Jet Measurements In CMS

A measurement of inclusive jet and dijet production cross sections is presented. Data from large hadron collider (LHC) proton-proton collisions at $\sqrt{s}=$ 7 TeV, corresponding to $4.67 fb^{-1}$ of integrated luminosity, have been collected with the compact muon solenoid (CMS) detector. Jets are reconstructed with the anti-$k_T$ clustering algorithm with size parameter $R=0.7$, extending to rapidity $|y|=2.5$, transverse momentum $p_{T}=$ 2 TeV, and dijet invariant mass $M_{JJ}=$ 5 TeV. The measured cross sections are corrected for detector effects and compared to perturbative QCD predictions at next-to-leading order (NLO), corrected for non perturbative (NP) factors, using various sets of parton distribution functions. Determination Of Jet Energy Correction from $\sqrt{s}=$ 7 TeV CMS data is presented. The individual components are determined. The jet energy scale uncertainty factors are also shown.Comment: 6 pages, 5 figures. Proceedings For ICHEP'201

Jet Production Measurements at CMS

Jet production cross-section measurements are presented. The measurements are done with the data from Large Hadron Collider (LHC) proton-proton collisions, collected with the Compact Muon Solenoid (CMS) detector. The inclusive jet production measurements are carried out with data collected $\rm \sqrt{s} ~= ~7 ~TeV$ and $\rm 8~TeV$ with total integrated luminosity ($\mathcal{L}_{int}$) $\rm 5.0~ fb^{-1}$ and $\rm 10.71~ fb^{-1}$ respectively. The dijet production measurements are carried out with the $\rm \sqrt{s}~ =~ 7 ~TeV$ dataset. Jets are reconstructed with the anti-$k_T$ clustering algorithm with size parameter $R=0.7$. The measured cross sections are corrected for detector effects and compared to perturbative QCD predictions at NLO, corrected for NP factors, using various sets of PDF. The inclusive jet cross-section ratio of the jets reconstructed with the anti-$k_T$ (AK) algorithm and two radius parameter $\rm R~=~0.5$ and $\rm R~=~0.7$ are also presented. The data used is $\rm \sqrt{s}~ =~ 7 ~TeV$ CMS data corresponding to $\rm \mathcal{L}_{int}~=~5.0 ~fb^{-1}$. Significant discrepancies are found comparing the data to leading order calculations and to fixed order calculations at NLO, corrected for NP effects, whereas simulations with NLO matrix elements matched to the parton showers describe the data quite well. A study of color coherence effects in pp collisions has been performed with the data collected at $\rm \sqrt{s}~ =~ 7~TeV$ and $\rm\mathcal{L}_{int}~=~ 36~pb^{-1}$. The measurement of the azimuthal angular correlation between the second and third jets is compared to the predictions of Monte Carlo models with different implementations of color coherence effects.Comment: 8 pages, 6 figures. Proceedings for EPS-HEP 201

Secondary Vertex Finding in Jets with Neural Networks

Jet classification is an important ingredient in measurements and searches for new physics at particle coliders, and secondary vertex reconstruction is a key intermediate step in building powerful jet classifiers. We use a neural network to perform vertex finding inside jets in order to improve the classification performance, with a focus on separation of bottom vs. charm flavor tagging. We implement a novel, universal set-to-graph model, which takes into account information from all tracks in a jet to determine if pairs of tracks originated from a common vertex. We explore different performance metrics and find our method to outperform traditional approaches in accurate secondary vertex reconstruction. We also find that improved vertex finding leads to a significant improvement in jet classification performance

Reconstructing particles in jets using set transformer and hypergraph prediction networks

The task of reconstructing particles from low-level detector response data to predict the set of final state particles in collision events represents a set-to-set prediction task requiring the use of multiple features and their correlations in the input data. We deploy three separate set-to-set neural network architectures to reconstruct particles in events containing a single jet in a fully-simulated calorimeter. Performance is evaluated in terms of particle reconstruction quality, properties regression, and jet-level metrics. The results demonstrate that such a high dimensional end-to-end approach succeeds in surpassing basic parametric approaches in disentangling individual neutral particles inside of jets and optimizing the use of complementary detector information. In particular, the performance comparison favors a novel architecture based on learning hypergraph structure, HGPflow, which benefits from a physically-interpretable approach to particle reconstruction.Comment: 17 pages, 21 figure

Light quark Yukawas in triboson final states

Abstract Triple heavy vector boson production, pp → VVV (V = W, Z), has recently been observed for the first time. We propose that precision measurements of this process provide an excellent probe of the first generation light quark Yukawa couplings. Modified quark interactions with the off-shell Higgs in this process lead to a rapid growth of the partonic cross sections with energy, which manifests in an enhanced pT distribution of the final state leptons and quarks. We quantify this effect and estimate the present and future 2σ sensitivity to the up, down, and strange Yukawas. In particular, we find that HL-LHC can reach $$\mathcal{O}(400)$$ O 400 sensitivity to the down Yukawa relative to the Standard Model value, improving the current sensitivity in this process by a factor of 10, and which can be further improved to $$\mathcal{O}(30)$$ O 30 at FCC-hh. This is competitive with and complementary to constraints from global fits and other on-shell probes of the first generation Yukawas. The triboson sensitivity at HL-LHC corresponds to probing dimension-6 SMEFT operators suppressed by an $$\mathcal{O}(1)$$ O 1 TeV scale, similarly to other LHC Higgs probes.</jats:p

Application of quantum computing techniques in particle tracking at LHC

After the next planned upgrades to the LHC, the luminosity it delivers will more than double, substantially increasing the already large demand on computing resources. Therefore an efficient way to reconstruct physical objects is required. Recent studies show that one of the quantum computing techniques, quantum annealing (QA), can be used to perform particle tracking with efficiency higher than 90% in the high pileup region in the high luminosity environment. The algorithm starts by determining the connection between the hits, and classifies the topological objects with their pattern. The current study aims to improve the pre-processing efficiency in the QA-based tracking algorithm by implementing a graph neural network (GNN), which is expected to efficiently generate the topological object needed for the annealing process. Tracking performance with a different setup of the original algorithm is also studied with data collected by the ATLAS experiment

HHH whitepaper

We here report on the progress of the HHH Workshop, that took place in Dubrovnik in July 2023. After the discovery of a particle that complies with the properties of the Higgs boson of the Standard Model, all Standard Model (SM) parameters are in principle determined. However, in order to verify or falsify the model, the full form of the potential has to be determined. This includes the measurement of the triple and quartic scalar couplings. We here report on ongoing progress of measurements for multi-scalar final states, with an emphasis on three SM-like scalar bosons at 125 GeV, but also mentioning other options. We discuss both experimental progress and challenges as well as theoretical studies and models that can enhance such rates with respect to the SM predictions