Stoppa, F., Vreeswijk, P., Bloemen, S., Bhattacharyya, S., Caron, S., Johannesson, G., et al. (2022). AutoSourceID-Light Fast optical source localization via U-Net and Laplacian of Gaussian. Astron. Astrophys., 662, A109–8pp.
Abstract: Aims. With the ever-increasing survey speed of optical wide-field telescopes and the importance of discovering transients when they are still young, rapid and reliable source localization is paramount. We present AutoSourceID-Light (ASID-L), an innovative framework that uses computer vision techniques that can naturally deal with large amounts of data and rapidly localize sources in optical images. Methods. We show that the ASID-L algorithm based on U-shaped networks and enhanced with a Laplacian of Gaussian filter provides outstanding performance in the localization of sources. A U-Net network discerns the sources in the images from many different artifacts and passes the result to a Laplacian of Gaussian filter that then estimates the exact location. Results. Using ASID-L on the optical images of the MeerLICHT telescope demonstrates the great speed and localization power of the method. We compare the results with SExtractor and show that our method outperforms this more widely used method. ASID-L rapidly detects more sources not only in low- and mid-density fields, but particularly in areas with more than 150 sources per square arcminute. The training set and code used in this paper are publicly available.
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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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ANTARES Collaboration(Albert, A. et al), Alves, S., Calvo, D., Carretero, V., Gozzini, R., Hernandez-Rey, J. J., et al. (2022). Search for magnetic monopoles with ten years of the ANTARES neutrino telescope. J. High Energy Astrophys., 34, 1–8.
Abstract: This work presents a new search for magnetic monopoles using data taken with the ANTARES neutrino telescope over a period of 10 years (January 2008 to December 2017). Compared to previous ANTARES searches, this analysis uses a run-by-run simulation strategy, with a larger exposure as well as a new simulation of magnetic monopoles taking into account the Kasama, Yang and Goldhaber model for their interaction cross-section with matter. No signal compatible with the passage of relativistic magnetic monopoles is observed, and upper limits on the flux of magnetic monopoles with beta = v/c & nbsp;>=& nbsp;0.55, are presented. For ultra-relativistic magnetic monopoles the flux limit is similar to 7 x 10(-18) cm(-2) s(-1) sr(-1). (C)& nbsp;2022 Elsevier B.V. All rights reserved.
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LHCb Collaboration(Aaij, R. et al), Jashal, B. K., Martinez-Vidal, F., Oyanguren, A., Remon Alepuz, C., & Ruiz Vidal, J. (2022). Identification of charm jets at LHCb. J. Instrum., 17(2), P02028–23pp.
Abstract: The identification of charm jets is achieved at LHCb for data collected in 2015-2018 using a method based on the properties of displaced vertices reconstructed and matched with jets. The performance of this method is determined using a dijet calibration dataset recorded by the LHCb detector and selected such that the jets are unbiased in quantities used in the tagging algorithm. The charm-tagging efficiency is reported as a function of the transverse momentum of the jet. The measured efficiencies are compared to those obtained from simulation and found to be in good agreement.
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Mistry, A. K. et al, Tain, J. L., Agramunt, J., Algora, A., Guadilla, V., Morales, A. I., et al. (2022). The DESPEC setup for GSI and FAIR. Nucl. Instrum. Methods Phys. Res. A, 1033, 166662–18pp.
Abstract: The DEcay SPECtroscopy (DESPEC) setup for nuclear structure investigations was developed and commissioned at GSI, Germany in preparation for a full campaign of experiments at the FRS and Super-FRS. In this paper, we report on the first employment of the setup in the hybrid configuration with the AIDA implanter coupled to the FATIMA LaBr3(Ce) fast-timing array, and high-purity germanium detectors. Initial results are shown from the first experiments carried out with the setup. An overview of the setup and function is discussed, including technical advancements along the path.
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