Vitez-Sveiczer, A. et al, Algora, A., Morales, A. I., Rubio, B., Agramunt, J., Guadilla, V., et al. (2022). The beta-decay of Kr-70 into Br-70: Restoration of the pseudo-SU(4) symmetry. Phys. Lett. B, 830, 137123–8pp.
Abstract: The beta-decay of the even-even nucleus Kr-70 with Z=N+2, has been investigated at the Radioactive Ion Beam Factory (RIBF) of the RIKEN Nishina Center using the BigRIPS fragment separator, the ZeroDegree Spectrometer, the WAS3ABI implantation station and the EURICA HPGe cluster array. Fifteen gamma-rays associated with the beta-decay of( 70)Kr into Br-70 have been identified for the first time, defining ten populated states below E-exc=3300 keV. The half-life of Kr-70 was derived with increased precision and found to be t(1/2)=45.19 +/- 0.14 ms. The beta-delayed proton emission probability has also been determined as epsilon(p)=0.545(23)%. An increase in the beta-strength to the yrast 1(+) state in comparison with the heaviest Z=N+2 system studied so far (Ge-62 decay) is observed that may indicate increased np correlations in the T=0 channel. The beta-decay strength deduced from the results is interpreted in terms of the proton-neutron quasiparticle random-phase approximation (pnQRPA) and also with a schematic model that includes isoscalar and isovector pairing in addition to quadrupole deformation. The application of this last model indicates an approximate realization of pseudo-SU(4) symmetry in this system.
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Viñals, S., Nacher, E., Tengblad, O., Borge, M. J. G., Briz, J. A., Gad, A., et al. (2021). Calibration and response function of a compact silicon-detector set-up for charged-particle spectroscopy using GEANT4. Eur. Phys. J. A, 57(2), 49–9pp.
Abstract: A complete methodology for detector calibration and energy-loss correction in charged-particle spectroscopy is presented. This has been applied to a compact set-up of four silicon detectors used for beta-delayed particle spectroscopy. The characterisation of the set-up was carried out using GEANT4 Monte Carlo simulations and standard alpha-calibration sources. The response function of the system was in this way accurately determined to be used for spectral unfolding.
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Villanueva-Domingo, P., Villaescusa-Navarro, F., Genel, S., Angles-Alcazar, D., Hernquist, L., Marinacci, F., et al. (2023). Weighing the Milky Way and Andromeda galaxies with artificial intelligence. Phys. Rev. D, 107(10), 103003–8pp.
Abstract: We present new constraints on the masses of the halos hosting the Milky Way and Andromeda galaxies derived using graph neural networks. Our models, trained on 2,000 state-of-the-art hydrodynamic simulations of the CAMELS project, only make use of the positions, velocities and stellar masses of the galaxies belonging to the halos, and are able to perform likelihood-free inference on halo masses while accounting for both cosmological and astrophysical uncertainties. Our constraints are in agreement with estimates from other traditional methods, within our derived posterior standard deviation.
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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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Villanueva-Domingo, P., & Villaescusa-Navarro, F. (2021). Removing Astrophysics in 21 cm Maps with Neural Networks. Astrophys. J., 907(1), 44–14pp.
Abstract: Measuring temperature fluctuations in the 21 cm signal from the epoch of reionization and the cosmic dawn is one of the most promising ways to study the universe at high redshifts. Unfortunately, the 21 cm signal is affected by both cosmology and astrophysics processes in a nontrivial manner. We run a suite of 1000 numerical simulations with different values of the main astrophysical parameters. From these simulations we produce tens of thousands of 21 cm maps at redshifts 10 <= z <= 20. We train a convolutional neural network to remove the effects of astrophysics from the 21 cm maps and output maps of the underlying matter field. We show that our model is able to generate 2D matter fields not only that resemble the true ones visually but whose statistical properties agree with the true ones within a few percent down to scales 2 Mpc(-1). We demonstrate that our neural network retains astrophysical information that can be used to constrain the value of the astrophysical parameters. Finally, we use saliency maps to try to understand which features of the 21 cm maps the network is using in order to determine the value of the astrophysical parameters.
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