Guadilla, V. et al, Algora, A., Tain, J. L., Agramunt, J., Jordan, D., Monserrate, M., et al. (2017). Characterization of a cylindrical plastic beta-detector with Monte Carlo simulations of optical photons. Nucl. Instrum. Methods Phys. Res. A, 854, 134–138.
Abstract: In this work we report on the Monte Carlo study performed to understand and reproduce experimental measurements of a new plastic beta-detector with cylindrical geometry. Since energy deposition simulations differ from the experimental measurements for such a geometry, we show how the simulation of production and transport of optical photons does allow one to obtain the shapes of the experimental spectra. Moreover, taking into account the computational effort associated with this kind of simulation, we develop a method to convert the simulations of energy deposited into light collected, depending only on the interaction point in the detector. This method represents a useful solution when extensive simulations have to be done, as in the case of the calculation of the response function of the spectrometer in a total absorption gamma-ray spectroscopy analysis.
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Guadilla, V. et al, Tain, J. L., Algora, A., Agramunt, J., Gelletly, W., Jordan, D., et al. (2018). Characterization and performance of the DTAS detector. Nucl. Instrum. Methods Phys. Res. A, 910, 79–89.
Abstract: DTAS is a segmented total absorption y-ray spectrometer developed for the DESPEC experiment at FAIR. It is composed of up to eighteen NaI(Tl) crystals. In this work we study the performance of this detector with laboratory sources and also under real experimental conditions. We present a procedure to reconstruct offline the sum of the energy deposited in all the crystals of the spectrometer, which is complicated by the effect of NaI(Tl) light-yield non-proportionality. The use of a system to correct for time variations of the gain in individual detector modules, based on a light pulse generator, is demonstrated. We describe also an event-based method to evaluate the summing-pileup electronic distortion in segmented spectrometers. All of this allows a careful characterization of the detector with Monte Carlo simulations that is needed to calculate the response function for the analysis of total absorption gamma-ray spectroscopy data. Special attention was paid to the interaction of neutrons with the spectrometer, since they are a source of contamination in studies of beta-delayed neutron emitting nuclei.
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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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