Searcharxiv⌕ Search
Home/Archive/arXiv

arXiv · hep-ex/0007045

Studies of the Response of the Prototype CMS Hadron Calorimeter, Including Magnetic Field Effects, to Pion, Electron, and Muon Beams

V. V. Abramov·B. S. Acharya·N. Akchurin·I. Atanasov·G. Baiatian·A. Ball·S. Banerjee·P. de Barbaro·V. Barnes·G. Bencze·A. Bodek·M. Booke·H. Budd·L. Cremaldi·P. Cushman·S. R. Dugad·L. Dimitrov·A. Dyshkant·J. Elias·V. N. Evdokimov·D. Fong·J. Freeman·V. Genchev·P. I. Goncharov·D. Green·A. Gurtu·V. Hagopian·P. Iaydijiev·Yu. Korneev·A. Krinitsyn·G. Krishnaswami·M. R. Krishnaswamy·V. Kryshkin·S. Kunori·A. Laasanen et. al
View original source ↗Read source document ↗Export metadata ↓

Abstract

We report on the response of a prototype CMS hadron calorimeter module to charged particle beams of pions, muons, and electrons with momenta up to 375 GeV/c. The data were taken at the H2 and H4 beamlines at CERN in 1995 and 1996. The prototype sampling calorimeter used copper absorber plates and scintillator tiles with wavelength shifting fibers for readout. The effects of a magnetic field of up to 3 Tesla on the response of the calorimeter to muons, electrons, and pions are presented, and the effects of an upstream lead tungstate crystal electromagnetic calorimeter on the linearity and energy resolution of the combined calorimetric system to hadrons are evaluated. The results are compared with Monte Carlo simulations and are used to optimize the choice of total absorber depth, sampling frequency, and longitudinal readout segmentation.

Explore related subjects

hep-ex

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

V. V. Abramov, B. S. Acharya, N. Akchurin, I. Atanasov, G. Baiatian, A. Ball, S. Banerjee, P. de Barbaro, V. Barnes, G. Bencze, A. Bodek, M. Booke, H. Budd, L. Cremaldi, P. Cushman, S. R. Dugad, L. Dimitrov, A. Dyshkant, J. Elias, V. N. Evdokimov, D. Fong, J. Freeman, V. Genchev, P. I. Goncharov, D. Green, A. Gurtu, V. Hagopian, P. Iaydijiev, Yu. Korneev, A. Krinitsyn, G. Krishnaswami, M. R. Krishnaswamy, V. Kryshkin, S. Kunori, A. Laasanen et. al. 2000-07-21. Studies of the Response of the Prototype CMS Hadron Calorimeter, Including Magnetic Field Effects, to Pion, Electron, and Muon Beams. https://doi.org/10.1016/s0168-9002(00)00711-7

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

Calibration of the jet energy scale and resolution of small-radius jets using semileptonic $t\bar{t}$ events with the ATLAS detector

A measurement of correction factors for the hadronic jet energy scale and resolution in the ATLAS detector is presented. These correction factors account for differences between simulated and observed data. They are obtained by analysing a selection of top quark events collected in proton-proton collisions by ATLAS between the years 2015 and 2018 at a centre-of-mass energy $\sqrt{s} = 13$ TeV as well as in 2022 and 2023 at $\sqrt{s} = 13.6$ TeV. The impact of different jet energy scale or resolution corrections on the reconstructed mass of the hadronically decaying $W$ boson from top-quark decays in simulation is quantified. The correction factors are extracted from a fit to the parameterised reconstructed $W$-boson mass distribution to data. The energy scale and resolution corrections are measured as a function of the jet transverse momentum between 20 and 200 GeV and absolute pseudorapidity less than 0.8. The uncertainties in the energy scale range from about 0.93% to about 1.7% for jets between 35 and 200 GeV, while for the energy resolution the uncertainties range from about 14% to 28%. The method presented will be used in conjunction with other techniques to further improve ATLAS jet energy scale and resolution precision.

hep-ex↗
arXiv2026

Search for anomalies in vector-boson fusion production of the Higgs boson in $H(\rightarrow γγ) jj$ events using 164 fb$^{-1}$ of $pp$ collision data collected at $\sqrt{s}=13.6$ TeV with the ATLAS detector

This article details two studies of Higgs boson properties using the vector-boson fusion production mode and the $γγjj$ final state. Both efforts are based on a data sample corresponding to 164 fb$^{-1}$ of $\sqrt{s}=13.6$ TeV proton--proton collisions recorded by the ATLAS experiment at the Large Hadron Collider. The first study employs matrix-element-based optimal observables to constrain CP-odd couplings beyond the Standard Model within the Standard Model Effective Field Theory framework, expressed in the Warsaw basis. The second study exploits angular distributions to probe the Higgs boson's couplings to longitudinally and transversely polarised $W$ and $Z$ bosons in the production of the Higgs boson. To maximise the sensitivity, the constraints of the CP-odd couplings are combined with those from a previous analysis performed in $γγjj$ events in a data sample of proton--proton collisions at $\sqrt{s}=13$ TeV, corresponding to an integrated luminosity of 140 fb$^{-1}$. A significant improvement with respect to the previous analysis is achieved through the implementation of a new neural-network-based classification algorithm. All measurements are in agreement with the Standard Model prediction of a CP-even Higgs boson with the expected relative coupling strengths to longitudinally and transversely polarised vector bosons.

hep-ex↗
arXiv2026

Probabilistic, Structure-Intrinsic Clustering with a Hierarchical Embedding (PSICHE) in Time and Space Applied In Particle Physics Jet Reconstruction

Jet reconstruction is an active and open area of particle physics research, with questions related to jet size, multiplicity, substructure, and experimental performance in the presence of pileup and noise. Analogous challenges are ubiquitous throughout other applications of unsupervised learning in the context of clustering and association. This paper introduces a new algorithm for clustering in the context of jet reconstruction: Probabilistic, Structure-Intrinsic Clustering with a Hierarchical Embedding (PSICHE). PSICHE addresses several challenges and shortcomings of existing jet clustering methods with novel features, including but not limited to: (i) dynamically-learned and variable jet (cluster) sizes, (ii) unsupervised, multi-scale learning of emergent features, like jet substructure and multiplicity, (iii) clustering in space and time, and (iv) incorporation of domain-specific experimental uncertainties. All of these properties are achieved in a self-consistent, probabilistic framework that is computationally tractable. Furthermore, demonstrations of these features are presented for a range of jet clustering examples in LHC scenarios for various physics phenomena, clustering inputs, pileup conditions, and detector performance parameters.

hep-ex↗