ML-assisted versatile approach to Calorimeter R&D
Advanced detector R&D for both new and ongoing experiments in HEP requires performing computationally intensive and detailed simulations as part of the detector-design optimisation process. We propose a versatile approach to this task that is based on machine learning and can substitute the most computationally intensive steps of the process while retaining the GEANT4 accuracy to details. The approach covers entire detector representation from the event generation to the evaluation of the physics performance. The approach allows the use of arbitrary modules arrangement, different signal and background conditions, tunable reconstruction algorithms, and desired physics performance metrics. While combined with properties of detector and electronics prototypes obtained from beam tests, the approach becomes even more versatile. We focus on the Phase II Upgrade of the LHCb Calorimeter under the requirements on operation at high luminosity. We discuss the general design of the approach and particular estimations, including spatial and energy resolution for the future LHCb Calorimeter setup at different pile-up conditions.
We present a model for freight train time prediction based on station network analysis and specific feature engineering. We discuss the first pipeline to improve the freight flight duration prediction in Russia. While every freight company use only reference book made by RZD (Russian Railways) based on railroad distances with accuracy measured in days, we argue that one could predict the flight duration with error less than twenty hours while decreasing error to twelve hours for certain type of freight trains.
The law of accelerating returns can be viewed as a concept that describes acceleration of technological progress. The idea is that tools are used for developing more advanced tools that are applied for creating even more advanced tools etc. A similar idea has been implemented in algorithms for advancing artificial intelligence. In this paper, the results of applying these algorithms in games are discussed. Nevertheless, real life tasks seem more complicated. The game theoretic approach can be applied for transition from theoretical and unrealistic games to more complex and practical tasks. Applications of the game theoretic approach to advance artificial intelligence in solving tasks in the credit industry are proposed.
Proceedings of Machine Learning Research: Volume 97: International Conference on Machine Learning, 9-15 June 2019, Long Beach, California, USA
One of the most challenging data analysis tasks of modern High Energy Physics experiments is the identification of particles. In this proceedings we review the new approaches used for particle identification at the LHCb experiment. Machine-Learning based techniques are used to identify the species of charged and neutral particles using several observables obtained by the LHCb sub-detectors. We show the performances of various solutions based on Neural Network and Boosted Decision Tree models.
Reconstruction and identification in calorimeters of modern High Energy Physics experiments is a complicated task. Solutions are usually driven by a priori knowledge about expected properties of reconstructed objects. Such an approach is also used to distinguish single photons in the electromagnetic calorimeter of the LHCb detector on LHC from overlapping photons produced from high momentum pi0 decays. We studied an alternative solution based on applying machine learning techniques to primary calorimeter information, that are energies collected in individual cells around the energy cluster. Constructing such a discriminator from “first principles” allowed improve separation performance from 80% to 93%, that means reducing primary photons fake rate by factor of two. In presentation we discuss different approaches to the problem, architecture of the classifier, its optimization, and compare performance of the ML approach with classical one.
A full amplitude analysis of Λ 0 b → J/ψ pπ− decays is performed with a data sample acquired with the LHCb detector from 7 and 8 TeV pp collisions, corresponding to an integrated luminosity of 3 fb−1 . A significantly better description of the data is achieved when, in addition to the previously observed nucleon excitations N → pπ−, either the Pc(4380)+ and Pc(4450)+ → J/ψ p states, previously observed in Λ 0 b → J/ψ pK− decays, or the Zc(4200)− → J/ψ π− state, previously reported in B0 → J/ψ K+π − decays, or all three, are included in the amplitude models. The data support a model containing all three exotic states, with a significance of more than three standard deviations. Within uncertainties, the data are consistent with the Pc(4380)+ and Pc(4450)+ production rates expected from their previous observation taking account of Cabibbo suppression.
The production of W and Z bosons in association with jets is studied in the forward region of proton-proton collisions collected at a centre-of-mass energy of 8 TeV by the LHCb experiment, corresponding to an integrated luminosity of 1.98 ± 0.02 fb−1 . The W boson is identified using its decay to a muon and a neutrino, while the Z boson is identified through its decay to a muon pair. Total cross-sections are measured and combined into charge ratios, asymmetries, and ratios of W+jet and Z+jet production cross-sections. Differential measurements are also performed as a function of both boson and jet kinematic variables. All results are in agreement with Standard Model predictions.