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Dhani, P. K., Rodrigo, G., & Sborlini, G. F. R. (2023). Triple-collinear splittings with massive particles. J. High Energy Phys., 12(12), 188–20pp.
Abstract: We analyze in detail the most singular behaviour of processes involving triple-collinear splittings with massive particles in the quasi-collinear limit, and present compact expressions for the splitting amplitudes and the corresponding splitting kernels at the squared-amplitude level. Our expressions fully agree with well-known triple-collinear splittings in the massless limit, which are used as a guide to achieve the final expressions. These results are important to quantify dominant mass effects in many observables, and constitute an essential ingredient of current high-precision computational frameworks for collider phenomenology.
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Ramirez-Uribe, S., Hernandez-Pinto, R. J., Rodrigo, G., & Sborlini, G. F. R. (2022). From Five-Loop Scattering Amplitudes to Open Trees with the Loop-Tree Duality. Symmetry-Basel, 14(12), 2571–14pp.
Abstract: Characterizing multiloop topologies is an important step towards developing novel methods at high perturbative orders in quantum field theory. In this article, we exploit the Loop-Tree Duality (LTD) formalism to analyse multiloop topologies that appear for the first time at five loops. Explicitly, we open the loops into connected trees and group them according to their topological properties. Then, we identify a kernel generator, the so-called N7MLT universal topology, that allows us to describe any scattering amplitude of up to five loops. Furthermore, we provide factorization and recursion relations that enable us to write these multiloop topologies in terms of simpler subtopologies, including several subsets of Feynman diagrams with an arbitrary number of loops. Our approach takes advantage of many symmetries present in the graphical description of the original fundamental five-loop topologies. The results obtained in this article might shed light into a more efficient determination of higher-order corrections to the running couplings, which are crucial in the current and future precision physics program.
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Clemente, G., Crippa, A., Jansen, K., Ramirez-Uribe, S., Renteria-Olivo, A. E., Rodrigo, G., et al. (2023). Variational quantum eigensolver for causal loop Feynman diagrams and directed acyclic graphs. Phys. Rev. D, 108(9), 096035–19pp.
Abstract: We present a variational quantum eigensolver (VQE) algorithm for the efficient bootstrapping of the causal representation of multiloop Feynman diagrams in the loop-tree duality or, equivalently, the selection of acyclic configurations in directed graphs. A loop Hamiltonian based on the adjacency matrix describing a multiloop topology, and whose different energy levels correspond to the number of cycles, is minimized by VQE to identify the causal or acyclic configurations. The algorithm has been adapted to select multiple degenerated minima and thus achieves higher detection rates. A performance comparison with a Grover's based algorithm is discussed in detail. The VQE approach requires, in general, fewer qubits and shorter circuits for its implementation, albeit with lesser success rates.
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Martinez de Lejarza, J. J., Cieri, L., & Rodrigo, G. (2022). Quantum clustering and jet reconstruction at the LHC. Phys. Rev. D, 106(3), 036021–16pp.
Abstract: Clustering is one of the most frequent problems in many domains, in particular, in particle physics where jet reconstruction is central in experimental analyses. Jet clustering at the CERN's Large Hadron Collider (LHC) is computationally expensive and the difficulty of this task will increase with the upcoming High-Luminosity LHC (HL-LHC). In this paper, we study the case in which quantum computing algorithms might improve jet clustering by considering two novel quantum algorithms which may speed up the classical jet clustering algorithms. The first one is a quantum subroutine to compute a Minkowski-based distance between two data points, whereas the second one consists of a quantum circuit to track the maximum into a list of unsorted data. The latter algorithm could be of value beyond particle physics, for instance in statistics. When one or both of these algorithms are implemented into the classical versions of well-known clustering algorithms (K-means, affinity propagation, and k(T) -jet) we obtain efficiencies comparable to those of their classical counterparts. Even more, exponential speed-up could be achieved, in the first two algorithms, in data dimensionality and data length when the distance algorithm or the maximum searching algorithm are applied.
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Aparisi, J., Fuster, J., Irles, A., Rodrigo, G., Vos, M., Yamamoto, H., et al. (2022). m(b) at m(H): The Running Bottom Quark Mass and the Higgs Boson. Phys. Rev. Lett., 128(12), 122001–7pp.
Abstract: We present a new measurement of the bottom quark mass in the MS scheme at the renormalization scale of the Higgs boson mass from measurements of Higgs boson decay rates at the LHC: -0.31 GeV. The measurement has a negligible theory uncertainty and excellent prospects to improve at the HL-LHC and a future Higgs factory. Confronting this result and mb(mb) from low-energy measurements and mb(mZ) from Z-pole data, with the prediction of the scale evolution of the renormalization group equations, we find strong evidence for the “running” of the bottom quark mass.
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