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Angles-Castillo, A., & Perez, A. (2022). A quantum walk simulation of extra dimensions with warped geometry. Sci Rep, 12(1), 1926–12pp.
Abstract: We investigate the properties of a quantum walk which can simulate the behavior of a spin 1/2 particle in a model with an ordinary spatial dimension, and one extra dimension with warped geometry between two branes. Such a setup constitutes a 1+ 1 dimensional version of the Randall-Sundrum model, which plays an important role in high energy physics. In the continuum spacetime limit, the quantum walk reproduces the Dirac equation corresponding to the model, which allows to anticipate some of the properties that can be reproduced by the quantum walk. In particular, we observe that the probability distribution becomes, at large time steps, concentrated near the “low energy” brane, and can be approximated as the lowest eigenstate of the continuum Hamiltonian that is compatible with the symmetries of the model. In this way, we obtain a localization effect whose strength is controlled by a warp coefficient. In other words, here localization arises from the geometry of the model, at variance with the usual effect that is originated from random irregularities, as in Anderson localization. In summary, we establish an interesting correspondence between a high energy physics model and localization in quantum walks.
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Lerendegui-Marco, J., Balibrea-Correa, J., Babiano-Suarez, V., Ladarescu, I., & Domingo-Pardo, C. (2022). Towards machine learning aided real-time range imaging in proton therapy. Sci Rep, 12(1), 2735–17pp.
Abstract: Compton imaging represents a promising technique for range verification in proton therapy treatments. In this work, we report on the advantageous aspects of the i-TED detector for proton-range monitoring, based on the results of the first Monte Carlo study of its applicability to this field. i-TED is an array of Compton cameras, that have been specifically designed for neutron-capture nuclear physics experiments, which are characterized by gamma-ray energies spanning up to 5-6 MeV, rather low gamma-ray emission yields and very intense neutron induced gamma-ray backgrounds. Our developments to cope with these three aspects are concomitant with those required in the field of hadron therapy, especially in terms of high efficiency for real-time monitoring, low sensitivity to neutron backgrounds and reliable performance at the high gamma-ray energies. We find that signal-to-background ratios can be appreciably improved with i-TED thanks to its light-weight design and the low neutron-capture cross sections of its LaCl3 crystals, when compared to other similar systems based on LYSO, CdZnTe or LaBr3. Its high time-resolution (CRT similar to 500 ps) represents an additional advantage for background suppression when operated in pulsed HT mode. Each i-TED Compton module features two detection planes of very large LaCl3 monolithic crystals, thereby achieving a high efficiency in coincidence of 0.2% for a point-like 1 MeV gamma-ray source at 5 cm distance. This leads to sufficient statistics for reliable image reconstruction with an array of four i-TED detectors assuming clinical intensities of 10(8) protons per treatment point. The use of a two-plane design instead of three-planes has been preferred owing to the higher attainable efficiency for double time-coincidences than for threefold events. The loss of full-energy events for high energy gamma-rays is compensated by means of machine-learning based algorithms, which allow one to enhance the signal-to-total ratio up to a factor of 2.
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Hirn, J., Garcia, J. E., Montesinos-Navarro, A., Sanchez-Martin, R., Sanz, V., & Verdu, M. (2022). A deep Generative Artificial Intelligence system to predict species coexistence patterns. Methods Ecol. Evol., 13, 1052–1061.
Abstract: Predicting coexistence patterns is a current challenge to understand diversity maintenance, especially in rich communities where these patterns' complexity is magnified through indirect interactions that prevent their approximation with classical experimental approaches. We explore cutting-edge Machine Learning techniques called Generative Artificial Intelligence (GenAI) to predict species coexistence patterns in vegetation patches, training generative adversarial networks (GAN) and variational AutoEncoders (VAE) that are then used to unravel some of the mechanisms behind community assemblage. The GAN accurately reproduces real patches' species composition and plant species' affinity to different soil types, and the VAE also reaches a high level of accuracy, above 99%. Using the artificially generated patches, we found that high-order interactions tend to suppress the positive effects of low-order interactions. Finally, by reconstructing successional trajectories, we could identify the pioneer species with larger potential to generate a high diversity of distinct patches in terms of species composition. Understanding the complexity of species coexistence patterns in diverse ecological communities requires new approaches beyond heuristic rules. Generative Artificial Intelligence can be a powerful tool to this end as it allows to overcome the inherent dimensionality of this challenge.
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Capra, S. et al, & Gadea, A. (2022). GALTRACE: A highly segmented silicon detector array for charged particle spectroscopy and discrimination. Nuovo Cim. C, 45(5), 98–4pp.
Abstract: GALTRACE is an array of segmented silicon detectors specifically built to work as an ancillary of the GALILEO gamma-ray spectrometer at Legnaro National Laboratory of INFN. GALTRACE consists of four telescopic Delta E-Edetectors which allow discriminating light charged particles also via pulse-shape analysis techniques. The good angular and energy resolutions, together with particle discrimination capabilities, make GALTRACE suitable for experiments where coincidences with specific emitted particles allow for the selection of reaction channels with very low cross section. The first in-beam experiment is reported here, aiming at identifying a narrow resonance, near-proton-threshold state in B-11, currently under discussion.
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HISPEC-DESPEC Collaboration(Polettini, M. et al), Algora, A., Morales, A. I., & Orrigo, S. E. A. (2022). Decay studies in the A similar to 225 Po-Fr region from the DESPEC campaign at GSI in 2021. Nuovo Cim. C, 45(5), 125–4pp.
Abstract: The HISPEC-DESPEC collaboration aims at investigating the struc-ture of exotic nuclei formed in fragmentation reactions with decay spectroscopymeasurements, as part of the FAIR Phase-0 campaign at GSI. This paper reportson first results of an experiment performed in spring 2021, with a focus on beta-decaystudies in the Po-Fr nuclei in the 220 < A <230 island of octupole deformationexploiting the DESPEC setup. Ion-beta correlations and fast-timing techniques arebeing employed, giving an insight into this difficult-to-reach region.
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