2,049 research outputs found

    The control systems of the CMS Pixel detector

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    Comparison of Bayesian and particle swarm algorithms for hyperparameter optimisation in machine learning applications in high energy physics

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    When using machine learning (ML) techniques, users typically need to choose a plethora of algorithm-specific parameters, referred to as hyperparameters. In this paper, we compare the performance of two algorithms, particle swarm optimisation (PSO) and Bayesian optimisation (BO), for the autonomous determination of these hyperparameters in applications to different ML tasks typical for the field of high energy physics (HEP). Our evaluation of the performance includes a comparison of the capability of the PSO and BO algorithms to make efficient use of the highly parallel computing resources that are characteristic of contemporary HEP experiments.Comment: Accepted by Computer Physics Communications. Changes made compared to previous version: added references to other strategies, added Zenodo entry for the implemented software, added a brief description of PSO, added more explanations regarding the benchmark task

    Evolutionary algorithms for hyperparameter optimization in machine learning for application in high energy physics

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    The analysis of vast amounts of data constitutes a major challenge in modern high energy physics experiments. Machine learning (ML) methods, typically trained on simulated data, are often employed to facilitate this task. Several choices need to be made by the user when training the ML algorithm. In addition to deciding which ML algorithm to use and choosing suitable observables as inputs, users typically need to choose among a plethora of algorithm-specific parameters. We refer to parameters that need to be chosen by the user as hyperparameters. These are to be distinguished from parameters that the ML algorithm learns autonomously during the training, without intervention by the user. The choice of hyperparameters is conventionally done manually by the user and often has a significant impact on the performance of the ML algorithm. In this paper, we explore two evolutionary algorithms: particle swarm optimization (PSO) and genetic algorithm (GA), for the purposes of performing the choice of optimal hyperparameter values in an autonomous manner. Both of these algorithms will be tested on different datasets and compared to alternative methods.Comment: Corrected typos. Removed a remark on page 2 regarding the similarity of minimization and maximization problem. Removed a remark on page 9 (Summary) regarding thee ANN, since this was not studied in the pape

    Forward pi^0 Production and Associated Transverse Energy Flow in Deep-Inelastic Scattering at HERA

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    Deep-inelastic positron-proton interactions at low values of Bjorken-x down to x \approx 4.10^-5 which give rise to high transverse momentum pi^0 mesons are studied with the H1 experiment at HERA. The inclusive cross section for pi^0 mesons produced at small angles with respect to the proton remnant (the forward region) is presented as a function of the transverse momentum and energy of the pi^0 and of the four-momentum transfer Q^2 and Bjorken-x. Measurements are also presented of the transverse energy flow in events containing a forward pi^0 meson. Hadronic final state calculations based on QCD models implementing different parton evolution schemes are confronted with the data.Comment: 27 pages, 8 figures and 3 table

    Performance of the CMS Cathode Strip Chambers with Cosmic Rays

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    The Cathode Strip Chambers (CSCs) constitute the primary muon tracking device in the CMS endcaps. Their performance has been evaluated using data taken during a cosmic ray run in fall 2008. Measured noise levels are low, with the number of noisy channels well below 1%. Coordinate resolution was measured for all types of chambers, and fall in the range 47 microns to 243 microns. The efficiencies for local charged track triggers, for hit and for segments reconstruction were measured, and are above 99%. The timing resolution per layer is approximately 5 ns

    IGLV3-21*01 is an inherited risk factor for CLL through the acquisition of a single-point mutation enabling autonomous BCR signaling

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    The prognosis of chronic lymphocytic leukemia (CLL) depends on different markers, including cytogenetic aberrations, oncogenic mutations, and mutational status of the immunoglobulin (Ig) heavy-chain variable (IGHV) gene. The number of IGHV mutations distinguishes mutated (M) CLL with a markedly superior prognosis from unmutated (UM) CLL cases. In addition, B cell antigen receptor (BCR) stereotypes as defined by IGHV usage and complementarity-determining regions (CDRs) classify ‚ąľ30% of CLL cases into prognostically important subsets. Subset 2 expresses a BCR with the combination of IGHV3-21-derived heavy chains (HCs) with IGLV3-21-derived light chains (LCs), and is associated with an unfavorable prognosis. Importantly, the subset 2 LC carries a single-point mutation, termed R110, at the junction between the variable and constant LC regions. By analyzing 4 independent clinical cohorts through BCR sequencing and by immunophenotyping with antibodies specifically recognizing wild-type IGLV3-21 and R110-mutated IGLV3-21 (IGLV3-21R110), we show that IGLV3-21R110-expressing CLL represents a distinct subset with poor prognosis independent of IGHV mutations. Compared with other alleles, only IGLV3-21*01 facilitates effective homotypic BCR-BCR interaction that results in autonomous, oncogenic BCR signaling after acquiring R110 as a single-point mutation. Presumably, this mutation acts as a standalone driver that transforms IGLV3-21*01-expressing B cells to develop CLL. Thus, we propose to expand the conventional definition of CLL subset 2 to subset 2L by including all IGLV3-21R110-expressing CLL cases regardless of IGHV mutational status. Moreover, the generation of monoclonal antibodies recognizing IGLV3-21 or mutated IGLV3-21R110 facilitates the recognition of B cells carrying this mutation in CLL patients or healthy donors

    Performance and Operation of the CMS Electromagnetic Calorimeter