Nanosecond anomaly detection with decision trees and real-time application to exotic Higgs decays.

S T Roche, Q Bayer, B T Carlson, W C Ouligian, P Serhiayenka, J Stelzer, T M Hong

Abstract read

In one paragraph

Article in Nature communications, 2024. 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

7 authors.

S T RocheSchool of Medicine, Saint Louis University, Saint Louis, MO, USA.ORCID http://orcid.org/0000-0002-3878-5873

Q BayerDepartment of Physics and Astronomy, University of Pittsburgh, Pittsburgh, PA, USA.ORCID http://orcid.org/0009-0005-1203-7577

B T CarlsonDepartment of Physics and Astronomy, University of Pittsburgh, Pittsburgh, PA, USA.ORCID http://orcid.org/0000-0002-7550-7821

W C OuligianDepartment of Physics and Astronomy, University of Pittsburgh, Pittsburgh, PA, USA.

P SerhiayenkaDepartment of Physics and Astronomy, University of Pittsburgh, Pittsburgh, PA, USA.

J StelzerDepartment of Physics and Astronomy, University of Pittsburgh, Pittsburgh, PA, USA.ORCID http://orcid.org/0000-0003-0690-8573

T M HongDepartment of Physics and Astronomy, University of Pittsburgh, Pittsburgh, PA, USA. tmhong@pitt.edu.ORCID http://orcid.org/0000-0001-7834-328X

Funding

DOE | SC | High Energy Physics (HEP) DE-SC0007914DOE | SC | High Energy Physics (HEP) DE-SC0012704National Science Foundation (NSF) NSF-2209370

6 · The paper itself

Abstract

We present an interpretable implementation of the autoencoding algorithm, used as an anomaly detector, built with a forest of deep decision trees on FPGA, field programmable gate arrays. Scenarios at the Large Hadron Collider at CERN are considered, for which the autoencoder is trained using known physical processes of the Standard Model. The design is then deployed in real-time trigger systems for anomaly detection of unknown physical processes, such as the detection of rare exotic decays of the Higgs boson. The inference is made with a latency value of 30 ns at percent-level resource usage using the Xilinx Virtex UltraScale+ VU9P FPGA. Our method offers anomaly detection at low latency values for edge AI users with resource constraints.

Identifiers

PMID38664390

DOI10.1038/s41467-024-47704-8

PMCPMC11045859

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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.