Cover Image

Real-time Anomaly Detection at the L1 Trigger of CMS Experiment

Fuente: arXiv

Saved in:

Bibliographic Details
Main Author:	Gandrakota, Abhijith
Format:	Preprint
Published:	2024
Subjects:	High Energy Physics - Experiment Machine Learning Data Analysis, Statistics and Probability
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Robust Anomaly Detection for Particle Physics Using Multi-Background Representation Learning
by: Gandrakota, Abhijith, et al.
Published: (2024)

Interpreting Transformers for Jet Tagging
by: Wang, Aaron, et al.
Published: (2024)

Autoencoder-based Anomaly Detection System for Online Data Quality Monitoring of the CMS Electromagnetic Calorimeter
by: The CMS ECAL Collaboration
Published: (2023)

Poisson Log-Normal Process for Count Data Prediction
by: Saha, Anushka, et al.
Published: (2026)

Isolating Unisolated Upsilons with Anomaly Detection in CMS Open Data
by: Gambhir, Rikab, et al.
Published: (2025)

Anomaly Detection for Automated Data Quality Monitoring in the CMS Detector
by: Brinkerhoff, Andrew, et al.
Published: (2025)

Machine Learning for Anomaly Detection in Particle Physics
by: Belis, Vasilis, et al.
Published: (2023)

Why Is Attention Sparse In Particle Transformer?
by: Legge, Timothy, et al.
Published: (2025)

Anomaly Detection Based on Machine Learning for the CMS Electromagnetic Calorimeter Online Data Quality Monitoring
by: Harilal, Abhirami, et al.
Published: (2024)

Review of Machine Learning for Real-Time Analysis at the Large Hadron Collider experiments ALICE, ATLAS, CMS and LHCb
by: Boggia, Laura, et al.
Published: (2025)

The CMS statistical analysis and combination tool: COMBINE
by: CMS Collaboration
Published: (2024)

SIGMA: Single Interpolated Generative Model for Anomalies
by: Das, Ranit, et al.
Published: (2024)

Run 3 performance and advances in heavy-flavor jet tagging in CMS
by: Sarkar, Uttiya
Published: (2024)

Kitchen Sink Anomaly Detection
by: Das, Ranit, et al.
Published: (2026)

Learning the Standard Model Manifold: Bayesian Latent Diffusion for Collider Anomaly Detection
by: Patel, Jigar, et al.
Published: (2026)

TrackSorter: A Transformer-based sorting algorithm for track finding in High Energy Physics
by: Melkani, Yash, et al.
Published: (2024)

Scaling Particle Collision Data Analysis
by: Wu, Hengkui, et al.
Published: (2024)

An information theoretic limit to data amplification
by: Watts, S. J., et al.
Published: (2024)

SymbolFit: Automatic Parametric Modeling with Symbolic Regression
by: Tsoi, Ho Fung, et al.
Published: (2024)

Graph Neural Network for Neutrino Physics Event Reconstruction
by: Hewes, V, et al.
Published: (2024)

Physics Event Classification Using Large Language Models
by: Fanelli, Cristiano, et al.
Published: (2024)

Comparison of Machine Learning Approaches for Classifying Spinodal Events
by: Malviya, Ashwini, et al.
Published: (2024)

Global Optimisation of Black-Box Functions with Generative Models in the Wasserstein Space
by: Ramazyan, Tigran, et al.
Published: (2024)

Mind the Gap: Navigating Inference with Optimal Transport Maps
by: Algren, Malte, et al.
Published: (2025)

Learning to bin: differentiable and Bayesian optimization for multi-dimensional discriminants in high-energy physics
by: Erdmann, Johannes, et al.
Published: (2026)

DeepTreeGANv2: Iterative Pooling of Point Clouds
by: Scham, Moritz Alfons Wilhelm, et al.
Published: (2023)

GNN For Muon Particle Momentum estimation
by: Bhat, Vishak K, et al.
Published: (2026)

Scalable Multi-Task Learning for Particle Collision Event Reconstruction with Heterogeneous Graph Neural Networks
by: Sutcliffe, William, et al.
Published: (2025)

Introduction to the Usage of Open Data from the Large Hadron Collider for Computer Scientists in the Context of Machine Learning
by: Saala, Timo, et al.
Published: (2025)

A First Full Physics Benchmark for Highly Granular Calorimeter Surrogates
by: Buss, Thorsten, et al.
Published: (2025)

Designing Observables for Measurements with Deep Learning
by: Long, Owen, et al.
Published: (2023)

Model-Agnostic Signal Discovery with Machine Learning: Bridging the Gap Between Theory and Practice
by: Amram, Oz, et al.
Published: (2026)

Complete Optimal Non-Resonant Anomaly Detection
by: Kasieczka, Gregor, et al.
Published: (2024)

Non-resonant Anomaly Detection with Background Extrapolation
by: Bai, Kehang, et al.
Published: (2023)

Full Phase Space Resonant Anomaly Detection
by: Buhmann, Erik, et al.
Published: (2023)

Anomaly Detection in Collider Physics via Factorized Observables
by: Metodiev, Eric M., et al.
Published: (2023)

The Interplay of Machine Learning--based Resonant Anomaly Detection Methods
by: Golling, Tobias, et al.
Published: (2023)

Anomaly-aware summary statistic from data batches
by: Grosso, Gaia
Published: (2024)

Sensitivity to New Physics Phenomena in Anomaly Detection: A Study of Untunable Hyperparameters
by: de Souza, Fernando Abreu, et al.
Published: (2025)

Finetuning Foundation Models for Joint Analysis Optimization
by: Vigl, Matthias, et al.
Published: (2024)