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The Pixel Luminosity Telescope: a detector for luminosity measurement at CMS using silicon pixel sensors

2023· article· en· W4385350596 on OpenAlexfundno aff
E. Ayala, E. Carrera Jarrin, I. Ahmed, Alan Campbell, V. Danilov, L. I. Estevez Banos, A. Giraldi, M. Guthoff, M. Hempel, H. Henschel, J. Knolle, W. Lange, J. Leonard, W. Lohmann, A. B. Meyer, V. Myronenko, M. Penno, B. Ribeiro Lopes, J. Rübenach, A. Saggio, V. Scheurer, R. E. Sosa Ricardo, O. Turkot, D. Walter, F. Kassel, S. Mallows, M. Bartók, R. Chudasama, K. Farkas, M. Fejes, M. M. A. Gadallah, P. Major, A. Mehta, G. Pásżtor, Attila József Rádl, G. I. Veres, H. Bakhshiansohi, A. Gholami, E. Khazaie, Mosslim Sedghi, M. Zeinali, F. Fabbri, N. Tosi, N. Bacchetta, P. Trapani, J. Daugalas, J. F. Benitez, A. Castaneda Hernandez, H. A. Encinas Acosta, L. G. Gallegos Maríñez, M. León Coello, J. A. Murillo Quijada, A. Sehrawat, L. Valencia Palomo, C. Oropeza Barrera, S. Bheesette, Anthony Butler, Philip H. Butler, A. Lokhovitskiy, P. Lujan, G. Auzinger, A. H. Ball, Yusuf Can Cekmecelioglu, Anne Dabrowski, K. Damanakis, A. Donadon Servelle, F. Eble, M. Haranko, J. Hegeman, K. Kessaci, A. Kornmayer, R. Loos, M. Miraglia, J. Nicolini, S. Orfanelli, L. Orsini, Andrea Petrucci, V. Ryjov, Santeri Saariokari, C. Schwick, B. Schneider, Stefanos Tsoukias, Peter Tsrunchev, J. Wańczyk, Agnieszka Zagozdzinska-Bochenek, W. D. Zeuner, T. Rohe, G. Krintiras, C. Palmer, Sh. Jain, J. Mans, R. Rusack, J. Bueghly, Z. Chen, T. Gunter, Nathaniel Odell, A. Pozdnyakov, M. Velasco, B. Harrop, S. Higginbotham, A. Kalogeropoulos, J. Luo, D. Marlow, David Stickland, Z. Xie, E. Bartz, D. Hidas, O. Karacheban, S. Schnetzer, R. Stone, H. Acharya, A. G. Delannoy, J. Heideman, Nimmitha Karunarathna, Grant Riley, K. Rose, S. Spanier, K. Thapa, A. Gurrola, W. Johns, F. Romeo, B. Soubasis, M. C. Farrow, I. Azhgirey, A. Ershov, A. Gribushin, A. Kaminskiy, I. Kurochkin, V. Okhotnikov, E. Popova, Anastasiia Riabchikova, D. Selivanova, Alexey Shevelev

Bibliographic record

VenueThe European Physical Journal C · 2023
Typearticle
Languageen
FieldPhysics and Astronomy
TopicParticle Detector Development and Performance
Canadian institutionsnot available
FundersHigh Energy PhysicsNational Research, Development and Innovation OfficeDeutsche ForschungsgemeinschaftCERNIstituto Nazionale di Fisica NucleareNemzeti Kutatási Fejlesztési és Innovációs HivatalMagyar Tudományos AkadémiaMcGill UniversityMinistry of Business, Innovation and EmploymentBundesministerium für Bildung und ForschungU.S. Department of EnergyConsejo Nacional de Ciencia y TecnologíaNational Science Foundation
KeywordsPhysicsTelescopeLuminosityDetectorLarge Hadron ColliderPixelInteraction pointOpticsAstrophysicsParticle physics

Abstract

fetched live from OpenAlex

Abstract The Pixel Luminosity Telescope is a silicon pixel detector dedicated to luminosity measurement at the CMS experiment at the LHC. It is located approximately 1.75 m from the interaction point and arranged into 16 “telescopes”, with eight telescopes installed around the beam pipe at either end of the detector and each telescope composed of three individual silicon sensor planes. The per-bunch instantaneous luminosity is measured by counting events where all three planes in the telescope register a hit, using a special readout at the full LHC bunch-crossing rate of 40 MHz. The full pixel information is read out at a lower rate and can be used to determine calibrations, corrections, and systematic uncertainties for the online and offline measurements. This paper details the commissioning, operational history, and performance of the detector during Run 2 (2015–18) of the LHC, as well as preparations for Run 3, which will begin in 2022.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.012
Threshold uncertainty score0.039

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0120.004

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.053
GPT teacher head0.269
Teacher spread0.217 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations7
Published2023
Admission routes1
Has abstractyes

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Same venueThe European Physical Journal CSame topicParticle Detector Development and PerformanceFrench-language works237,207