifying stars and galaxies directly from astronomical images.Methods. The AutoSourceID-Classifier (ASID-C) algorithm developed for this work uses 32x32 pixel single filter band source cutouts generated by the previously developed AutoSourceID-Light (ASID-L) code. By leveraging convolutional neural networks (CNN) and additional information about the source position within the full-field image, ASID-C aims to accurately classify all stars and galaxies within a survey. Subsequently, we employed a modified Platt scaling calibration for the output of the CNN, ensuring that the derived probabilities were effectively calibrated, delivering precise and reliable results.Results. We show that ASID-C, trained on MeerLICHT telescope images and using the Dark Energy Camera Legacy Survey (DECaLS) morphological classification, is a robust classifier and outperforms similar codes such as SourceExtractor. To facilitate a rigorous comparison, we also trained an eXtreme Gradient Boosting (XGBoost) model on tabular features extracted by SourceExtractor. While this XGBoost model approaches ASID-C in performance metrics, it does not offer the computational efficiency and reduced error propagation inherent in ASID-C's direct image-based classification approach. ASID-C excels in low signal-to-noise ratio and crowded scenarios, potentially aiding in transient host identification and advancing deep-sky astronomy.

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: Supersymmetry has often been invoked as the new physics that might reconcile the experimental muon magnetic anomaly, a(mu), with the theoretical prediction (basing the computation of the hadronic contribution on e(+)e(-) data). However, in the context of the constrained minimal supersymmetric standard model (CMSSM), the required supersymmetric contributions (which grow with decreasing supersymmetric masses) are in potential tension with a possibly large Higgs mass (which requires large stop masses). In the limit of very large m(h) supersymmetry gets decoupled, and the CMSSM must show the same discrepancy as the standard model with a(mu). But it is much less clear for which size of m(h) does the tension start to be unbearable. In this paper, we quantify this tension with the help of Bayesian techniques. We find that for m(h) >= 125 GeV the maximum level of discrepancy given the current data (similar to 3.2 sigma) is already achieved. Requiring less than 3 sigma discrepancy, implies m(h) less than or similar to 120 GeV. For a larger Higgs mass we should give up either the CMSSM model or the computation of a(mu) based on e(+)e(-); or accept living with such an inconsistency.

Abstract: The unfortunate case where the Large Hadron Collider (LHC) fails to discover physics Beyond the Standard Model (BSM) is sometimes referred to as the “Nightmare scenario” of particle physics. We study the consequences of this hypothetical scenario for Dark Matter (DM), in the framework of the constrained Minimal Supersymmetric Standard Model (cMSSM). We evaluate the surviving regions of the cMSSM parameter space after null searches at the LHC, using several different LHC configurations, and study the consequences for DM searches with ton-scale direct detectors and the IceCube neutrino telescope. We demonstrate that ton-scale direct detection experiments will be able to conclusively probe the cMSSM parameter space that would survive null searches at the LHC with 100 fb(-1) of integrated luminosity at 14TeV. We also demonstrate that IceCube (80 strings plus DeepCore) will be able to probe as much as similar or equal to 17% of the currently favoured parameter space after 5 years of observation.

Abstract: The discovery by the ATLAS and CMS experiments of a new boson with mass around 125 GeV and with measured properties compatible with those of a Standard-Model Higgs boson, coupled with the absence of discoveries of phenomena beyond the Standard Model at the TeV scale, has triggered interest in ideas for future Higgs factories. A new circular e(+)e(-) collider hosted in a 80 to 100 km tunnel, TLEP, is among the most attractive solutions proposed so far. It has a clean experimental environment, produces high luminosity for top-quark, Higgs boson, W and Z studies, accommodates multiple detectors, and can reach energies up to the threshold and beyond. It will enable measurements of the Higgs boson properties and of Electroweak Symmetry-Breaking (EWSB) parameters with unequalled precision, offering exploration of physics beyond the Standard Model in the multi-TeV range. Moreover, being the natural precursor of the VHE-LHC, a 100 TeV hadron machine in the same tunnel, it builds up a long-term vision for particle physics. Altogether, the combination of TLEP and the VHE-LHC offers, for a great cost effectiveness, the best precision and the best search reach of all options presently on the market. This paper presents a first appraisal of the salient features of the TLEP physics potential, to serve as a baseline for a more extensive design study.