Abstract: This work reports that gas chamber geometry and gas flow management substantially affect the neutron production of a repetitive fast plasma focus. The gas flow rate is the most sensitive parameter. An appropriate design of the gas chamber combined with a suitable flow-rate management can lead to improvements in the neutron production of one order of magnitude working in a fast repetitive mode.

Abstract: The decay of an isomeric state in the neutron-rich nucleus Cd-129 has been observed via the detection of internal conversion and Compton electrons providing first experimental information on excited states in this nucleus. The isomer was populated in the projectile fission of a U-238 beam at the Radioactive Isotope Beam Factory at RIKEN. From the measured yields of gamma-rays and internal conversion electrons, a multipolarity of E3 was tentatively assigned to the isomeric transition. A half-life of T-1/2 = 3.6(2) ms was determined for the new state which was assigned a spin of (21/2(+)), based on a comparison to shell model calculations performed using state-of-the-art realistic effective interactions.

Abstract: The neutron sensitivity of a cylindrical circle minus 1.5 in x 1.5 in LaBr3:Ce scintillation detector was measured using quasi-monoenergetic neutron beams in the energy range from 40 keV to 2.5 MeV. In this energy range the detector is sensitive to gamma-rays generated in neutron inelastic and capture processes. The experimental energy response was compared with Monte Carlo simulations performed with the Geant4 simulation toolkit using the so-called High Precision Neutron Models. These models rely on relevant information stored in evaluated nuclear data libraries. The performance of the Geant4 Neutron Data Library as well as several standard nuclear data libraries was investigated. In the latter case this was made possible by the use of a conversion tool that allowed the direct use of the data from other libraries in Geant4. Overall it was found that there was good agreement with experiment for some of the neutron data bases like ENDF/B-VII.0 or JENDL-3.3 but not with the others such as ENDF/B-VI.8 or JEFF-3.1.

Abstract: We present statistically convergent profile likelihood maps obtained via global fits of a phenomenological Minimal Supersymmetric Standard Model with 15 free parameters (the MSSM-15), based on over 250M points. We derive constraints on the model parameters from direct detection limits on dark matter, the Planck relic density measurement and data from accelerator searches. We provide a detailed analysis of the rich phenomenology of this model, and determine the SUSY mass spectrum and dark matter properties that are preferred by current experimental constraints. We evaluate the impact of the measurement of the anomalous magnetic moment of the muon (g – 2) on our results, and provide an analysis of scenarios in which the lightest neutralino is a subdominant component of the dark matter. The MSSM-15 parameters are relatively weakly constrained by current data sets, with the exception of the parameters related to dark matter phenomenology (M-1, M-2, mu), which are restricted to the sub-TeV regime, mainly due to the relic density constraint. The mass of the lightest neutralino is found to be < 1.5TeV at 99% C.L., but can extend up to 3 TeV when excluding the g – 2 constraint from the analysis. Low-mass bino-like neutralinos are strongly favoured, with spin-independent scattering cross-sections extending to very small values, similar to 10(-20) pb. ATLAS SUSY null searches strongly impact on this mass range, and thus rule out a region of parameter space that is outside the reach of any current or future direct detection experiment. The best-fit point obtained after inclusion of all data corresponds to a squark mass of 2.3 TeV, a gluino mass of 2.1 TeV and a 130 GeV neutralino with a spin-independent cross-section of 2.4 x 10(-10) pb, which is within the reach of future multi-ton scale direct detection experiments and of the upcoming LHC run at increased centre-of-mass energy.

Abstract: Aims. In astronomy, machine learning has been successful in various tasks such as source localisation, classification, anomaly detection, and segmentation. However, feature regression remains an area with room for improvement. We aim to design a network that can accurately estimate sources' features and their uncertainties from single-band image cutouts, given the approximated locations of the sources provided by the previously developed code AutoSourceID-Light (ASID-L) or other external catalogues. This work serves as a proof of concept, showing the potential of machine learning in estimating astronomical features when trained on meticulously crafted synthetic images and subsequently applied to real astronomical data.Methods. The algorithm presented here, AutoSourceID-FeatureExtractor (ASID-FE), uses single-band cutouts of 32x32 pixels around the localised sources to estimate flux, sub-pixel centre coordinates, and their uncertainties. ASID-FE employs a two-step mean variance estimation (TS-MVE) approach to first estimate the features and then their uncertainties without the need for additional information, for example the point spread function (PSF). For this proof of concept, we generated a synthetic dataset comprising only point sources directly derived from real images, ensuring a controlled yet authentic testing environment.Results. We show that ASID-FE, trained on synthetic images derived from the MeerLICHT telescope, can predict more accurate features with respect to similar codes such as SourceExtractor and that the two-step method can estimate well-calibrated uncertainties that are better behaved compared to similar methods that use deep ensembles of simple MVE networks. Finally, we evaluate the model on real images from the MeerLICHT telescope and the Zwicky Transient Facility (ZTF) to test its transfer learning abilities.