Vnuchenko, A., Esperante Pereira, D., Gimeno, B., Benedetti, S., Catalan Lasheras, N., Garlasch, M., et al. (2020). High-gradient testing of an S-band, normal-conducting low phase velocity accelerating structure. Phys. Rev. Accel. Beams, 23(8), 084801–13pp.
Abstract: A novel high-gradient accelerating structure with low phase velocity, v/c = 0.38, has been designed, manufactured and high-power tested. The structure was designed and built using the methodology and technology developed for CLIC 100 MV/m high-gradient accelerating structures, which have speed of light phase velocity, but adapts them to a structure for nonrelativistic particles. The parameters of the structure were optimized for the compact proton therapy linac project, and specifically to 76 MeV energy protons, but the type of structure opens more generally the possibility of compact low phase velocity linacs. The structure operates in S-band, is backward traveling wave (BTW) with a phase advance of 150 degrees and has an active length of 19 cm. The main objective for designing and testing this structure was to demonstrate that low velocity particles, in particular protons, can be accelerated with high gradients. In addition, the performance of this structure compared to other type of structures provides insights into the factors that limit high gradient operation. The structure was conditioned successfully to high gradient using the same protocol as for CLIC X-band structures. However, after the high power test, data analysis realized that the structure had been installed backwards, that is, the input power had been fed into what is nominally the output end of the structure. This resulted in higher peak fields at the power feed end and a steeply decreasing field profile along the structure, rather than the intended near constant field and gradient profile. A local accelerating gradient of 81 MV/m near the input end was achieved at a pulse length of 1.2 μs and with a breakdown rate (BDR) of 7.2 x 10(-7) 1 /pulse/m. The reverse configuration was accidental but the operating with this field condition gave very important insights into high-gradient behaviour and a comprehensive analysis has been carried out. A particular attention was paid to the characterization of the distribution of BD positions along the structure and within a cell.
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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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Villanueva-Domingo, P., Mena, O., & Palomares-Ruiz, S. (2021). A Brief Review on Primordial Black Holes as Dark Matter. Front. Astron. Space Sci., 8, 681084–10pp.
Abstract: Primordial black holes (PBHs) represent a natural candidate for one of the components of the dark matter (DM) in the Universe. In this review, we shall discuss the basics of their formation, abundance and signatures. Some of their characteristic signals are examined, such as the emission of particles due to Hawking evaporation and the accretion of the surrounding matter, effects which could leave an impact in the evolution of the Universe and the formation of structures. The most relevant probes capable of constraining their masses and population are discussed.
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Villanueva-Domingo, P., Mena, O., & Miralda-Escude, J. (2020). Maximum amplitude of the high-redshift 21-cm absorption feature. Phys. Rev. D, 101(8), 083502–8pp.
Abstract: We examine the maximum possible strength of the global 21-cm absorption dip on the cosmic background radiation at high-redshift caused by the atomic intergalactic medium, when the Lyman-alpha coupling is maximum, assuming no exotic cooling mechanisms from interactions with dark matter. This maximum absorption is limited by three inevitable factors that need to be accounted for: (a) heating by energy transferred from the cosmic background radiation to the hydrogen atoms via 21-cm transitions, dubbed as 21-cm heating; (b) Ly alpha heating by scatterings of Ly alpha photons from the first stars; (c) the impact of the expected density fluctuations in the intergalactic gas in standard cold dark matter theory, which reduces the mean 21-cm absorption signal. Inclusion of this third novel effect reduces the maximum global 21-cm absorption by similar to 10%. Overall, the three effects studied here reduce the 21-cm global absorption by similar to 20% at z similar or equal to 17.
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Villanueva-Domingo, P., & Ichiki, K. (2023). 21 cm forest constraints on primordial black holes. Publ. Astron. Soc. Jpn., 75(SP1), S33–S49.
Abstract: Primordial black holes (PBHs) as part of the dark matter (DM) would modify the evolution of large-scale structures and the thermal history of the universe. Future 21 cm forest observations, sensitive to small scales and the thermal state of the intergalactic medium (IGM), could probe the existence of such PBHs. In this article, we show that the shot noise isocurvature mode on small scales induced by the presence of PBHs can enhance the amount of low-mass halos, or minihalos, and thus, the number of 21 cm absorption lines. However, if the mass of PBHs is as large as M-PBH greater than or similar to 10 M-circle dot, with an abundant enough fraction of PBHs as DM, f(PBH), the IGM heating due to accretion on to the PBHs counteracts the enhancement due to the isocurvature mode, reducing the number of absorption lines instead. The concurrence of both effects imprints distinctive signatures on the number of absorbers, allowing the abundance of PBHs to be bound. We compute the prospects for constraining PBHs with future 21 cm forest observations, finding achievable competitive upper limits on the abundance as low as f(PBH) similar to 10(-3) at M-PBH = 100 M-circle dot, or even lower at larger masses, in regions of the parameter space unexplored by current probes. The impact of astrophysical X-ray sources on the IGM temperature is also studied, which could potentially weaken the bounds.
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Villaescusa-Navarro, F. et al, & Villanueva-Domingo, P. (2022). The CAMELS Multifield Data Set: Learning the Universe's Fundamental Parameters with Artificial Intelligence. Astrophys. J. Suppl. Ser., 259(2), 61–14pp.
Abstract: We present the Cosmology and Astrophysics with Machine Learning Simulations (CAMELS) Multifield Data set (CMD), a collection of hundreds of thousands of 2D maps and 3D grids containing many different properties of cosmic gas, dark matter, and stars from more than 2000 distinct simulated universes at several cosmic times. The 2D maps and 3D grids represent cosmic regions that span similar to 100 million light-years and have been generated from thousands of state-of-the-art hydrodynamic and gravity-only N-body simulations from the CAMELS project. Designed to train machine-learning models, CMD is the largest data set of its kind containing more than 70 TB of data. In this paper we describe CMD in detail and outline a few of its applications. We focus our attention on one such task, parameter inference, formulating the problems we face as a challenge to the community. We release all data and provide further technical details at https://camels-multifield-dataset.readthedocs.io.
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