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Dong, P. V., Huong, D. T., Camargo, D. A., Queiroz, F. S., & Valle, J. W. F. (2019). Asymmetric dark matter, inflation, and leptogenesis from B-L symmetry breaking. Phys. Rev. D, 99(5), 055040–17pp.
Abstract: We propose a unified setup for dark matter, inflation, and baryon asymmetry generation through the neutrino mass seesaw mechanism. Our scenario emerges naturally from an extended gauge group containing B-L as a noncommutative symmetry, broken by a singlet scalar that also drives inflation. Its decays reheat the universe, producing the lightest right-handed neutrino. Automatic matter parity conservation leads to the stability of an asymmetric dark matter candidate, directly linked to the matter-antimatter asymmetry in the Universe.
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Centelles Chulia, S., & Trautner, A. (2020). Asymmetric tri-bi-maximal mixing and residual symmetries. Mod. Phys. Lett. A, 35(35), 2050292–15pp.
Abstract: Asymmetric tri-bi-maximal mixing is a recently proposed, grand unified theory (GUT) based, flavor mixing scheme. In it, the charged lepton mixing is fixed by the GUT connection to down-type quarks and a T-13 flavor symmetry, while neutrino mixing is assumed to be tri-bi-maximal (TBM) with one additional free phase. Here we show that this additional free phase can be fixed by the residual flavor and CP symmetries of the effective neutrino mass matrix. We discuss how those residual symmetries can be unified with T-13 and identify the smallest possible unified flavor symmetries, namely (Z(13)xZ(13))(sic)D-12 and (Z(13)xZ(13))(sic)S-4. Sharp predictions are obtained for lepton mixing angles, CP violating phases and neutrinoless double beta decay.
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Magalhaes, R. B., Maso-Ferrando, A., Olmo, G. J., & Crispino, L. C. B. (2023). Asymmetric wormholes in Palatini f (R) gravity: Energy conditions, absorption, and quasibound states. Phys. Rev. D, 108(2), 024063–20pp.
Abstract: We investigate the scalar absorption spectrum of wormhole solutions constructed via the recently developed thin-shell formalism for Palatini f(R) gravity. Such wormholes come from the matching of two Reissner-Nordstrom spacetimes at a timelike hypersurface (shell), which, according to the junction conditions in Palatini f(R), can be stable and have either positive or negative energy density. In particular, we identified a new physically interesting configuration made out of two overcharged Reissner-Nordstrom spacetimes, whose absorption profile departs from that of black holes and other previously considered wormholes in the whole range of frequencies. Unlike in symmetric wormhole solutions, the asymmetry of the effective potential causes the dilution of the resonances associated to the quasibound states for the high -frequency regime. Therefore, slight asymmetries in wormhole space-times could have a dramatic impact on the observable features associated to resonant states.
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Herrero-Garcia, J., Landini, G., & Vatsyayan, D. (2023). Asymmetries in extended dark sectors: a cogenesis scenario. J. High Energy Phys., 05(5), 049–41pp.
Abstract: The observed dark matter relic abundance may be explained by different mechanisms, such as thermal freeze-out/freeze-in, with one or more symmetric/asymmetric components. In this work we investigate the role played by asymmetries in determining the yield and nature of dark matter in non-minimal scenarios with more than one dark matter particle. In particular, we show that the energy density of a particle may come from an asymmetry, even if the particle is asymptotically symmetric by nature. To illustrate the different effects of asymmetries, we adopt a model with two dark matter components. We embed it in a multi-component cogenesis scenario that is also able to reproduce neutrino masses and the baryon asymmetry. In some cases, the model predicts an interesting monochromatic neutrino line that may be searched for at neutrino telescopes.
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Plenter, J., & Rodrigo, G. (2021). Asymptotic expansions through the loop-tree duality. Eur. Phys. J. C, 81(4), 320–13pp.
Abstract: Asymptotic expansions of Feynman amplitudes in the loop-tree duality formalism are implemented at integrand-level in the Euclidean space of the loop three-momentum, where the hierarchies among internal and external scales are well-defined. The ultraviolet behaviour of the individual contributions to the asymptotic expansion emerges only in the first terms of the expansion and is renormalized locally in four space-time dimensions. These two properties represent an advantage over the method of Expansion by Regions. We explore different approaches in different kinematical limits, and derive explicit asymptotic expressions for several benchmark configurations.
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ATLAS Collaboration(Aad, G. et al), Alvarez Piqueras, D., Aparisi Pozo, J. A., Bailey, A. J., Cabrera Urban, S., Castillo, F. L., et al. (2019). ATLAS b-jet identification performance and efficiency measurement with t(t)over-bar events in pp collisions at root s=13 TeV. Eur. Phys. J. C, 79(11), 970–36pp.
Abstract: The algorithms used by the ATLAS Collaboration during Run 2 of the Large Hadron Collider to identify jets containing b-hadrons are presented. The performance of the algorithms is evaluated in the simulation and the efficiency with which these algorithms identify jets containing b-hadrons is measured in collision data. The measurement uses a likelihood-based method in a sample highly enriched in t (t) over bar events. The topology of the t -> Wb decays is exploited to simultaneously measure both the jet flavour composition of the sample and the efficiency in a transverse momentum range from 20 to 600 GeV. The efficiency measurement is subsequently compared with that predicted by the simulation. The data used in this measurement, corresponding to a total integrated luminosity of 80.5 fb(-1), were collected in proton-proton collisions during the years 2015-2017 at a centre-of-mass energy root s = 13 TeV. By simultaneously extracting both the efficiency and jet flavour composition, this measurement significantly improves the precision compared to previous results, with uncertainties ranging from 1 to 8% depending on the jet transverse momentum.
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ATLAS Collaboration(Aad, G. et al), Aparisi Pozo, J. A., Bailey, A. J., Barranco Navarro, L., Cabrera Urban, S., Castillo, F. L., et al. (2020). ATLAS data quality operations and performance for 2015-2018 data-taking. J. Instrum., 15(4), P04003–43pp.
Abstract: The ATLAS detector at the Large Hadron Collider reads out particle collision data from over 100 million electronic channels at a rate of approximately 100 kHz, with a recording rate for physics events of approximately 1 kHz. Before being certified for physics analysis at computer centres worldwide, the data must be scrutinised to ensure they are clean from any hardware or software related issues that may compromise their integrity. Prompt identification of these issues permits fast action to investigate, correct and potentially prevent future such problems that could render the data unusable. This is achieved through the monitoring of detector-level quantities and reconstructed collision event characteristics at key stages of the data processing chain. This paper presents the monitoring and assessment procedures in place at ATLAS during 2015-2018 data-taking. Through the continuous improvement of operational procedures, ATLAS achieved a high data quality efficiency, with 95.6% of the recorded proton-proton collision data collected at root s = 13 TeV certified for physics analysis.
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ATLAS Collaboration(Aad, G. et al), Amos, K. R., Aparisi Pozo, J. A., Bailey, A. J., Bouchhar, N., Cabrera Urban, S., et al. (2023). ATLAS flavour-tagging algorithms for the LHC Run 2 pp collision dataset. Eur. Phys. J. C, 83(7), 681–37pp.
Abstract: The flavour-tagging algorithms developed by the AvTLAS Collaboration and used to analyse its dataset of root s = 13 TeV pp collisions from Run 2 of the Large Hadron Collider are presented. These new tagging algorithms are based on recurrent and deep neural networks, and their performance is evaluated in simulated collision events. These developments yield considerable improvements over previous jet-flavour identification strategies. At the 77% b-jet identification efficiency operating point, light-jet (charm-jet) rejection factors of 170 (5) are achieved in a sample of simulated Standard Model t (t) over bar events; similarly, at a c-jet identification efficiency of 30%, a light-jet (b-jet) rejection factor of 70 (9) is obtained.
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Stoppa, F., Bhattacharyya, S., Ruiz de Austri, R., Vreeswijk, P., Caron, S., Zaharijas, G., et al. (2023). AutoSourceID-Classifier Star-galaxy classification using a convolutional neural network with spatial information. Astron. Astrophys., 680, A109–16pp.
Abstract: Aims. Traditional star-galaxy classification techniques often rely on feature estimation from catalogs, a process susceptible to introducing inaccuracies, thereby potentially jeopardizing the classification's reliability. Certain galaxies, especially those not manifesting as extended sources, can be misclassified when their shape parameters and flux solely drive the inference. We aim to create a robust and accurate classification network for identifying stars and galaxies directly from astronomical images.Methods. The AutoSourceID-Classifier (ASID-C) algorithm developed for this work uses 32x32 pixel single filter band source cutouts generated by the previously developed AutoSourceID-Light (ASID-L) code. By leveraging convolutional neural networks (CNN) and additional information about the source position within the full-field image, ASID-C aims to accurately classify all stars and galaxies within a survey. Subsequently, we employed a modified Platt scaling calibration for the output of the CNN, ensuring that the derived probabilities were effectively calibrated, delivering precise and reliable results.Results. We show that ASID-C, trained on MeerLICHT telescope images and using the Dark Energy Camera Legacy Survey (DECaLS) morphological classification, is a robust classifier and outperforms similar codes such as SourceExtractor. To facilitate a rigorous comparison, we also trained an eXtreme Gradient Boosting (XGBoost) model on tabular features extracted by SourceExtractor. While this XGBoost model approaches ASID-C in performance metrics, it does not offer the computational efficiency and reduced error propagation inherent in ASID-C's direct image-based classification approach. ASID-C excels in low signal-to-noise ratio and crowded scenarios, potentially aiding in transient host identification and advancing deep-sky astronomy.
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Stoppa, F., Ruiz de Austri, R., Vreeswijk, P., Bhattacharyya, S., Caron, S., Bloemen, S., et al. (2023). AutoSourceID-FeatureExtractor Optical image analysis using a two-step mean variance estimation network for feature estimation and uncertainty characterisation. Astron. Astrophys., 680, A108–14pp.
Abstract: Aims. In astronomy, machine learning has been successful in various tasks such as source localisation, classification, anomaly detection, and segmentation. However, feature regression remains an area with room for improvement. We aim to design a network that can accurately estimate sources' features and their uncertainties from single-band image cutouts, given the approximated locations of the sources provided by the previously developed code AutoSourceID-Light (ASID-L) or other external catalogues. This work serves as a proof of concept, showing the potential of machine learning in estimating astronomical features when trained on meticulously crafted synthetic images and subsequently applied to real astronomical data.Methods. The algorithm presented here, AutoSourceID-FeatureExtractor (ASID-FE), uses single-band cutouts of 32x32 pixels around the localised sources to estimate flux, sub-pixel centre coordinates, and their uncertainties. ASID-FE employs a two-step mean variance estimation (TS-MVE) approach to first estimate the features and then their uncertainties without the need for additional information, for example the point spread function (PSF). For this proof of concept, we generated a synthetic dataset comprising only point sources directly derived from real images, ensuring a controlled yet authentic testing environment.Results. We show that ASID-FE, trained on synthetic images derived from the MeerLICHT telescope, can predict more accurate features with respect to similar codes such as SourceExtractor and that the two-step method can estimate well-calibrated uncertainties that are better behaved compared to similar methods that use deep ensembles of simple MVE networks. Finally, we evaluate the model on real images from the MeerLICHT telescope and the Zwicky Transient Facility (ZTF) to test its transfer learning abilities.
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