Добірка наукової літератури з теми "L1 trigger"

One of the major challenges for the Compact Muon Solenoid (CMS)experiment, is the task of reducing event rate from roughly 40 MHz down to a more manageable 1 kHz while keeping as many interesting physics events as possible. This is accomplished through the use of a Level-1 (L1) hardware based trigger as well as a software based High-Level Trigger (HLT). Monitoring and understanding the output rates of the L1 and HLT triggers is of key importance for determining the overall performance of the trigger system and is intimately tied to what type of data is being recorded for physics analyses. We present here a collection of tools used by CMS to monitor the L1 and HLT trigger rates. One of these tools is a script (run in the CMS control room) that gives valuable real-time feedback of trigger rates to the shift crew. Another useful tool is a plotting library, that is used for observing how trigger rates vary over a range of beam and detector conditions, in particular how the rates of individual triggers scale with event pile-up.

Abstract The ATLAS trigger system includes a Level-1 (L1) trigger based on custom electronics and firmware, and a high-level software trigger running on off-the-shelf hardware. The L1 trigger system uses information from the forward detectors, the calorimeters and the muon trigger detectors. Once information from all muon trigger sectors has been received, trigger candidate multiplicities are calculated by the Muon-to-Central-Trigger-Processor Interface (MUCTPI). Muon multiplicity information is sent to the Central-Trigger-Processor (CTP) and trigger objects are sent to the L1 Topological Trigger Processor (L1Topo). The CTP combines the information received from the MUCTPI with the trigger information from the forward detectors, the calorimeters and the L1Topo, and takes the L1 trigger decision. As part of the ATLAS L1 trigger system upgrade for Run-3 of the Large Hadron Collider (LHC) a new MUCTPI has been designed and commissioned. We discuss the commissioning and operation of the new MUCTPI used in ATLAS from the beginning of Run-3. In particular, we describe the integration tests which have been carried out for the commissioning and operation of the new MUCTPI.

Abstract The CMS experiment at CERN uses a two-stage triggering system composed of the Level-1 (L1), instrumented with custom-designed hardware boards with an output rate of 100 kHz, and the High Level Trigger (HLT), streamlined version of the offline software reconstruction that runs on the computing farm, allowing to store around 1.5 kHz of rate. New trigger algorithms and new features, as well as optimized trigger menus at both L1 and HLT are mandatory in order to be able to successfully record the events at higher data loads due to increasing luminosity and pileup at the LHC in Run 3. Many measurements and searches will profit from the updates implemented in the CMS trigger. The highlights of Run 2 CMS trigger results will be presented, together with the improvements for Run 3.

During its second run of operation (Run 2), started in 2015, the LHC will deliver a peak instantaneous luminosity that may reach 2 · 1034 cm-2s-1 with an average pileup of about 55, far larger than the design value. Under these conditions, the online event selection is a very challenging task. In CMS, it is realized by a two-level trigger system: the Level-1 (L1) Trigger, implemented in custom-designed electronics, and the High Level Trigger (HLT), a streamlined version of the offine reconstruction software running on a computer farm. In order to face this challenge, the L1 trigger has been through a major upgrade compared to Run 1, whereby all electronic boards of the system have been replaced, allowing more sophisticated algorithms to be run online. Its last stage, the global trigger, is now able to perform complex selections and to compute high-level quantities, like invariant masses. Likewise, the algorithms that run in the HLT have been greatly improved; in particular, new approaches for the online track reconstruction lead to a drastic reduction of the computing time, and to much improved performances. This document will describe the performance of the upgraded trigger system in Run 2.

The CMS collaboration is building a high-granularity calorimeter (HGCAL) for the endcap regions as part of its planned upgrade for the High-Luminosity LHC. The calorimetric data will form part of the Level-1 trigger (hardware) of the CMS experiment, reducing the event rate from the nominal 40 MHz to 750 kHz with a decision time (latency) of 12.5 microseconds. In addition to basic tracking information, which will also be available in the Level-1 trigger system, the use of particle-flow techniques will be facilitated as part of the trigger system. Around 1-million “trigger channels” are read at 40 MHz from the HGCAL, presenting a significant challenge in terms of data manipulation and processing for the trigger system: the trigger data volumes will be an order of magnitude above those currently handled at CMS. In addition, the high luminosity will result in an average of 140 (or more) interactions per bunch crossing that produce a huge background rate in the forward region and these will need to be efficiently rejected by the trigger algorithms. Furthermore, the reconstruction of particle clusters used for particle flow in high hit-rate events presents a complex computational problem associated with the trigger. We present the status of the trigger architecture and design, as well as the algorithmic concepts needed in order to tackle these major issues.

The High-Luminosity LHC (HL-LHC) provides an opportunity for a pioneering physics program to harness an integrated luminosity of 4000 fb−1 of ten years of operations. This large volume of collision data will help in high precision measurements of the Standard Model (SM) and the search for new and rare physics phenomena. The harsh environment of 200 proton–proton interactions poses a substantial challenge in the collection of these large datasets. The HL-LHC CMS Level-1 (L1) trigger, including the calorimeter trigger, will receive a massive upgrade to tackle the challenge of a high-bandwidth and high pileup environment. The L1 trigger is planned to handle a very high bandwidth (∼63 Tb/s) with an output rate of 750 kHz, and the desired latency budget is 12.5 μs. The calorimeter trigger aims to process the high-granular information from the new end-cap detector called the high-granularity calorimeter (HGCAL) and the barrel calorimeter. The HL-LHC trigger prototyped boards are equipped with large modern-day FPGAs and high-speed optical links (∼28 Gb/s), which helps in the parallel and rapid computation of the calorimeter trigger algorithms. This article discusses the proposed design and expected performance of the upgraded CMS Level-1 calorimeter trigger system.

Cette thèse présente l'étude de la production de paire de bosons de Higgs (HH) dans l'état final avec une paire de quarks b et une paire de leptons τ (bbττ), en exploitant les données de collisions proton-proton collectées à 13TeV d’énergie de centre de masse avec le détecteur CMS au grand collisionneur de hadrons (LHC) du CERN, correspondant à 138fb-1 accumulée pendant la période de prise de données Run2 (2015-2018). Le canal de désintégration bbττ offre un compromis entre le rapport d’embranchement (7,3%) et la pureté de sélection des τ, garantissant un bon rejet du bruit de fond. L'étude de la production de HH permet d’étudier l’auto-couplage du boson de Higgs (λ3h) qui, dans le modèle standard (SM), est le seul paramètre prédit par la théorie qui régit la forme du potentiel du Higgs; par conséquent, une mesure de λ3h est un test de la validité du SM et nous permet d’étudier le processus de brisure de symétrie électrofaible. Dans le théories au-delà du SM (BSM) - avec un intérêt particulier pour les théories effectives - λ3h peut prendre des valeurs plus grandes que prédit par le SM, augmentant la section efficace de production de HH. La mesure des écarts par rapport à la prédiction du SM ouvrirait la voie à une nouvelle ère de la physique. Les limites supérieures sur le signal sont fixées à 95% de niveau de confiance (CL) correspondant à environ 3 et 124 fois le SM pour σ(gg->HH) et σ(qq->HH), respectivement. Les résultats sont également interprétés dans le contexte de 20 scénarios BSM pour lesquels des limites à 95% de CL sont fixées. Le contexte expérimental de cette thèse est la reprise des opérations du LHC en 2022 pour sa phase Run3, une nouvelle phase de collisions à 13.6TeV d’énergie et luminosité instantanée de 2-2,2x10^34cm-2s-1. Pendant le Run3, les capacités du déclencheur de niveau 1 (L1T) du CMS restent inchangées par rapport au Run2, nécessitant le développement d'approches plus complexes pour optimiser les algorithmes disponibles, garantissant le succès du programme de physique de CMS. L'optimisation de la section L1T qui exploite les informations calorimétriques est particulièrement intéressante. Dans cette thèse, une nouvelle méthode d'apprentissage automatique, basée sur un réseau de neurones, a été développée pour l’étalonnage des dépôts d'énergie du calorimètre dans le L1T; elle exploite les données pour l'étalonnage des objets détecteurs individuels et ses performances sont évaluées par rapport à la reconstruction hors ligne des électrons et jets. Les informations calorimétriques sont ensuite utilisées par l'algorithme pour la reconstruction et l'identification des leptons τ se désintégrant hadroniquement (τh), dont l'optimisation pour Run3 est réalisée dans cette thèse en utilisant une approche nouvelle; les performances de cette approche sont évaluées à l'aide des événements Z->ττ collectés en 2022. Parallèlement, la collaboration CMS s'efforce de réaliser son programme de mise à niveau Phase2, destiné à poursuivre au programme de physique du haute luminosité LHC (HL-LHC). Le volume accru de données collectées par le HL-LHC assurera la puissance statistique pour l'étude de λ3h et éventuellement sa mesure; en revanche, la luminosité instantanée accrue exigera le remplacement complet du L1T par un hardware basé sur des field programmable gate arrays (FPGAs) plus performant pour la collecte efficace des données. Pour exploiter au maximum les capacités des FPGA, un nouvel algorithme d'apprentissage automatique pour la reconstruction, l'identification et l'étalonnage des candidats τh dans le L1T a été développé dans cette thèse. Cet algorithme exploite des réseaux de neurones convolutifs implémentés dans un FPGA et assure des performances accrues par rapport aux approches standard. Tout le progrès technique développé dans cette thèse à pour but d’améliorer la sensibilité des analyses CMS à la mesure de l’auto-couplage du boson de Higgs au cours des opérations actuelles et futures du LHC
This thesis presents the study of the Higgs boson pair (HH) production in the final state with a pair of b quarks and a pair of τ leptons (bbττ), exploiting proton-proton collisions data collected at 13 TeV centre-of-mass energy with the CMS detector at the CERN large hadron collider (LHC), corresponding to 138fb-1 accumulated during the Run2 data-taking period (2015-2018). The bbττ decay channel gives a good trade-off between a sizable branching fraction (7.3%) and the purity of the τ selection, ensuring the good rejection of the background contributions. The study of HH production gives access to the measurement of the Higgs boson self-coupling (λ3h). In the context of the standard model (SM), this coupling is the only parameter governing the shape of the Higgs potential and it is precisely predicted by the theory; therefore a measurement of λ3h is a test of the validity of the SM and allows us to shed light on the process of electroweak symmetry breaking. In the context of beyond the SM (BSM) theories - with a particular interest in effective field theories - λ3h can assume values larger than that predicted by the SM, greatly enhancing the HH production cross section; the measurement of deviations from the SM prediction would open the road to yet another new era of physics. Upper limits on the SM signal are set at 95% confidence level (CL) to be around 3 and 124 times the SM for σ(gg->HH) and σ(qq->HH), respectively. The results are also interpreted in the context of 20 different independent BSM scenarios for which 95% CL limits are set. The experimental context of this thesis is the restart of LHC operations in 2022 for its Run3, a new phase with collisions at an energy of 13.6 TeV and instantaneous luminosity of 2-2.2x10^34cm-2s-1. In Run3, the hardware capabilities of the CMS Level-1 trigger (L1T) are unchanged with respect to Run2. This requires the development of bolder and more sophisticated approaches to optimise available algorithms, to guarantee the success of the CMS physics program. Especially interesting is the optimisation of the L1T section that exploits calorimetric information. As part of this thesis a new machine learning method, based on a neural network, has been developed for the calibration applied in the L1T to calorimeter energy deposits; it exploits data for the calibration of single detector objects and its promising performance is evaluated against the offline reconstruction of electrons and hadronic jets. The calorimetric information is then optimally used by the algorithm for the reconstruction and identification of hadronically decaying τ leptons (τh), whose optimisation for the Run3 is performed in this thesis employing a new, simple, and more informative approach; the performance of this approach is evaluated using Z->ττ events collected during 2022. At the same time, the CMS collaboration is striving for its Phase2 upgrade program, which is intended to match the ambitious High-Luminosity LHC (HL-LHC) physics program, starting in 2029. The considerably increased volume of data collected by the HL-LHC will ensure the statistical power for the detailed study of λ3h and possibly its measurement; on the other hand, the larger instantaneous luminosity will require the full replacement of the L1T with hardware of increased capabilities based on state-of-the-art field programmable gate arrays (FPGAs) to efficiently collect data. To exploit the FPGA capabilities to the maximum, a new machine learning algorithm for the reconstruction, identification, and calibration of τh candidates in the L1T has been developed as part of this thesis. This algorithm exploits convolutional neural networks implemented in FPGA firmware and ensures largely enhanced performance compared to standard approaches. All the technical advancement developed within this thesis has one goal: improving the sensitivity of CMS analyses to the measurement of the Higgs boson self-coupling during the ongoing and future Runs of the LHC

The present research explores the interactional nature of oral tasks carried out in two types of learner dyads in terms of their likelihood to foster negotiation for meaning during Language Related Episodes (LREs). Quantitative data analyses reveal how learners in same L1 dyads, Spanish English as a Foreign Language (EFL) learners, and in different L1 dyads, Canadian learners of Spanish and Spanish learners of English participating in a virtual exchange, modify their speech using negotiations and clarifications to make it comprehensible to their interlocutors. Eighteen different L1 dyads of university learners doing a virtual exchange (Canada-Spain) and eighteen dyads of Spanish-speakers learning English at the Spanish university carried out three oral communicative tasks online following the same procedures. Data were transcribed, LREs were identified, quantified for each dyad, and analyzed to determine their characteristics in terms of types of triggers, modified output, and type of feedback provided. Initial findings point to substantial differences in meaning negotiation occurring during LREs in each group. Different-L1 dyads exhibit more clarifications, meaning negotiation, and provide more feedback, which leads to higher amounts of comprehensible and modified output than learners in same L1 dyads.

The aim of this study is to analyze the writing errors made by first-year undergraduate Medicine students in the English classroom at a Spanish university. Forty-nine subjects enrolled in the English for Health Sciences module were expected to subtitle short videos not only implementing medical vocabulary seen in previous lessons, but also using Open Educational Resources (OERs), more specifically Bombay TV. This online tool allows learners to practise and develop their writing skills in the target language as well as their autonomy and creativity. Results showed that the most frequently committed errors were, in order, punctuation, spelling, wrong verb choice, wrong word choice, pronouns, fragment, word order, articles, verb tense, subject-verb agreement, nouns, prepositions, capitalization and adjectives respectively. Moreover, the negative transfer of students’ first language (L1) occasionally resulted in a lack of grammar and vocabulary accuracy that should be taken into account in order to enhance students’ writing competence in the target language. A final questionnaire revealed that the use of new technologies in the foreign language classroom triggered students’ motivation. Likewise, students are provided with corrective feedback, a practice used in the field of education. Given that OERs are key in the study, the focus will be on online feedback.