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Albaladejo, M., Guo, F. K., Hanhart, C., Meissner, U. G., Nieves, J., Nogga, A., et al. (2017). Note on X(3872) production at hadron colliders and its molecular structure. Chin. Phys. C, 41(12), 121001–3pp.
Abstract: The production of the X (3872) as a hadronic molecule in hadron colliders is clarified. We show that the conclusion of Bignamini et al., Phys. Rev. Lett. 103 (2009) 162001, that the production of the X(3872) at high pT implies a non-molecular structure, does not hold. In particular, using the well understood properties of the deuteron wave function as an example, we identify the relevant scales in the production process.
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Albaladejo, M., & Moussallam, B. (2017). Extended chiral Khuri-Treiman formalism for eta -> 3 pi and the role of the a(0)(980), f(0)(980) resonances. Eur. Phys. J. C, 77(8), 508–23pp.
Abstract: Recent experiments on eta -> 3 pi decays have provided an extremely precise knowledge of the amplitudes across the Dalitz region which represent stringent constraints on theoretical descriptions. We reconsider an approach in which the low-energy chiral expansion is assumed to be optimally convergent in an unphysical region surrounding the Adler zero, and the amplitude in the physical region is uniquely deduced by an analyticity-based extrapolation using the Khuri-Treiman dispersive formalism. We present an extension of the usual formalism which implements the leading inelastic effects from the K (K) over bar channel in the final-state pi pi interaction as well as in the initial-state eta pi interaction. The constructed amplitude has an enlarged region of validity and accounts in a realistic way for the influence of the two light scalar resonances f(0)(980) and a(0)(980) in the dispersive integrals. It is shown that the effect of these resonances in the low-energy region of the eta -> 3 pi decay is not negligible, in particular for the 3 pi(0) mode, and improves the description of the energy variation across the Dalitz plot. Some remarks are made on the scale dependence and the value of the double quark mass ratio Q.
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Albaladejo, M., & Nieves, J. (2022). Compositeness of S-wave weakly-bound states from next-to-leading order Weinberg's relations. Eur. Phys. J. C, 82(8), 724–12pp.
Abstract: We discuss a model-independent estimator of the likelihood of the compositeness of a shallow S-wave bound or virtual state. The approach is based on an extension of Weinberg's relations in Weinberg (Phys Rev 137:B672, 1965) and it relies only on the proximity of the energy of the state to the two-hadron threshold to which it significantly couples. The scheme only makes use of the experimental scattering length and the effective range low energy parameters, and it is shown to be fully consistent for predominantly molecular hadrons. As explicit applications, we analyse the case of the deuteron, the S-1(0) nucleon virtual state and the exotic D-so(*)(2317)(+/-) , and find strong support to the molecular interpretation in all cases. Results are less conclusive for the D* (s0)(2317)+/-, since the binding energy of this state would be significantly higher than that of the deuteron, and the approach employed here is at the limit of its applicability. We also qualitatively address the case of the recently discovered T + cc state, within the isospin limit to avoid the complexity of the very close thresholds (DD)-D-0*+ and D + D*(0), which could mask the ingredients of the approach proposed in this work.
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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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Albiol, A., Corbi, A., & Albiol, F. (2017). Automatic intensity windowing of mammographic images based on a perceptual metric. Med. Phys., 44(4), 1369–1378.
Abstract: Purpose: Initial auto-adjustment of the window level WL and width WW applied to mammographic images. The proposed intensity windowing (IW) method is based on the maximization of the mutual information (MI) between a perceptual decomposition of the original 12-bit sources and their screen displayed 8-bit version. Besides zoom, color inversion and panning operations, IW is the most commonly performed task in daily screening and has a direct impact on diagnosis and the time involved in the process. Methods: The authors present a human visual system and perception-based algorithm named GRAIL (Gabor-relying adjustment of image levels). GRAIL initially measures a mammogram's quality based on the MI between the original instance and its Gabor-filtered derivations. From this point on, the algorithm performs an automatic intensity windowing process that outputs the WL/WW that best displays each mammogram for screening. GRAIL starts with the default, high contrast, wide dynamic range 12-bit data, and then maximizes the graphical information presented in ordinary 8-bit displays. Tests have been carried out with several mammogram databases. They comprise correlations and an ANOVA analysis with the manual IW levels established by a group of radiologists. A complete MATLAB implementation of GRAIL is available at . Results: Auto-leveled images show superior quality both perceptually and objectively compared to their full intensity range and compared to the application of other common methods like global contrast stretching (GCS). The correlations between the human determined intensity values and the ones estimated by our method surpass that of GCS. The ANOVA analysis with the upper intensity thresholds also reveals a similar outcome. GRAIL has also proven to specially perform better with images that contain micro-calcifications and/or foreign X-ray-opaque elements and with healthy BI-RADS A-type mammograms. It can also speed up the initial screening time by a mean of 4.5 s per image. Conclusions: A novel methodology is introduced that enables a quality-driven balancing of the WL/WW of mammographic images. This correction seeks the representation that maximizes the amount of graphical information contained in each image. The presented technique can contribute to the diagnosis and the overall efficiency of the breast screening session by suggesting, at the beginning, an optimal and customized windowing setting for each mammogram.
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Albiol, F., Corbi, A., & Albiol, A. (2019). Densitometric Radiographic Imaging With Contour Sensors. IEEE Access, 7, 18902–18914.
Abstract: We present the technical/physical foundations of a new imaging technique that combines ordinary radiographic information (generated by conventional X-ray settings) with the patient's volume to derive densitometric images. Traditionally, these images provide quantitative information about tissues densities. In our approach, they graphically enhance either soft or bony regions. After measuring the patient's volume with contour recognition devices, the physical traversed lengths within it (as the Roentgen beam intersects the patient) are calculated and pixel-wise associated with the original radiograph (X). In order to derive this map of lengths (L), the camera equations of the X-ray system and the contour sensor are determined. The patient's surface is also translated to the point-of-view of the X-ray beam and all its entrance/exit points are sought with the help of ray-casting methods. The derived L is applied to X as a physical operation (subtraction), obtaining soft tissue-(D-S) or bone-enhanced (D'(B)) figures. In the D-S type, the contained graphical information can be linearly mapped to the average electronic density (traversed by the X-ray beam). This feature represents an interesting proof-of-concept of associating density data to radiographs, but most important, their intensity histogram is objectively compressed, i.e., the dynamic range is more shrunk (compared against the corresponding X). This leads to other advantages: improvement in the visibility of border/edge areas (high gradient), extended manual window level/width manipulations during screening, and immediate correction of underexposed X instances. In the D-B' type, high-density elements are highlighted and easier to discern. All these results can be achieved with low-energy beam exposures, saving costs and dose. Future work will deepen this clinical side of our research. In contrast with other image-based modifiers, the proposed method is grounded on the measurement of a physical entity: the span of the X-ray beam within a body while undertaking a radiographic examination.
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