Abstract: Context. Astronomical imaging aims to maximize signal capture while minimizing noise. It is difficult and expensive to enhance the signal-to-noise ratio directly on detectors, which has led to extensive research into advanced post-processing techniques. Aims. Removing background noise from images is a valuable preprocessing step for catalog-building tasks. We introduce BGRem, a machine-learning (ML)-based tool to remove background noise from astronomical images. Our aim is to improve image quality and enhance the performance of the subsequent analysis pipeline, from detecting faint sources to performing source characterization tasks. Methods. The BGRem tool uses a diffusion-based model with an attention U-Net as backbone, trained on simulated images for optical and gamma (gamma)-ray data from the MeerLICHT and Fermi-LAT telescopes. The tool learns to denoise astronomical images in a supervised manner over several diffusion steps. We performed preprocessing and postprocessing techniques, including normalization and median subtraction, on these images to make them suitable for the analysis pipeline. Results. We compared the performance of BGRem with SourceExtractor (SExtractor), a widely used tool for cataloging astronomical sources. The number of true positive sources using SExtractor increased by about 7% for MeerLICHT data when we used BGRem as a preprocessing step. We also show the generalizability of BGRem by testing it with optical images from different telescopes and on simulated gamma-ray data representative of the Fermi-LAT telescope. In both cases, BGRem improves the source detection efficiency. Conclusions. The BGRem tool improves the source detection accuracy of traditional pixel-based methods by removing complex background noise. Using zero-shot approach, BGRem generalizes well to a wide range of optical images. The successful application of BGRem to simulated gamma-ray images, alongside optical data, demonstrates its adaptability to distinct noise characteristics and observational domains. This cross-wavelength performance highlights its potential as a general-purpose background removal framework for multiwavelength astronomical surveys.

Abstract: We investigate the impact of a nonstandard electron mass me on early-Universe thermal history, focusing on neutrino decoupling and big bang nucleosynthesis (BBN). In the standard cosmology, neutrino-electron interactions keep neutrinos in thermal contact with the electromagnetic plasma until shortly before et annihilation. Varying me shifts the decoupling epoch and the entropy transfer from et annihilation, thereby modifying the neutrino energy density and the inferred effective number of relativistic species, Neff. Independently, during BBN, the rates of charged-current weak processes, and hence the neutron-to-proton ratio, depend on me. By confronting BBN predictions for the primordial light-element abundances with observations and imposing cosmological constraints on Neff, we obtain the following 1 sigma bounds on me in the early Universe: me = 0.505+0.006 -0.007 MeV (for the NACRE II nuclear reaction network) or -0.004 MeV (for the PRIMAT nuclear reaction network). These bounds have been derived by adopting the recent determination of the primordial helium-4 abundance by the Large Binocular Telescope observations of 54 metal-poor H II regions. If instead we adopt the Particle Data Book helium-4 abundance, the bounds are me = 0.503 & thorn;0.011 -0.015 MeV (NACRE II) or me = 0.521 & thorn;0.009 obtained allowed ranges are close to the present laboratory value at the level of similar to 0.4%-2%, depending on the dataset and nuclear network, thus supporting the constancy of the electron mass over cosmological timescales.

Abstract: Complete isotopic fission yields distributions of 240Pu have been measured as a function of the initial excitation energy. The 240Pu fissioning system was produced through a two-proton transfer reaction between a 238U beam and a 12C target. The reaction was measured in inverse kinematics at Coulomb barrier energies, allowing for the full distribution of fission fragments to be isotopically identified with the VAMOS + + Spectrometer. The excitation energy of the system was measured on an event-by-event basis by detecting the targetlike recoil 10Be in a segmented silicon telescope. This manuscript reports on the evolution of the fission yields as a function of the excitation energy of the system between 8.2 to 11.9 MeV. The influence of the excitation energy is manifested in the damping of shell effects that feed the yields in the symmetry valley, as well as in a reduction of the neutron content of the fragments. This reduction, however, is observed only in the heavy fragment, while the neutron content of the light fragment remains unaffected. The comparison with previous measurements, models, and evaluations highlights the importance of correlated observables for improving fission models.