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Feijoo, A., Wang, W. F., Xiao, C. W., Wu, J. J., Oset, E., Nieves, J., et al. (2023). A new look at the P-cs states from a molecular perspective. Phys. Lett. B, 839, 137760–7pp.
Abstract: We have a look at the P-cs states generated from the interaction of (D) over bar(*)Xi(c)('*) coupled channels. We consider the blocks of pseudoscalar-baryon (1/2(+) , 3/2(+)) and vector-baryon (1/2(+), 3/2(+)), and find 10 resonant states coupling mostly to (D) over bar Xi(c), <(D)*over bar>*Xi(c), (D) over bar Xi(c)' <(DA novel aspect of the work is the realization that the <(Dover bar>Xi(c), (Dover bar>(s) Lambda(c) or (Dover bar>*Xi(c), D-s*Lambda(c) channels, with a strong transition potential, collaborate to produce a larger attraction than the corresponding states <(Dover bar>Xi(c), <(Dover bar>Lambda(c) or (D) over bar*Xi(c), (D) over bar*Lambda(c) appearing in the generation of the strangenessless P-c states, since in the latter case the transition potential between those channels is zero. The extra attraction obtained in the (D) over bar Xi(c), (D) over bar* Xi(c) pairs preclude the association of the P-cs(4338) state coupling mostly to (D) over bar*Xi(c) while the P-cs(4459) is associated to the state found that couples mostly to (D) over bar Xi(c)'. Four more states appear, like in other molecular pictures, and some of the states are degenerate in spin. Counting different spin states we find 10states, which we hope can be observed in the near future.
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Araujo Filho, A. A., Zare, S., Porffrio, P. J., Kriz, J., & Hassanabadi, H. (2023). Thermodynamics and evaporation of a modified Schwarzschild black hole in a non-commutative gauge theory. Phys. Lett. B, 838, 137744–9pp.
Abstract: In this work, we study the thermodynamic properties on a non-commutative background via gravitational gauge field potentials. This procedure is accomplished after contracting de Sitter (dS) group, SO(4, 1), with the Poincare group, ISO(3, 1). Particularly, we focus on a static spherically symmetric black hole. In this manner, we calculate the modified Hawking temperature and the other deformed thermal state quantities, namely, entropy, heat capacity, Helmholtz free energy and pressure. Finally, we also investigate the black hole evaporation process in such a context.
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Pinto-Gomez, F., De Soto, F., Ferreira, M. N., Papavassiliou, J., & Rodriguez-Quintero, J. (2023). Lattice three-gluon vertex in extended kinematics: Planar degeneracy. Phys. Lett. B, 838, 137737–8pp.
Abstract: We present novel results for the three-gluon vertex, obtained from an extensive quenched lattice simulation in the Landau gauge. The simulation evaluates the transversely projected vertex, spanned on a special tensorial basis, whose form factors are naturally parametrized in terms of individually Bosesymmetric variables. Quite interestingly, when evaluated in these kinematics, the corresponding form factors depend almost exclusively on a single kinematic variable, formed by the sum of the squares of the three incoming four-momenta, q, r, and p. Thus, all configurations lying on a given plane in the coordinate system (q2, r2, p2) share, to a high degree of accuracy, the same form factors, a property that we denominate planar degeneracy. We have confirmed the validity of this property through an exhaustive study of the set of configurations satisfying the condition q2 = r2, within the range [0, 5 GeV]. This drastic simplification allows for a remarkably compact description of the main bulk of the data, which is particularly suitable for future numerical applications. A semi-perturbative analysis reproduces the lattice findings rather accurately, once the inclusion of a gluon mass has cured all spurious divergences.
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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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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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