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Albaladejo, M., Nieves, J., & Ruiz Arriola, E. (2023). Femtoscopic signatures of the lightest S-wave scalar open-charm mesons. Phys. Rev. D, 108, 014020–7pp.
Abstract: We predict femtoscopy correlation functions for S-wave D(s)ϕ pairs of lightest pseudoscalar open-charm mesons and Goldstone bosons from next-to-leading-order unitarized heavy-meson chiral perturbation theory amplitudes. The effect of the two-state structure around 2300 MeV can be clearly seen in the (S,I)=(0,1/2) Dπ, Dη, and Ds¯K correlation functions, while in the scalar-strange (1,0) sector, the D∗s0(2317)± state lying below the DK threshold produces a depletion of the correlation function near threshold. These exotic states owe their existence to the nonperturbative dynamics of Goldstone-boson scattering off D(s). The predicted correlation functions could be experimentally measured and will shed light into the hadron spectrum, confirming that it should be viewed as more than a collection of quark model states.
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Albaladejo, M., Nieves, J., & Tolos, L. (2021). D(D)over-bar* scattering and chi(c1) (3872) in nuclear matter. Phys. Rev. C, 104(3), 035203–20pp.
Abstract: We study the behavior of the chi(c1) (3872), also known as X(3872), in dense nuclear matter. We begin from a picture in vacuum of the X(3872) as a purely molecular (D (D) over bar*-c.c.) state, generated as a bound state from a heavy-quark symmetry leading-order interaction between the charmed mesons, and analyze the D (D) over bar* scattering T matrix (T-D (D) over bar*) inside of the medium. Next, we consider also mixed-molecular scenarios and, in all cases, we determine the corresponding X(3872) spectral function and the D (D) over bar* amplitude, with the mesons embedded in the dense environment. We find important nuclear corrections for T-D (D) over bar* and the pole position of the resonance, and discuss the dependence of these results on the D (D) over bar* molecular component in the X(3872) wave function. These predictions could be tested in the finite-density regime that can be accessed in the future CBM and PANDA experiments at the Facility for Antiproton and Ion Research (FAIR).
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Albandea, D., Del Debbio, L., Hernandez, P., Kenway, R., Marsh Rossney, J., & Ramos, A. (2023). Learning trivializing flows. Eur. Phys. J. C, 83(7), 676–14pp.
Abstract: The recent introduction of machine learning techniques, especially normalizing flows, for the sampling of lattice gauge theories has shed some hope on improving the sampling efficiency of the traditional hybrid Monte Carlo (HMC) algorithm. In this work we study a modified HMC algorithm that draws on the seminal work on trivializing flows by L & uuml;scher. Autocorrelations are reduced by sampling from a simpler action that is related to the original action by an invertible mapping realised through Normalizing Flows models with a minimal set of training parameters. We test the algorithm in a f(4) theory in 2D where we observe reduced autocorrelation times compared with HMC, and demonstrate that the training can be done at small unphysical volumes and used in physical conditions. We also study the scaling of the algorithm towards the continuum limit under various assumptions on the network architecture.
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Albandea, D., Hernandez, P., Ramos, A., & Romero-Lopez, F. (2021). Topological sampling through windings. Eur. Phys. J. C, 81(10), 873–12pp.
Abstract: We propose a modification of the Hybrid Monte Carlo (HMC) algorithm that overcomes the topological freezing of a two-dimensional U(1) gauge theory with and without fermion content. This algorithm includes reversible jumps between topological sectors – winding steps – combined with standard HMC steps. The full algorithm is referred to as winding HMC (wHMC), and it shows an improved behaviour of the autocorrelation time towards the continuum limit. We find excellent agreement between the wHMC estimates of the plaquette and topological susceptibility and the analytical predictions in the U(1) pure gauge theory, which are known even at finite beta. We also study the expectation values in fixed topological sectors using both HMC and wHMC, with and without fermions. Even when topology is frozen in HMC – leading to significant deviations in topological as well as non-topological quantities – the two algorithms agree on the fixed-topology averages. Finally, we briefly compare the wHMC algorithm results to those obtained with master-field simulations of size L similar to 8 x 10(3).
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Albiol, A., Albiol, F., Paredes, R., Plasencia-Martinez, J. M., Blanco Barrio, A., Garcia Santos, J. M., et al. (2022). A comparison of Covid-19 early detection between convolutional neural networks and radiologists. Insights Imaging, 13(1), 122–12pp.
Abstract: Background The role of chest radiography in COVID-19 disease has changed since the beginning of the pandemic from a diagnostic tool when microbiological resources were scarce to a different one focused on detecting and monitoring COVID-19 lung involvement. Using chest radiographs, early detection of the disease is still helpful in resource-poor environments. However, the sensitivity of a chest radiograph for diagnosing COVID-19 is modest, even for expert radiologists. In this paper, the performance of a deep learning algorithm on the first clinical encounter is evaluated and compared with a group of radiologists with different years of experience. Methods The algorithm uses an ensemble of four deep convolutional networks, Ensemble4Covid, trained to detect COVID-19 on frontal chest radiographs. The algorithm was tested using images from the first clinical encounter of positive and negative cases. Its performance was compared with five radiologists on a smaller test subset of patients. The algorithm's performance was also validated using the public dataset COVIDx. Results Compared to the consensus of five radiologists, the Ensemble4Covid model achieved an AUC of 0.85, whereas the radiologists achieved an AUC of 0.71. Compared with other state-of-the-art models, the performance of a single model of our ensemble achieved nonsignificant differences in the public dataset COVIDx. Conclusion The results show that the use of images from the first clinical encounter significantly drops the detection performance of COVID-19. The performance of our Ensemble4Covid under these challenging conditions is considerably higher compared to a consensus of five radiologists. Artificial intelligence can be used for the fast diagnosis of COVID-19.
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