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Spectroscopy studies from the ATLAS and CMS experiments
No full textRecent spectroscopy results from the ATLAS and CMS experiments at the LHC are shedding new light on the hadronic spectrum. These include the first precise measurements of the excited vector mesons , , and , alongside the observation of a new family of tetraquark candidates in the di- channel. CMS has further determined the spin-parity quantum numbers of this new family to be . Both collaborations have extended these studies to other decay channels, such as . Collectively, these results underscore the LHC's unparalleled capability to explore the spectrum of hadronic states, providing crucial insights into non-perturbative QCD and the quark model
CMS outreach overview
No full textEffective scientific communication and public outreach are essential components of modern high-energy physics research, serving to educate the public, engage stakeholders, and ensure continued support for fundamental science.
This article presents a comprehensive analysis of the communication and outreach strategies implemented by the Compact Muon Solenoid (CMS) experiment at CERN, one of the flagship experiments at the Large Hadron Collider (LHC). The CMS communications program demonstrates how large-scale scientific collaborations can effectively reach diverse audiences through multi-platform engagement, education-al initiatives, and innovative digital content creation
Identification of tau leptons using a convolutional neural network with domain adaptation
No full textA tau lepton identification algorithm, DEEPTAU, based on convolutional neural network techniques, has been developed in the CMS experiment to discriminate reconstructed hadronic decays of tau leptons () from quark or gluon jets and electrons and muons that are misreconstructed as candidates. The latest version of this algorithm, v2.5, includes domain adaptation by backpropagation, a technique that reduces discrepancies between collision data and simulation in the region with the highest purity of genuine candidates. Additionally, a refined training workflow improves classification performance with respect to the previous version of the algorithm, with a reduction of 30-50% in the probability for quark and gluon jets to be misidentified as candidates for given reconstruction and identification efficiencies. This paper presents the novel improvements introduced in the DEEPTAU algorithm and evaluates its performance in LHC proton-proton collision data at 13 and 13.6 TeV collected in 2018 and 2022 with integrated luminosities of 60 and 35 fb, respectively. Techniques to calibrate the performance of the identification algorithm in simulation with respect to its measured performance in real data are presented, together with a subset of results among those measured for use in CMS physics analyses.A tau lepton identification algorithm, DeepTau, based on convolutional neural network techniques, has been developed in the CMS experiment to discriminate reconstructed hadronic decays of tau leptons () from quark or gluon jets and electrons and muons that are misreconstructed as candidates. The latest version of this algorithm, v2.5, includes domain adaptation by backpropagation, a technique that reduces discrepancies between collision data and simulation in the region with the highest purity of genuine candidates. Additionally, a refined training workflow improves classification performance with respect to the previous version of the algorithm, with a reduction of 3050% in the probability for quark and gluon jets to be misidentified as candidates for given reconstruction and identification efficiencies. This paper presents the novel improvements introduced in the DeepTau algorithm and evaluates its performance in LHC proton-proton collision data at = 13 and 13.6 TeV collected in 2018 and 2022 with integrated luminosities of 60 and 35 fb, respectively. Techniques to calibrate the performance of the identification algorithm in simulation with respect to its measured performance in real data are presented, together with a subset of results among those measured for use in CMS physics analyses
GAN-based fast calorimeter simulation of the ATLAS experiment
No full textDetector simulation uses a large part (about 40%) of the computational resources of the ATLAS Experiment at the LHC, with the largest fraction dedicated to simulation of calorimeters. Machine Learning-based systems have been developed to simulate calorimeter response faster than , while keeping acceptable accuracy. For LHC Run 3 ATLAS developed , based on Generative Adversarial Networks. In addition, a container-based system, , allows for a simple and effective distribution of the training on additional high performance resources like HPC clusters (including Leonardo at ), independently from the underlying system. This contribution presents and , discussing their technical details, the advantage they provide to research activities and the latest developments
Future challenges for CERN’s ion injector complex
No full textThe ion injector complex at CERN supplies ions for collisions at the Large Hadron Collider (LHC) and for fixed-target physics programmes at the Super Proton Synchrotron (SPS) and Proton Synchrotron (PS). In recent years, there has been growing interest in experiments with lighter ions than lead within the ion-physics community. The NA61/SHINE collaboration has requested beams of oxygen, magnesium, and boron for Run 4 (2030-2033), while the HEARTS++ project proposal aims to enable switching between four ion species, with each transition occurring within 15 minutes. Additionally, LHC experiments are considering lighter-than-lead ion beams for Run 5 (2036-2041), pending an assessment of which particle species collisions offer higher nucleon-nucleon luminosity. Consolidating these future scenarios demands an evaluation of the light-ion performance of the present injector complex. This contribution discusses the challenges of the present injector complex in view of light-ion operation and a proposed ion complex upgrade to address future needs