REVIEW 6 cited by
Search for new phenomena in two-body invariant mass distributions using unsupervised machine learning for anomaly detection at $\sqrt{s} = 13$ TeV with the ATLAS detector
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
abstract
Searches for new resonances are performed using an unsupervised anomaly-detection technique. Events with at least one electron or muon are selected from 140 fb$^{-1}$ of $pp$ collisions at $\sqrt{s} = 13$ TeV recorded by ATLAS at the Large Hadron Collider. The approach involves training an autoencoder on data, and subsequently defining anomalous regions based on the reconstruction loss of the decoder. Studies focus on nine invariant mass spectra that contain pairs of objects consisting of one light jet or $b$-jet and either one lepton ($e$, $\mu$), photon, or second light jet or $b$-jet in the anomalous regions. No significant deviations from the background hypotheses are observed.
Forward citations
Cited by 6 Pith papers
-
Learning Standard Model structure from LHC data with Riemannian flow matching
ShellFlow, a Riemannian flow-matching transformer fed only on-shell and invariant-mass priors and ~8×10^8 recorded ATLAS events, reproduces the SM's dilepton resonances, Weinberg angle, and top/W mass peaks in a singl...
-
Model-agnostic search for dijet resonances with anomalous jet substructure in proton-proton collisions at $\sqrt{s}$ = 13 TeV
A model-agnostic CMS search for dijet resonances with anomalous jet substructure finds no excess and reports first exclusion limits on several benchmark signals, with ML anomaly detection improving sensitivity over in...
-
Classical Hardware Acceleration of Quantum Autoencoders for Real-Time Anomaly Detection in Collider Experiments
Quantum and hybrid quantum-classical autoencoders for LHC trigger anomaly detection are quantized and synthesized onto a single FPGA SLR with claimed sub-microsecond-to-few-microsecond latency at stated parity with cl...
-
Wasserstein normalized autoencoder for anomaly detection
A Wasserstein-distance-trained normalized autoencoder detects semivisible jets in simulated LHC events with AUCs around 0.69–0.77, outperforming standard and normalized autoencoders on a ttbar background.
-
Jet Substructure Probe on Scalar Leptoquark Models via Top Polarization
A simulation study projects up to 5.4 sigma discovery significance for scalar leptoquarks at the HL-LHC and up to 3.2 sigma separation between the S3 and R2 models using a BDT score.
-
Automatizing the search for mass resonances using BumpNet
One trained convolutional network predicts bump significance across mass histograms of different sizes and backgrounds, approaching the accuracy of the ideal likelihood-ratio test.
Discussion (0). Continue with ORCID to comment.