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Yokoyama, R., Singh, M., Grzywacz, R., Keeler, A., King, T. T., Agramunt, J., et al. (2019). Segmented YSO scintillation detectors as a new beta-implant detection tool for decay spectroscopy in fragmentation facilities. Nucl. Instrum. Methods Phys. Res. A, 937, 93–97.
Abstract: A newly developed segmented YSO scintillator detector was implemented for the first time at the RI-beam Factory at RIKEN Nishina Center as an implantation-decay counter. The results from the experiment demonstrate that the detector is a viable alternative to conventional silicon-strip detectors with its good timing resolution and high detection efficiency for beta particles. A Position-Sensitive Photo-Multiplier Tube (PSPMT) is coupled with a 48 x 48 segmented YSO crystal. To demonstrate its capabilities, a known short-lived isomer in Ni-76 and the beta decay of Co-74 were measured by implanting those ions into the YSO detector. The half-lives and gamma-rays observed in this work are consistent with the known values. The beta-ray detection efficiency is more than 80 % for the decay of Co-74.
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Kiss, G. G. et al, Tarifeño-Saldivia, A., Tain, J. L., Agramunt, J., Algora, A., Domingo-Pardo, C., et al. (2022). Measuring the beta-decay Properties of Neutron-rich Exotic Pm, Sm, Eu, and Gd Isotopes to Constrain the Nucleosynthesis Yields in the Rare-earth Region. Astrophys. J., 936(2), 107–18pp.
Abstract: The beta-delayed neutron-emission probabilities of 28 exotic neutron-rich isotopes of Pm, Sm, Eu, and Gd were measured for the first time at RIKEN Nishina Center using the Advanced Implantation Detector Array (AIDA) and the BRIKEN neutron detector array. The existing beta-decay half-life (T (1/2)) database was significantly increased toward more neutron-rich isotopes, and uncertainties for previously measured values were decreased. The new data not only constrain the theoretical predictions of half-lives and beta-delayed neutron-emission probabilities, but also allow for probing the mechanisms of formation of the high-mass wing of the rare-earth peak located at A approximate to 160 in the r-process abundance distribution through astrophysical reaction network calculations. An uncertainty quantification of the calculated abundance patterns with the new data shows a reduction of the uncertainty in the rare-earth peak region. The newly introduced variance-based sensitivity analysis method offers valuable insight into the influence of important nuclear physics inputs on the calculated abundance patterns. The analysis has identified the half-lives of Sm-168 and of several gadolinium isotopes as some of the key variables among the current experimental data to understand the remaining abundance uncertainty at A = 167-172.
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HAWC Collaboration(Albert, A. et al), & Salesa Greus, F. (2022). Constraints on the Very High Energy Gamma-Ray Emission from Short GRBs with HAWC. Astrophys. J., 936(2), 126–14pp.
Abstract: Many gamma-ray bursts (GRBs) have been observed from radio wavelengths, and a few at very high energies (VHEs, >100 GeV). The High Altitude Water Cherenkov (HAWC) gamma-ray observatory is well suited to study transient phenomena at VHEs owing to its large field of view and duty cycle. These features allow for searches of VHE emission and can probe different model assumptions of duration and spectra. In this paper, we use data collected by HAWC between 2014 December and 2020 May to search for emission in the energy range from 80 to 800 GeV coming from a sample of 47 short GRBs that triggered the Fermi, Swift, and Konus satellites during this period. This analysis is optimized to search for delayed and extended VHE emission within the first 20 s of each burst. We find no evidence of VHE emission, either simultaneous or delayed, with respect to the prompt emission. Upper limits (90% confidence level) derived on the GRB fluence are used to constrain the synchrotron self-Compton forward-shock model. Constraints for the interstellar density as low as 10(-2) cm(-3) are obtained when assuming z = 0.3 for bursts with the highest keV fluences such as GRB 170206A and GRB 181222841. Such a low density makes observing VHE emission mainly from the fast-cooling regime challenging.
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Capra, S., Mengoni, D., Dueñas, J. A., John, P. R., Gadea, A., Aliaga, R. J., et al. (2019). Performance of the new integrated front-end electronics of the TRACE array commissioned with an early silicon detector prototype. Nucl. Instrum. Methods Phys. Res. A, 935, 178–184.
Abstract: The spectroscopic performances of the new integrated ASIC (Application-Specific Integrated Circuit) preamplifiers for highly segmented silicon detectors have been evaluated with an early silicon detector prototype of the TRacking Array for light Charged Ejectiles (TRACE). The ASICS were mounted on a custom-designed PCB (Printed Circuit Board) and the detector plugged on it. Energy resolution tests, performed on the same detector before and after irradiation, yielded a resolution of 21 keV and 33 keV FWHM respectively. The output signals were acquired with an array of commercial 100-MHz 14-bit digitizers. The preamplifier chip is equipped with an innovative Fast-Reset device that has two functions: it reduces dramatically the dead time of the preamplifier in case of saturation (from milliseconds to microseconds) and extends the spectroscopic dynamic range of the preamplifier by more than one order of magnitude. Other key points of the device are the low noise and the wide bandwidth.
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Villanueva-Domingo, P., Villaescusa-Navarro, F., Angles-Alcazar, D., Genel, S., Marinacci, F., Spergel, D. N., et al. (2022). Inferring Halo Masses with Graph Neural Networks. Astrophys. J., 935(1), 30–15pp.
Abstract: Understanding the halo-galaxy connection is fundamental in order to improve our knowledge on the nature and properties of dark matter. In this work, we build a model that infers the mass of a halo given the positions, velocities, stellar masses, and radii of the galaxies it hosts. In order to capture information from correlations among galaxy properties and their phase space, we use Graph Neural Networks (GNNs), which are designed to work with irregular and sparse data. We train our models on galaxies from more than 2000 state-of-the-art simulations from the Cosmology and Astrophysics with MachinE Learning Simulations project. Our model, which accounts for cosmological and astrophysical uncertainties, is able to constrain the masses of the halos with a similar to 0.2 dex accuracy. Furthermore, a GNN trained on a suite of simulations is able to preserve part of its accuracy when tested on simulations run with a different code that utilizes a distinct subgrid physics model, showing the robustness of our method. The PyTorch Geometric implementation of the GNN is publicly available on GitHub (https://github.com/PabloVD/HaloGraphNet).
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