Reliable predictions of (anti)neutrino spectra and luminosities are essential for assessing the feasibility of detecting pre-supernova neutrinos. Using the stellar evolution code MESA, we calculate the (anti)neutrino spectra and luminosities under realistic conditions of temperature, density, and electron fraction. Our study includes (anti)neutrinos produced by both thermal processes and nuclear weak-interaction reactions. By comparing the results of the thermal quasiparticle random-phase approximation with the standard technique based on the effective Q-value method, we investigate how thermal effects influence the spectra and luminosities of emitted (anti)neutrinos. Our findings show that a thermodynamically consistent treatment of Gamow–Teller transitions in hot nuclei enhances both the energy luminosity and the average energies of the emitted (anti)neutrinos.

The escalating demand for data processing in particle physics research has spurred the exploration of novel technologies to enhance the efficiency and speed of calculations. This study presents the development of an implementation of MADGRAPH, a widely used tool in particle collision simulations, to Field Programmable Gate Array (FPGA) using High-Level Synthesis (HLS). This research presents a proof of concept limited to a single, relatively simple process e+e−→μ+μ−. The experimental evaluation methodology is described, focusing on performance comparison between traditional CPU implementations, GPU acceleration, and the new FPGA approach. This study describes the complex process of adapting MADGRAPH to FPGA using HLS, focusing on optimizing algorithms for parallel processing. These advancements could enable faster execution of complex simulations, highlighting FPGA’s crucial role in advancing particle physics research. The encouraging results obtained in this proof of concept prove potential interest in testing the performance of the FPGA implementation of more complex processes.

The civil engineering industry faces a critical need for innovative non-destructive evaluation methods, particularly for ageing critical infrastructure, such as bridges, where current techniques fall short. Muography, a non-invasive imaging technique, constructs three-dimensional density maps by detecting the interactions of naturally occurring cosmic-ray muons within the scanned volume. Cosmic-ray muons offer both deep penetration capabilities due to their high momenta and inherent safety due to their natural source. However, the technology’s reliance on this natural source results in a constrained muon flux, leading to prolonged acquisition times, noisy reconstructions, and challenges in image interpretation. To address these limitations, we developed a two-model deep learning approach. First, we employed a conditional Wasserstein Generative Adversarial Network with Gradient Penalty (cWGAN-GP) to perform predictive upsampling of undersampled muography images. Using the Structural Similarity Index Measure (SSIM), 1-day sampled images were able to match the perceptual qualities of a 21-day image, while the Peak Signal-to-Noise Ratio (PSNR) indicated a noise improvement to that of 31 days worth of sampling. A second cWGAN-GP model, trained for semantic segmentation, was developed to quantitatively assess the upsampling model’s impact on each of the features within the concrete samples. This model was able to achieve segmentation of rebar grids and tendon ducts embedded in the concrete, with respective Dice–Sørensen accuracy coefficients of 0.8174 and 0.8663. This model also revealed an unexpected capability to mitigate—and in some cases entirely remove—z-plane smearing artifacts caused by the muography’s inherent inverse imaging problem. Both models were trained on a comprehensive dataset generated through Geant4 Monte Carlo simulations designed to reflect realistic civil infrastructure scenarios. Our results demonstrate significant improvements in both acquisition speed and image quality, marking a substantial step toward making muography more practical for reinforced concrete infrastructure monitoring applications.

The shape of a nucleus is one of its fundamental properties. We conduct a systematic investigation of shape coexistence in odd-Z nuclei from fluorine to potassium using the deformed relativistic Hartree–Bogoliubov theory in continuum. First, we present a simple argument regarding the energy differences between degenerate vacua, which can serve as a criterion for identifying candidates for shape coexistence. We then predict isotopes that exhibit shape coexistence.

We investigate the phenomenological effects of the nonholomorphic (NH) higgsino mass term, μ′, within the minimal supersymmetric standard model (MSSM) extended by a non-abelian flavor symmetry, referred to as the sNHSSM. This flavor symmetry enables a substantial reduction in the number of free parameters inherent to the MSSM, streamlining them from a large set to just eight. Our study explores the interplay between cold dark matter (CDM) relic density (ΩCDMh2) and the anomalous magnetic moment of the muon, (g−2)μ. We study correlations among the theoretical parameters that emerge from this interplay and are further constrained by experimental data such as the Higgs boson mass, B-physics observables, and the charge and color breaking minima constraints. Moreover, our findings reveal that incorporating the NH higgsino mass term opens up new regions of parameter space that were previously inaccessible.

The extraction of the Generalized Parton Distributions of the nucleons from phenomenological analyses of experimental data presents a challenging problem which is being actively studied in the literature. Due to theoretical limitations of some of the well-known channels, currently many new processes are being analyzed in the literature as potential novel probes. In this proceeding we propose to use the exclusive photoproduction of ηcγ pairs as a new channel for study of the GPDs. Our analysis shows that this process is primarily sensitive to the unpolarized gluon GPDs Hg in the Efremov-Radyushkin-Brodsky-Lepage (ERBL) kinematics. The numerical estimates of the cross-section and the expected counting rates for middle-energy photoproduction experiments show that expected counting rates are sufficiently large for a dedicated experimental study at the future Electron-Ion Collider (EIC) or in ultraperipheral collisions at the LHC. The total (integrated) photoproduction cross-section σtotγp→γηcp in this kinematics scales with energy W as σtotγp→γηcpW,Mγηc≥3.5GeV≈0.48pbW100GeV0.75, and yields a few thousands of events per 100fb−1 of the integrated luminosity.

The ICARUS-T600 liquid argon time projection chamber detector takes data at a shallow depth as the far detector of the Short Baseline Neutrino program at Fermilab, searching for sterile neutrinos with the Booster and Main Injector neutrino beams. The ICARUS trigger system exploits the temporal coincidence of the beams with scintillation light signals detected by 360 photo-multiplier tubes in limited TPC regions. The trigger efficiency measurement leverages cosmic rays collected without any scintillation light requirement, with timing from an external cosmic ray tagger system. The efficiency measured with stopping muons roughly saturates at Eμ∼300 MeV, covering most of the expected energy range of charged-current neutrino interactions. For the latest ICARUS physics runs, special “adder” boards performing the analog sum of light signals were introduced as a complementary trigger to possibly recover low-energy neutrino interactions.

Muon collisions are considered a promising means for exploring the energy frontier, leading to a detailed study of the possible feasibility challenges. Beam intensities of the order of 1012 muons per bunch are needed to achieve the necessary luminosity, generating a high flux of secondary and tertiary particles from muons decay that reach both the machine elements and the detector region. To limit the impact of this background on the physics performance, tungsten shieldings have been studied. A machine learning-based approach to the geometry optimization of these shieldings will be discussed.

Although the gravitational interaction between matter and antimatter has been the subject of theoretical speculation since the discovery of the latter in 1928, only recently was the ALPHA experiment at CERN able to observe, for the first time, the effects of gravity on antimatter atoms, namely on antihydrogen. After an introduction of the concept of antimatter, along with its still-unresolved mysteries, details about how antihydrogen is produced at the Antimatter Factory at CERN will be given. Finally, the measurement of the acceleration of gravity of antihydrogen atoms falling in the Earth’s gravitational field will be described.

In high-energy physics, resistive plate chamber (RPC) detectors operating in avalanche mode make use of a high-performance gas mixture. Its main component, Tetrafluoroethane (C2H2F4), is classified as a fluorinated greenhouse gas. The RPC EcoGas@GIF++ collaboration is pursuing an intensive R&D on new gas mixtures for RPCs to explore eco-friendly alternatives complying with recent European regulations. The performance of different RPC detectors has been evaluated at the CERN Gamma Irradiation Facility with Tetrafluoropropene (C3H2F4)-CO2-based gas mixtures. A long-term ageing test campaign was launched in 2022, and since 2023, systematic long-term performance studies have been carried out thanks to dedicated beam tests. The results of these studies are discussed together with their future perspectives.