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HAWC Collaboration(Albert, A. et al), & Salesa Greus, F. (2020). HAWC and Fermi-LAT Detection of Extended Emission from the Unidentified Source 2HWC J2006+341. Astrophys. J. Lett., 903(1), L14–6pp.
Abstract: The discovery of the TeV point source 2HWC J2006+341 was reported in the second HAWC gamma-ray catalog. We present a follow-up study of this source here. The TeV emission is best described by an extended source with a soft spectrum. At GeV energies, an extended source is significantly detected in Fermi-LAT data. The matching locations, sizes, and spectra suggest that both gamma-ray detections correspond to the same source. Different scenarios for the origin of the emission are considered and we rule out an association to the pulsar PSR J2004+3429 due to extreme energetics required, if located at a distance of 10.8 kpc.
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HAWC Collaboration(Albert, A. et al), & Salesa Greus, F. (2021). Evidence of 200 TeV Photons from HAWC J1825-134. Astrophys. J. Lett., 907(2), L30–9pp.
Abstract: The Earth is bombarded by ultrarelativistic particles, known as cosmic rays (CRs). CRs with energies up to a few PeV (=10(15) eV), the knee in the particle spectrum, are believed to have a Galactic origin. One or more factories of PeV CRs, or PeVatrons, must thus be active within our Galaxy. The direct detection of PeV protons from their sources is not possible since they are deflected in the Galactic magnetic fields. Hundred TeV gamma-rays from decaying pi(0), produced when PeV CRs collide with the ambient gas, can provide the decisive evidence of proton acceleration up to the knee. Here we report the discovery by the High Altitude Water Cerenkov (HAWC) observatory of the gamma-ray source, HAWC J1825-134, whose energy spectrum extends well beyond 200 TeV without a break or cutoff. The source is found to be coincident with a giant molecular cloud. The ambient gas density is as high as 700 protons cm(-3). While the nature of this extreme accelerator remains unclear, CRs accelerated to energies of several PeV colliding with the ambient gas likely produce the observed radiation.
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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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Albiol, F., Corbi, A., & Albiol, A. (2017). Evaluation of modern camera calibration techniques for conventional diagnostic X-ray imaging settings. Radiol. Phys. Technol., 10(1), 68–81.
Abstract: We explore three different alternatives for obtaining intrinsic and extrinsic parameters in conventional diagnostic X-ray frameworks: the direct linear transform (DLT), the Zhang method, and the Tsai approach. We analyze and describe the computational, operational, and mathematical background differences for these algorithms when they are applied to ordinary radiograph acquisition. For our study, we developed an initial 3D calibration frame with tin cross-shaped fiducials at specific locations. The three studied methods enable the derivation of projection matrices from 3D to 2D point correlations. We propose a set of metrics to compare the efficiency of each technique. One of these metrics consists of the calculation of the detector pixel density, which can be also included as part of the quality control sequence in general X-ray settings. The results show a clear superiority of the DLT approach, both in accuracy and operational suitability. We paid special attention to the Zhang calibration method. Although this technique has been extensively implemented in the field of computer vision, it has rarely been tested in depth in common radiograph production scenarios. Zhang's approach can operate on much simpler and more affordable 2D calibration frames, which were also tested in our research. We experimentally confirm that even three or four plane-image correspondences achieve accurate focal lengths.
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Borja, E. F., Garay, I., & Vidotto, F. (2012). Learning about Quantum Gravity with a Couple of Nodes. Symmetry Integr. Geom., 8, 015–44pp.
Abstract: Loop Quantum Gravity provides a natural truncation of the infinite degrees of freedom of gravity, obtained by studying the theory on a given finite graph. We review this procedure and we present the construction of the canonical theory on a simple graph, formed by only two nodes. We review the U(N) framework, which provides a powerful tool for the canonical study of this model, and a formulation of the system based on spinors. We consider also the covariant theory, which permits to derive the model from a more complex formulation, paying special attention to the cosmological interpretation of the theory.
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