Search for Resonances Decaying to Three W Bosons in Proton-Proton Collisions at s = 13 TeV
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Results are presented for a test of the compositeness of the heaviest charged lepton, τ, using data collected by the CMS experiment in proton-proton collisions at a center-of-mass energy of 13 TeV at the CERN LHC. The data were collected in 2016–2018 and correspond to an integrated luminosity of 138 fb −1 . This analysis searches for tau lepton pair production in which one of the tau leptons is produced in an excited state and decays to a ground state tau lepton and a photon. The event selection consists of two isolated tau lepton decay candidates and a high-energy photon. The mass of the excited tau lepton is reconstructed using the missing transverse momentum in the event, assuming the momentum of the neutrinos from each tau lepton decay are aligned with the visible decay products. No excess of events above the standard model background prediction is observed. This null result is used to set lower bounds on the excited tau lepton mass. For a compositeness scale Λ equal to the excited tau lepton mass, excited tau leptons with masses below 4700 GeV are excluded at 95% confidence level; for Λ = 10 TeV this exclusion is set at 2800 GeV. This is the first experimental result covering this production and decay process in the excited tau mass range above 175 GeV.
Within the soft collinear effective theory (SCET), we derive a factorization theorem which resums Sudakov logarithms (α s ln 2 ( –t)) n to all orders in the quark-in-quark generalized parton distribution (GPD) at large momentum transfer t, and perform a consistency check to one-loop. We show that the same Sudakov factor appears in the ‘Feynman’ contribution to the GPDs of the nucleon. Our result enables the resummation of all the large logarithms ln Q 2 and ln 2 t in exclusive processes with two hard scales Λ$^{2}_{QCD}$ ≪ |t| ≪ Q 2 . We also present a SCET power counting analysis of the Feynman contributions to the GPDs and show that the x-dependence of GPDs factorizes at large-t with controlled corrections. This in particular implies that any ratio of GPD moments such as the electromagnetic and gravitational form factors (GFF) is perturbatively calculable in this approximation. Furthermore, we identify a novel order α s power-law t-dependence in the GPD and the D-type GFF that will dominate over the standard order α$^{2}_{s}$ ‘leading twist’ asymptotic contribution in the phenomenologically relevant region of t.
The ATLAS experiment at CERN relies on a Worldwide Distributed Computing Grid infrastructure to support its physics program at the Large Hadron Collider. ATLAS has integrated cloud computing resources to complement its Grid infrastructure and conducted an R&D program on Google Cloud Platform. These initiatives leverage key features of commercial cloud providers: lightweight configuration and operation, elasticity and availability of diverse infrastructures. Here this paper examines the seamless integration of cloud computing services as a conventional Grid site within the ATLAS workflow management and data management systems, while also offering new setups for interactive, parallel analysis. It underscores pivotal results that enhance the on-site computing model and outlines several R&D projects that have benefited from large-scale, elastic resource provisioning models. Furthermore, this study discusses the impact of cloud-enabled R&D projects in three domains: accelerators and AI/ML, ARM CPUs and columnar data analysis techniques.
Is there a quantum utility in establishing the masses of hadrons? Here we show that the best response is that of Farrés, Capó, and Davis: perhaps, perhaps, perhaps. This is interesting given the general case that particle physics demands quantum computers. For stable hadrons, classical LQCD has achieved sub-percent precision with no sign problem, and quantum computers offer no advantage. For resonances, the Maiani-Testa theorem is an obstruction that quantum simulation is immune to. For nuclei, Wick contractions and signal-to-noise are genuine classical barriers. Underlying these cases is a unified picture connecting the sign problem to Wigner negativity and T gate cost. This manuscript was drafted from extensive interaction with \textsc{Claude}.
Heavy flavor production at the future Electron-Ion Collider (EIC) will allow us to precisely determine the quark/gluon fragmentation processes in vacuum and the nuclear medium especially within the poorly constrained kinematic region. Heavy flavor hadron and jet reconstructions with the recent EIC detector design have been studied in simulation. Results of corresponding physics projections such as the flavor dependent hadron nuclear modification factor R_{eA} R e A in electron+nucleus collisions will be shown. The statistical precision obtained by these proposed heavy flavor measurements for the future EIC provides a strong discriminating power in separating different theoretical predictions.
In long-baseline neutrino oscillation experiments, Monte Carlo (MC) simulations based on hadron interactions and decays are used to predict the neutrino flux. The 10%-level systematic uncertainty of the predicted neutrino fluxes from these simulations is dominated by uncertainties in hadron interaction cross sections due to limited hadron scattering data. EMPHATIC aims to reduce the neutrino flux uncertainty by providing additional data. Using a table-top-sized spectrometer located at the Fermilab Test Beam Facility (FTBF), its physics program includes precise measurements of hadron scattering and production cross sections at various beam momenta and target species that are relevant for GeV-scale neutrino production. Using simulation, we have developed a simple single-track reconstruction algorithm that has a momentum resolution of 3-4\%. We will demonstrate the progress in developing one of EMPHATIC’s first track reconstruction algorithms – an important step in making a new single-track forward scattering measurement (p + C $\rightarrow$ p + C at several beam momenta) using Phase 1 data collected between 2022 and 2023.
The CERN ATLAS Experiment successfully uses a worldwide dis-tributed computing Grid infrastructure to support its physics programme at the Large Hadron Collider (LHC). The Grid workflow system PanDA routinely manages up to 700,000 concurrently running production and analysis jobs to process simulation and detector data. In total more than 500 PB of data are distributed over more than 150 sites in the WLCG and handled by the ATLAS data management system Rucio. To prepare for the ever growing data rate in future LHC runs new developments are underway to embrace industry accepted protocols and technologies, and utilize opportunistic resources in a standard way. This paper reviews how the Google and Amazon Cloud computing ser-vices have been seamlessly integrated as a Grid site within PanDA and Rucio. Performance and brief cost evaluations will be discussed. Such setups could offer advanced Cloud tool-sets and provide added value for analysis facilities that are under discussions for LHC Run-4.
This paper introduces a transverse-momentum dependent (TMD) factorization scheme designed to unify both large and small Bjorken-x regimes. We compute the next-to-leading order quantum chromodynamics (QCD) corrections to the gluon TMD operator for an unpolarized hadron within this proposed scheme. This leads to the emergence of a new TMD evolution, incorporating those in transverse momentum, rapidity, and Bjorken-x. When matched to the collinear factorization scheme, our factorization scheme faithfully reproduces the well-established Dokshitzer-Gribov-Lipatov-Altarelli-Parisi and Collins-Soper-Sterman (CSS) evolutions. Conversely, matching with high-energy factorization not only yields the Balitsky-Fadin-Kuraev-Lipatov evolution but also reveals distinctive signatures of CSS logarithms. The development of this novel TMD factorization scheme, capable of seamlessly reconciling disparate Bjorken-x regimes and faithfully reproducing established QCD evolution equations, has the potential to significantly advance our comprehension of high-energy processes and three-dimensional parton structures of hadrons. Published by the American Physical Society 2024
Neuro-Spark, which is a new neuromorphic architecture with a field-programmable gate array (FPGA) implementation for ultrafast spiking neural network (SNN) inference at the edge, facilitates smart-pixel in-sensor filtering for high-energy physics experiments at the Large Hadron Collider (LHC). Utilizing the evolutionary optimization for neuromorphic systems (EONS) training method, we generate compact SNN models with 91% signal efficiency, akin to convolutional neural networks but with half the parameters. However, deploying near the detector poses a challenge because the SNN must handle a sustained input data rate exceeding 1013 GB/s. To overcome this, we propose a novel hardware architecture that uses high-level synthesis to construct a tuned architecture for the EONS-trained SNN. In addition to the analysis and validation with an AMD Xilinx Artix-A7 FPGA, our solution consumes only ç24% of FPGA LUT and flipflops. We also introduce an innovative quantization method that reduces FPGA resource utilization by ç15% without compromising accuracy. Our FPGA implementation achieves computing latency of ç10 ns for smart-pixel application inference on an edge FPGA.
In this talk, I describe some recent ideas relating to the spectral reconstruction inverse problem, which arises frequently in lattice QCD calculations of inclusive hadronic quantities, and provide some physical context for this work. Particular emphasis is given to a new method for rigorously bounding uncertainties using techniques from complex analysis.
Differential cross sections for top quark pair ($\textrm{t}\overline{\textrm{t}}$) production are measured in proton-proton collisions at a center-of-mass energy of 13 TeV using a sample of events containing two oppositely charged leptons. The data were recorded with the CMS detector at the CERN Large Hadron Collider and correspond to an integrated luminosity of 138 fb −1 . The differential cross sections are measured as functions of kinematic observables of the $\textrm{t}\overline{\textrm{t}}$ system, the top quark and antiquark and their decay products, as well as of the number of additional jets in the event. The results are presented as functions of up to three variables and are corrected to the parton and particle levels. When compared to standard model predictions based on quantum chromodynamics at different levels of accuracy, it is found that the calculations do not always describe the observed data. The deviations are found to be largest for the multi-differential cross sections.
SF-24-038"PDFdecoder", as a new application to explore parametrizations of parton distribution functions (PDFs) of the proton or other hadrons. The PDFs are fundamental quantities in particle physics which are necessary inputs to precise theoretical predictions for experiments at the Large Hadron Collider (LHC) and other facilities. As such, understanding how the PDFs are parametrized and associated uncertainties is a pressing need. The specific problem PDFdecoder confronts is the need of having a tractable and interpretably machine-learning (ML) framework to parametrize the PDFs and their uncertainties so as to understand how a given preferred parametrization is obtained. This problem has not been significantly addressed in the current literature. While other groups have used ML-based approaches to parametrize PDFs in the form of feed-forward neural networks, the question of tractability has not been explored in a PDF context. Our solution makes significant progress in this problem by using an array of encoder-decoder (essentially, autoencoder) architectures with varying constraints to the intermediate latent spaces based on interpretable physics. As a consequence, the trained models can be used as generative networks to produce interpretable predictions for the PDFs in a way that can be refined and studied further.
In the realm of dijet searches in high-energy physics, a significant challenge has emerged: with experiments producing more and more data, the traditional methods of using analytic functions to describe dijet mass spectra start to fail. Here, to address this, we suggest the application of an anomaly detection approach to eliminate less interesting background events based on event final states. This method not only bypasses the limitations of conventional background models but also significantly enhances our ability to detect potential signals of new physics. Through simulations that mimic the conditions of the upcoming high-luminosity large Hadron collider, we demonstrate the strength and efficiency of this approach in dealing with large data volumes. The integration of unsupervised machine learning into our experimental framework paves the way for a promising avenue to unveil hidden physics discoveries within the overwhelming influx of data.
High energy collisions at the High-Luminosity Large Hadron Collider (LHC) produce a large number of particles along the beam collision axis, outside of the acceptance of existing LHC experiments. The proposed Forward Physics Facility (FPF), to be located several hundred meters from the ATLAS interaction point and shielded by concrete and rock, will host a suite of experiments to probe standard model (SM) processes and search for physics beyond the standard model (BSM). In this report, we review the status of the civil engineering plans and the experiments to explore the diverse physics signals that can be uniquely probed in the forward region. FPF experiments will be sensitive to a broad range of BSM physics through searches for new particle scattering or decay signatures and deviations from SM expectations in high statistics analyses with TeV neutrinos in this low-background environment. High statistics neutrino detection will also provide valuable data for fundamental topics in perturbative and non-perturbative QCD and in weak interactions. Experiments at the FPF will enable synergies between forward particle production at the LHC and astroparticle physics to be exploited. We report here on these physics topics, on infrastructure, detector, and simulation studies, and on future directions to realize the FPF’s physics potential.
One correction is noted for the paper. A wrong cross-section was used for the theory prediction in figure 6 due to not taking into account the VHH contamination properly in the rescaling formula for the signal samples.
Evidence for Higgs boson decay to a pair of muons is presented. This result combines searches in four exclusive categories targeting the production of the Higgs boson via gluon fusion, via vector boson fusion, in association with a vector boson, and in association with a top quark-antiquark pair. The analysis is performed using proton-proton collision data at $ \sqrt{s} $ = 13 TeV, corresponding to an integrated luminosity of 137 fb$^{−1}$, recorded by the CMS experiment at the CERN LHC. An excess of events over the back- ground expectation is observed in data with a significance of 3.0 standard deviations, where the expectation for the standard model (SM) Higgs boson with mass of 125.38 GeV is 2.5. The combination of this result with that from data recorded at $ \sqrt{s} $ = 7 and 8 TeV, corresponding to integrated luminosities of 5.1 and 19.7 fb$^{−1}$, respectively, increases both the expected and observed significances by 1%. The measured signal strength, relative to the SM prediction, is $ {1.19}_{-0.39}^{+0.40}{\left(\mathrm{stat}\right)}_{-0.14}^{+0.15}\left(\mathrm{syst}\right) $. This result constitutes the first evidence for the decay of the Higgs boson to second generation fermions and is the most precise measurement of the Higgs boson coupling to muons reported to date.[graphic not available: see fulltext]
A search for dark matter (DM) particles is performed using events with a Higgs boson candidate and large missing transverse momentum. The analysis is based on proton-proton collision data at a center-of-mass energy of 13 TeV collected by the CMS experiment at the LHC in 2016, corresponding to an integrated luminosity of 35.9 fb$^{-1}$. The search is performed in five Higgs boson decay channels: h$ \to \mathrm{b\bar{b}}$, $\gamma\gamma$, $\tau^{+}\tau^{-}$, W$^{+}$W$^{-}$, and ZZ and the results from the individual channels are combined to maximize the sensitivity of the analysis. No significant excess over the expected standard model background is observed in any of the five channels or in their combination. Limits are set on DM production in the context of two simplified models. The results are also interpreted in terms of a spin-independent DM-nucleon scattering cross section and compared to those from direct-detection DM experiments. This is the first search for DM particles produced in association with a Higgs boson decaying to a pair of W or Z bosons, and the first statistical combination based on five Higgs boson decay channels.