The LHCb experiment has recently started a new period of data taking after a major upgrade in both software and hardware. One of the biggest challenges has been the migration of the first part of the ...trigger system into a parallel GPU architecture framework called Allen, which performs a partial reconstruction of most of the LHCb sub-detectors. In Allen, the reconstruction of the Electromagnetic Calorimeter (ECAL) sub-detector is used in many selection algorithms, but its efficiency is currently 10% lower than the full reconstruction performed in the second stage of the trigger. In this work, we present a preliminary performance study of an alternative ECAL reconstruction algorithm implemented in Allen that complements the current algorithm to maximise the reconstruction efficiency and also minimise the impact on the throughput rate.
The LHCb’s Electromagentic Calorimeter (ECAL) measures the energy that any particle leaves behind when it travels through its sensors. However, with the current granularity, it is not possible to ...exploit the shape of the shower produced by the particle when it interacts with the ECAL, which is an information that could be enough to conclude what particle is being detected. In an attempt to find out whether it would be possible to classify them in future runs of the LHC, simulated data is generated with Geant4, giving an idea of what SPACAL, an updated version of the current calorimeter with better resolution, is capable of. Convolutional Neural Networks are applied so that the algorithm can understand the shapes and energy deposits produced by each kind of particle. Results obtained demonstrate that bigger resolution in ECAL allows over 95% precision in some classifications such as photons against neutrons.
The optimization of reconstruction algorithms has become a key aspect in LHCb as it is currently undergoing a major upgrade that will considerably increase the data processing rate. Aiming to ...accelerate the second most time consuming reconstruction process of the trigger, we propose an alternative reconstruction algorithm for the Electromagnetic Calorimeter of LHCb. Together with the use of deep learning techniques and the understanding of the current algorithm, our proposal decomposes the reconstruction process into small parts that benefit the generalized learning of small neural network architectures and simplifies the training dataset. This approach takes as input the full simulation data of the calorimeter and outputs a list of reconstructed clusters in a nearly constant time without any dependency in the event complexity.
The recent upgrade of the LHCb experiment pushes data processing rates up to 40 Tbit/s. Out of the whole reconstruction sequence, one of the most time consuming algorithms is the calorimeter data ...reconstruction. It aims at performing a clustering of the readout cells from the detector that belong to the same particle in order to measure its energy and position. This article presents a new algorithm for the calorimeter data reconstruction that makes use of graph data structures to optimise the clustering process, that will be denoted Graph Clustering. It outperforms the previously used method by
65.4
%
in terms of computational time on average, with an equivalent efficiency and resolution. The implementation of the Graph Clustering method is detailed in this article, together with its performance results inside the LHCb framework using simulation data.
Measurements of the cross section for producing b quarks in the reaction pp -> b (b) over barX are reported in 7 and 13 TeV collisions at the LHC as a function of the pseudorapidity. in the range 2 < ...eta < 5 covered by the acceptance of the LHCb experiment. The measurements are done using semileptonic decays of b-flavored hadrons decaying into a ground-state charmed hadron in association with a muon. The cross sections in the covered. range are 72.0 +/- 0.3 +/- 6.8 and 154.3 +/- 1.5 +/- 14.3 +/- mu b for 7 and 13 TeV. The ratio is 2.14 +/- 0.02 +/- 0.13, where the quoted uncertainties are statistical and systematic, respectively. The agreement with theoretical expectation is good at 7 TeV, but differs somewhat at 13 TeV. The measured ratio of cross sections is larger at lower eta than the model prediction.
The optimization of reconstruction algorithms has become a key aspect in the field of experimental particle physics. Since technology has allowed gradually increasing the complexity of the ...measurements, the amount of data taken that needs to be interpreted has grown as well. This is the case with the LHCb experiment at CERN, where a major upgrade currently undergoing will considerably increase the data processing rate. This has presented the need to search for specific reconstruction techniques that aim to accelerate one of the most time consuming reconstruction algorithms in LHCb, the electromagnetic calorimeter clustering. Together with the use of deep learning techniques and the understanding of the current reconstruction algorithm, we propose a method that decomposes the reconstruction process into small parts that can be formulated as a cellular automaton. This approach is shown to benefit the generalized learning of small convolutional neural network architectures and also simplify the training dataset. Final results applied to a complete LHCb simulation reconstruction are compatible in terms of efficiency, and execute in nearly constant time with independence on the complexity of the data.
The cross section for prompt antiproton production in collisions of protons with an energy of 6.5 TeV incident on helium nuclei at rest is measured with the LHCb experiment from a data set ...corresponding to an integrated luminosity of 0.5 nb − 1 . The target is provided by injecting helium gas into the LHC beam line at the LHCb interaction point. The reported results, covering antiproton momenta between 12 and 110 GeV / c , represent the first direct determination of the antiproton production cross section in p − He collisions, and impact the interpretation of recent results on antiproton cosmic rays from space-borne experiments.
The cross section for prompt antiproton production in collisions of protons with an energy of 6.5 TeV incident on helium nuclei at rest is measured with the LHCb experiment from a data set ...corresponding to an integrated luminosity of 0.5 nb − 1 . The target is provided by injecting helium gas into the LHC beam line at the LHCb interaction point. The reported results, covering antiproton momenta between 12 and 110 GeV / c , represent the first direct determination of the antiproton production cross section in p − He collisions, and impact the interpretation of recent results on antiproton cosmic rays from space-borne experiments.
The doubly charmed baryon decay Ξ + + c c → Ξ + c π + is observed for the first time, with a statistical significance of 5.9 σ , confirming a recent observation of the baryon in the Λ + c K − π + π + ...final state. The data sample used corresponds to an integrated luminosity of 1.7 fb − 1 , collected by the LHCb experiment in p p collisions at a center-of-mass energy of 13 TeV. The Ξ + + c c mass is measured to be 3620.6 ± 1.5 ( stat ) ± 0.4 ( syst ) ± 0.3 ( Ξ + c ) MeV / c 2 and is consistent with the previous result. The ratio of branching fractions between the decay modes is measured to be B ( Ξ + + c c → Ξ + c π + ) × B ( Ξ + c → p K − π + ) / B ( Ξ + + c c → Λ + c K − π + π + ) × B ( Λ + c → p K − π + ) = 0.035 ± 0.009 ( stat ) ± 0.003 ( syst )
The doubly charmed baryon decay Ξ + + c c → Ξ + c π + is observed for the first time, with a statistical significance of 5.9 σ , confirming a recent observation of the baryon in the Λ + c K − π + π + ...final state. The data sample used corresponds to an integrated luminosity of 1.7 fb − 1 , collected by the LHCb experiment in p p collisions at a center-of-mass energy of 13 TeV. The Ξ + + c c mass is measured to be 3620.6 ± 1.5 ( stat ) ± 0.4 ( syst ) ± 0.3 ( Ξ + c ) MeV / c 2 and is consistent with the previous result. The ratio of branching fractions between the decay modes is measured to be B ( Ξ + + c c → Ξ + c π + ) × B ( Ξ + c → p K − π + ) / B ( Ξ + + c c → Λ + c K − π + π + ) × B ( Λ + c → p K − π + ) = 0.035 ± 0.009 ( stat ) ± 0.003 ( syst )
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