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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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Tolos, L., Cabrera, D., Garcia-Recio, C., Molina, R., Nieves, J., Oset, E., et al. (2013). Strangeness and charm in nuclear matter. Nucl. Phys. A, 914, 461–471.
Abstract: The properties of strange (K, (K) over bar and (K) over bar*) and open-charm (D, (D) over bar and D*) mesons in dense matter are studied using a unitary approach in coupled channels for meson-baryon scattering. In the strangeness sector, the interaction with nucleons always comes through vector-meson exchange, which is evaluated by chiral and hidden gauge Lagrangians. For the interaction of charmed mesons with nucleons we extend the SU(3) Weinberg-Tomozawa Lagrangian to incorporate spin-flavor symmetry and implement a suitable flavor symmetry breaking. The in-medium solution for the scattering amplitude accounts for Pauli blocking effects and meson self-energies. On one hand, we obtain the K, (K) over bar and (K) over bar* spectral functions in the nuclear medium and study their behaviour at finite density, temperature and momentum. We also make an estimate of the transparency ratio of the gamma A -> K+ K*(-) A' reaction, which we propose as a tool to detect in-medium modifications of the (K) over bar* meson. On the other hand, in the charm sector, several resonances with negative parity are generated dynamically by the s-wave interaction between pseudoscalar and vector meson multiplets with 1/2(+) and 3/2(+) baryons. The properties of these states in matter are analyzed and their influence on the open-charm meson spectral functions is studied. We finally discuss the possible formation of D-mesic nuclei at FAIR energies.
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Hernandez, P., Pena, C., Ramos, A., & Gomez-Cadenas, J. J. (2021). A new formulation of compartmental epidemic modelling for arbitrary distributions of incubation and removal times. PLoS One, 16(2), e0244107–22pp.
Abstract: The paradigm for compartment models in epidemiology assumes exponentially distributed incubation and removal times, which is not realistic in actual populations. Commonly used variations with multiple exponentially distributed variables are more flexible, yet do not allow for arbitrary distributions. We present a new formulation, focussing on the SEIR concept that allows to include general distributions of incubation and removal times. We compare the solution to two types of agent-based model simulations, a spatially homogeneous one where infection occurs by proximity, and a model on a scale-free network with varying clustering properties, where the infection between any two agents occurs via their link if it exists. We find good agreement in both cases. Furthermore a family of asymptotic solutions of the equations is found in terms of a logistic curve, which after a non-universal time shift, fits extremely well all the microdynamical simulations. The formulation allows for a simple numerical approach; software in Julia and Python is provided.
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Feijoo, A., Magas, V. K., Ramos, A., & Oset, E. (2016). A hidden-charm S =-1 pentaquark from the decay Lambda(b) into J/psi eta Lambda states. Eur. Phys. J. C, 76(8), 446–12pp.
Abstract: The hidden-charm pentaquark P-c(4450) observed recently by the LHCb collaboration may be of molecular nature, as advocated by some unitary approaches that also predict pentaquark partners in the strangeness S = -1 sector. In this work we argue that a hidden-charm strange pentaquark could be seen from the decay of the Lambda b, just as in the case of the non-strange P-c(4450), but looking into the J/psi eta Lambda decay mode and forming the invariant mass spectrum of J/psi Lambda pairs. In the model presented here, which assumes a standard weak decay topology and incorporates the hadronization process and final-state interaction effects, we find the J/psi eta Lambda final states to be populated with similar strength as the J/psi K- p states employed for the observation of the non-strange pentaquark. This makes the Lambda b -> J/psi eta Lambda decay to be an interesting process to observe a possible strange partner of the P-c(4450). We study the dependence of the J/psi Lambda mass spectra on various model ingredients and on the unknown properties of the strange pentaquark.
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Dorigo, T. et al, Ramos, A., & Ruiz de Austri, R. (2023). Toward the end-to-end optimization of particle physics instruments with differentiable programming. Rev. Phys., 10, 100085– pp.
Abstract: The full optimization of the design and operation of instruments whose functioning relies on the interaction of radiation with matter is a super-human task, due to the large dimensionality of the space of possible choices for geometry, detection technology, materials, data-acquisition, and information-extraction techniques, and the interdependence of the related parameters. On the other hand, massive potential gains in performance over standard, “experience-driven” layouts are in principle within our reach if an objective function fully aligned with the final goals of the instrument is maximized through a systematic search of the configuration space. The stochastic nature of the involved quantum processes make the modeling of these systems an intractable problem from a classical statistics point of view, yet the construction of a fully differentiable pipeline and the use of deep learning techniques may allow the simultaneous optimization of all design parameters.
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