Balbinot, R., & Fabbri, A. (2022). Quantum correlations across the horizon in acoustic and gravitational black holes. Phys. Rev. D, 105(4), 045010–20pp.
Abstract: We investigate, within the framework of quantum field theory in curved space, the correlations across the horizon of a black hole in order to highlight the particle-partner pair creation mechanism at the origin of Hawking radiation. The analysis concerns both acoustic black holes, formed by Bose-Einstein condensates, and gravitational black holes. More precisely, we have considered a typical acoustic black hole metric with two asymptotic homogeneous regions and the Schwarzschild metric as describing a gravitational black hole. By considering equal-time correlation functions, we find a striking disagreement between the two cases: the expected characteristic peak centered along the trajectories of the Hawking particles and their partners seems to appear only for the acoustic black hole and not for the gravitational Schwarzschild one. The reason for that is the existence of a quantum atmosphere displaced from the horizon as the locus of origin of Hawking radiation together, and this is the crucial aspect, with the presence of a central singularity in the gravitational case swallowing everything is trapped inside the horizon. Correlations, however, are not absent in the gravitational case; to see them, one simply has to consider correlation functions at unequal times, which indeed display the expected peak.
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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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Ramirez-Uribe, S., Renteria-Olivo, A. E., Rodrigo, G., Sborlini, G. F. R., & Vale Silva, L. (2022). Quantum algorithm for Feynman loop integrals. J. High Energy Phys., 05(5), 100–32pp.
Abstract: We present a novel benchmark application of a quantum algorithm to Feynman loop integrals. The two on-shell states of a Feynman propagator are identified with the two states of a qubit and a quantum algorithm is used to unfold the causal singular configurations of multiloop Feynman diagrams. To identify such configurations, we exploit Grover's algorithm for querying multiple solutions over unstructured datasets, which presents a quadratic speed-up over classical algorithms when the number of solutions is much smaller than the number of possible configurations. A suitable modification is introduced to deal with topologies in which the number of causal states to be identified is nearly half of the total number of states. The output of the quantum algorithm in IBM Quantum and QUTE Testbed simulators is used to bootstrap the causal representation in the loop-tree duality of representative multiloop topologies. The algorithm may also find application and interest in graph theory to solve problems involving directed acyclic graphs.
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Cranmer, K. et al, & Sanz, V. (2022). Publishing statistical models: Getting the most out of particle physics experiments. SciPost Phys., 12(1), 037–55pp.
Abstract: The statistical models used to derive the results of experimental analyses are of incredible scientific value and are essential information for analysis preservation and reuse. In this paper, we make the scientific case for systematically publishing the full statistical models and discuss the technical developments that make this practical. By means of a variety of physics cases – including parton distribution functions, Higgs boson measurements, effective field theory interpretations, direct searches for new physics, heavy flavor physics, direct dark matter detection, world averages, and beyond the Standard Model global fits – we illustrate how detailed information on the statistical modelling can enhance the short- and long-term impact of experimental results.
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Briz, J. A., Nerio, A. N., Ballesteros, C., Borge, M. J. G., Martinez, P., Perea, A., et al. (2022). Proton Radiographs Using Position-Sensitive Silicon Detectors and High-Resolution Scintillators. IEEE Trans. Nucl. Sci., 69(4), 696–702.
Abstract: Proton therapy is a cancer treatment technique currently in growth since it offers advantages with respect to conventional X-ray and gamma-ray radiotherapy. In particular, better control of the dose deposition allowing to reach higher conformity in the treatments causing less secondary effects. However, in order to take full advantage of its potential, improvements in treatment planning and dose verification are required. A new prototype of proton computed tomography scanner is proposed to design more accurate and precise treatment plans for proton therapy. Our prototype is formed by double-sided silicon strip detectors and scintillators of LaBr3(Ce) with high energy resolution and fast response. Here, the results obtained from an experiment performed using a 100-MeV proton beam are presented. Proton radiographs of polymethyl methacrylate (PMMA) samples of 50-mm thickness with spatial patterns in aluminum were taken. Their properties were studied, including reproduction of the dimensions, spatial resolution, and sensitivity to different materials. Structures of up to 2 mm are well resolved and the sensitivity of the system was enough to distinguish the thicknesses of 10 mm of aluminum or PMMA. The spatial resolution of the images was 0.3 line pairs per mm (MTF-10%). This constitutes the first step to validate the device as a proton radiography scanner.
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