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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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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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Sakai, S., Oset, E., & Ramos, A. (2018). Triangle singularities in B- -> K- pi- D(s0)+ and B- -> K- pi- D(s1)+. Eur. Phys. J. A, 54(1), 10–14pp.
Abstract: We study the appearance of structures in the decay of the B- into K-pi D--(s0)+ (2317) and K-pi D--(s1)+ (2460) final states by forming invariant mass distributions of pi D--(s0)+ and pi D--(s1)+ pairs, respectively. The structure in the distribution is associated to the kinematical triangle singularity that appears when the B- -> K- K*(0) D-0 (B- -> K- K*(0) D*(0)) decay process is followed by the decay of the K*(0) into pi(-) K+ and the subsequent rescattering of the K+ D-0 (K+ D*(0)) pair forming the D-s0(+) (2317) (D-s1(+) (2460)) resonance. We find this type of non-resonant peaks at 2850MeV in the invariant mass of pi D--(s0) pairs from B- -> K- pi(-) D-s0(+) (2317) decays and around 3000MeV in the invariant mass of pi D--(s1)+ pairs from B- -> K- pi(-) D-s1(+)(2460) decays. By employing the measured branching ratios of the B- -> K- K*(0) D-0 and B- -> K- K*(0) D*(0) decays, we predict the branching ratios for the processes B- into K-pi D--(s0)+ (2317) K-pi D--(s1)+ (2460), in the vicinity of the triangle singularity peak, to be about 8 x 10(-6) and 1 x 10(-6), respectively. The observation of this reaction would also give extra support to the molecular picture of the D-s0(+)(2317) and D-s1(+)(2460).
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