Evidence map›Paper›PMID 41726844›Full record

ArticleEPJ research infrastructures2026

Anomaly Detection for Automated Data Quality Monitoring in the CMS Detector.

Andrew Brinkerhoff, Chosila Sutantawibul, Indara Suarez, Robert White, Caio Daumann, Jonathan Guiang, Chad Freer, Samuel May, Bennett Marsh, Darin Acosta and 18 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in EPJ research infrastructures, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

28 authors.

Andrew BrinkerhoffBaylor University, Waco, USA.
Chosila SutantawibulBaylor University, Waco, USA.
Indara SuarezBoston University, Boston , USA.
Robert WhiteINFN Sezione di Torino, Turin , Italy.
Caio DaumannRWTH Aachen University III. Physikalisches Institut A, Aachen, Germany.
Jonathan GuiangUniversity of California Santa Barbara, Santa Barbara, USA.
Chad FreerMassachusetts Institute of Technology, Cambridge, USA.
Samuel MayBoston University, Boston , USA.
Bennett MarshUniversity of California Santa Barbara, Santa Barbara, USA.
Darin AcostaRice University, Houston, USA.
Alex AubuchonNortheastern University, Boston, USA.
Emanuela BarberisNortheastern University, Boston, USA.
Aaron BundockUniversity of Bristol, Bristol, England.
Claudio CampagnariUniversity of California Santa Barbara, Santa Barbara, USA.
Evan CollinsBaylor University, Waco, USA.
Preston EppsNortheastern University, Boston, USA.
Johannes ErdmannRWTH Aachen University III. Physikalisches Institut A, Aachen, Germany.
Henning FlaecherUniversity of Bristol, Bristol, England.
Junshen HuangBaylor University, Waco, USA.
Vivan NguyenNortheastern University, Boston, USA.
Ryan NieBoston University, Boston , USA.
Sudarshan ParamesvaranUniversity of Bristol, Bristol, England.
John RotterRice University, Houston, USA.
Kaitlin SalyerBoston University, Boston , USA.
Siddhesh SawantBaylor University, Waco, USA.
Tanvi SheokandPanjab University, Chandigarh, India.
Darien WoodNortheastern University, Boston, USA.
the CMS Muon Detector Collaboration

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Successful operation of large particle detectors like the Compact Muon Solenoid (CMS) at the CERN Large Hadron Collider requires rapid, in-depth assessment of data quality. We introduce the "AutoDQM" system for Automated Data Quality Monitoring using advanced statistical techniques and unsupervised machine learning. Anomaly detection algorithms based on the beta-binomial probability function and principal component analysis are tested on the full set of proton-proton collision data collected by CMS in 2022. AutoDQM identifies anomalous "bad" data affected by significant detector malfunction at a rate 4 - 6 times higher than "good" data, demonstrating its effectiveness as a general data quality monitoring tool. Supplementary Information: The online version contains supplementary material available at 10.1007/s41781-025-00147-2.

Indexed as

Anomoly DetectionData Quality MonitoringParticle PhysicsPCA

Identifiers

PMID41726844
PMCPMC12922165

What OpenQuestion holds

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LicenceCC BY
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.