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Richard, J. M., Valcarce, A., & Vijande, J. (2020). Hall-Post inequalities: Review and application to molecules and tetraquarks. Ann. Phys., 412, 168009–32pp.
Abstract: A review is presented of the Hall-Post inequalities that give lower-bounds to the ground-state energy of quantum systems in terms of energies of smaller systems. New applications are given for systems experiencing both a static source and inner interactions, as well as for hydrogen-like molecules and for tetraquarks in some quark models. In the latter case, the Hall-Post inequalities constrain the possibility of deeply-bound exotic mesons below the threshold for dissociation into two quark-antiquark mesons. We also emphasize the usefulness of the Hall-Post bounds in terms of 3-body energies when some 2-body subsystems are ill defined or do not support any bound state.
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Richard, J. M., Valcarce, A., & Vijande, J. (2024). Resonances in the Quark Model. Few-Body Syst., 65(3), 71–11pp.
Abstract: A discussion is presented of the estimates of the energy and width of resonances in constituent models, with focus on the tetraquark states containing heavy quarks.
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Richard, J. M., Valcarce, A., & Vijande, J. (2017). Stable heavy pentaquarks in constituent models. Phys. Lett. B, 774, 710–714.
Abstract: It is shown that standard constituent quark models produce ((c)over-barcqqq) hidden-charm pentaquarks, where c denotes the charmed quark and q a light quark, which lie below the lowest threshold for spontaneous dissociation and thus are stable in the limit where the internal (c)over-barc annihilation is neglected. The binding is a cooperative effect of the chromoelectric and chromomagnetic components of the interaction, and it disappears in the static limit with a pure chromoelectric potential. Their wave function contains color sextet and color octet configurations for the subsystems and can hardly be reduced to a molecular state made of two interacting hadrons. These pentaquark states could be searched for in the experiments having discovered or confirmed the hidden-charm meson and baryon resonances.
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AGATA Collaboration(Rezynkina, K. et al), Gadea, A., & Perez-Vidal, R. M. (2022). Structure of As-83, As- 85, and As-87: From semimagicity to gamma softness. Phys. Rev. C, 106(1), 014320–14pp.
Abstract: The structure of As-83,As- 85, and As-87 have been studied in fusion-fission reaction( 238)U+9Be. Fission fragments were identified in mass and atomic number using the VAMOS++ spectrometer and the coincident gamma rays were detected in the gamma-ray tracking array AGATA. New transitions in 83As and 85As are reported and placed in the level schemes. A level scheme of the excited states in 87As is proposed for the first time. The data are interpreted in frame of large-scale shell-model calculations, SU3 symmetries, and beyond mean-field frameworks. A spherical regime at magic number N = 50 is predicted and the location of the proton g9/2 orbital is proposed for the first time. Development of collectivity in a prolate deformed, gamma-soft regime in the open shell cases 85As and 87As, most neutron-rich isotopes beyond N = 50, is concluded. Data and theoretical calculations give confidence to a relatively high extrapolated excitation energy about 4 MeV of the 9/2+ state in 79Cu, one proton above 78Ni.
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NEXT Collaboration(Renner, J. et al), Benlloch-Rodriguez, J., Botas, A., Ferrario, P., Gomez-Cadenas, J. J., Alvarez, V., et al. (2017). Background rejection in NEXT using deep neural networks. J. Instrum., 12, T01004–21pp.
Abstract: We investigate the potential of using deep learning techniques to reject background events in searches for neutrinoless double beta decay with high pressure xenon time projection chambers capable of detailed track reconstruction. The differences in the topological signatures of background and signal events can be learned by deep neural networks via training over many thousands of events. These networks can then be used to classify further events as signal or background, providing an additional background rejection factor at an acceptable loss of efficiency. The networks trained in this study performed better than previous methods developed based on the use of the same topological signatures by a factor of 1.2 to 1.6, and there is potential for further improvement.
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