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Dennis Noll16/06/2025, 13:30Plenary Talk - Physics Results with AD
This contribution discusses an anomaly detection search for narrow-width resonances beyond the Standard Model that decay into a pair of jets. Using 139 fb−1 of proton-proton collision data at sqrt(s) = 13 TeV, recorded from 2015 to 2018 with the ATLAS detector at the Large Hadron Collider, we aim to identify new physics without relying on a specific signal model. The analysis employs two...
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Oz Amram16/06/2025, 13:48
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Gabriel Matos (Columbia University)16/06/2025, 14:06
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Edison Weik (New York University)16/06/2025, 14:25Plenary Talk - Physics Results with AD
This work presents a strategy for model-independent searches for new physics using deep unsupervised learning techniques at the LHC. The first study applies a deep autoencoder to 140fb^-1 of sqrt(s)=13TeV pp collision data recorded by the ATLAS detector, selecting outlier events that deviate from the dominant Standard Model kinematics. Invariant mass distribuations of various two-body systems...
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Melissa Quinnan (UC San Diego)16/06/2025, 15:30
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Max Cohen16/06/2025, 15:48
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Kiley Kennedy, Kiley Kennedy (Princeton University (US))16/06/2025, 16:06
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Cecilia Tosciri (University of Chicago)16/06/2025, 16:24Plenary Talk - AD for Instrumentation
Recent efforts in high energy physics have explored a variety of machine learning approaches for detecting anomalies in collision data. While most are designed for offline analysis, applications at the trigger level are emerging. In this work, we introduce a novel framework for autonomous triggering that not only detects anomalous patterns directly at the trigger level but also determines how...
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Daniel Ratner (SLAC)16/06/2025, 16:42Plenary Talk - AD for Instrumentation
Modern light sources produce too many signals for a small operations team to monitor in real time. As a result, recovering from faults can require long downtimes, or even worse, performance issues may persist undiscovered. Existing automated methods tend to rely on pre-set limits which either miss subtle problems or produce too many false positives. AI methods can solve both problems, but...
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Vinny Mikuni17/06/2025, 09:00
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Anna Hallin17/06/2025, 09:18
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Chi Lung Cheng Cheng (University of Wisconsin-Madison)17/06/2025, 09:36Plenary Talk - New Directions in AD
Weakly-supervised anomaly detection methods offer a powerful approach for discovering new physics by comparing data to a background-only reference. However, the sensitivity of existing strategies can be significantly limited by rare signals or high-dimensional, noisy feature spaces. We present Prior-Assisted Weak Supervision (PAWS), a novel machine-learning technique that significantly boosts...
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Runze Li (Yale University)17/06/2025, 09:54Plenary Talk - New Directions in AD
Modern simulation-based inference (SBI) is a suite of tools to scaffold physics-based simulations with neural networks and other tools to perform inference using as much information as possible. We extend this toolkit to the case where some or all of the simulations are actually surrogate models learned directly from the data. This Surrogate Simulation-based Inference (S2BI) concept is...
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Gaia Grosso (IAIFI/MIT/Harvard)17/06/2025, 10:12Plenary Talk - New Directions in AD
Anomaly detection in high energy physics (HEP) and many other scientific fields, is challenged by rare signals found in high-dimensional data. Two main strategies have emerged to mitigate the curse of dimensionality: scaling detection methods to handle high dimensions, or reducing the dimensionality before statistical analysis.
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This talk focuses on the latter, introducing a supervised... -
Chitrakshee Yede (Universität Hamburg)17/06/2025, 11:00Early Career Lightning Talk
The search for physics beyond the Standard Model remains one of the primary focus in high-energy physics. Traditional searches at the LHC analyses, though comprehensive, have yet to yield signs of new physics. Anomaly detection has emerged as a powerful tool to widen the discovery horizon, offering a model-agnostic path as way to enhance the sensitivity of generic searches not targeting any...
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Jack Cleeve17/06/2025, 11:15Early Career Lightning Talk
We introduce an end-to-end, self-supervised neural workflow that learns the three-dimensional electric-field distortion in a Liquid Argon TPC directly from through-going cosmic muon tracks. The network uses geometric straightness and boundary conditions to generate its own target undistorted paths and iteratively refine them. In this talk, I will present the architecture of the model, detail...
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Prasanth Shyamsundar (Fermi National Accelerator Laboratory)17/06/2025, 11:30Early Career Lightning Talk
Recently a technique based on k-fold cross validation has become popular in anomaly detection in HEP, as a way to search for a wide class of anomalies in the data, without paying an associated penalty in sensitivity-depth. In this work, we point out that the breadth-depth tradeoff is an unavoidable aspect of anomaly detection, and cannot be overcome using the aforementioned k-fold cross...
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Ranit Das (Rutgers University)17/06/2025, 11:45Plenary Talk - New Directions in AD
Based on R-ANODE(https://arxiv.org/abs/2312.11629) and SIGMA(https://arxiv.org/abs/2410.20537).
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Zixin Ding (University of Chicago)17/06/2025, 12:00Early Career Lightning Talk
The Large Hadron Collider (LHC) produces collision data at the rate of 40 MHz, making it infeasible to store or analyze all events. Conventional trigger strategies at the experiments operating at the LHC typically rely on predefined physics signatures to decide which events to retain. However, possible manifestations of new physics may not conform to these established patterns and thus risk...
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Radha Mastandrea (UC Berkeley)18/06/2025, 10:30Early Career Lightning Talk
We present the first study of anti-isolated Upsilon decays to two muons (Υ → μ+μ−) in proton-proton collisions at the Large Hadron Collider. Using a machine learning (ML)-based anomaly detection strategy (CATHODE), we “rediscover” the Υ in 13 TeV CMS Open Data from 2016, despite overwhelming anti-isolated backgrounds. CATHODE can elevate the signal significance to 6.4σ (starting from 1.6σ...
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Rikab Gambhir (Massachusetts Institute of Technology)18/06/2025, 10:45Early Career Lightning Talk
Many Machine Learning (ML)-based Anomaly Detection (AD) methods are based on "extended bump hunt", where ML cuts are used to enhance the significance of signal in a bump. We show how, by taking advantage of the fact that ML methods (e.g. CATHODE with CWoLa) often involve learning the likelihood ratio of the signal and background hypotheses, events can be weighted rather than cut for improved...
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Wonyong Chung (Princeton University)18/06/2025, 11:00Early Career Lightning Talk
Physically-grounded anomaly detection requires reconstruction pipelines that preserve, rather than obscure, detector information. I introduce a two-stage strategy that inserts a synthetic, detector-aware representation (S) between the raw calorimeter readout (D) and low-dimensional truth (T). I demonstrate the concept in full simulation of a segmented dual-readout crystal ECAL for future...
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Seokju Chung (Columbia University)18/06/2025, 11:15
Real-time anomaly triggers based on autoencoders have been successfully demonstrated at the CMS experiment, including CICADA (operating on raw calorimeter data from ECAL and HCAL) and AXOL1TL (operating on trigger-level reconstructed objects such as electrons, taus, and jets). Motivated by the CICADA project, we explore the feasibility of applying similar techniques to raw wire data, which...
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Tengfei Ma (Stony Brook University)18/06/2025, 11:30Early Career Lightning Talk
This work introduces a new interpretable time series foundation model, and its application to anomaly prediction for sPHENIX - a recently commissioned detector at the RHIC facility at BNL. Our goal is to monitor the detector’s operational status, identify early warning signs, and predict potential anomalies. Despite diverse time series modeling techniques, existing models are black boxes and...
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Adriano Cherchiglia (Universidade Estadual de Campinas)18/06/2025, 11:45Early Career Lightning Talk
As neutrino experiments enter into the precision era, it is desirable to identify deviations from data in comparison to theory as well as provide possible models as explanation. Particularly useful is the description in terms of non-standard interactions (NSI), which can be related to neutral (NC-NSI) or charged (CC-NSI) currents. Previously, we have developed the code eft-neutrino that...
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Matt Buckley
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Elena Pinetti (Flatiron Institute/Simons Foundation)Early Career Lightning Talk
Axions with a mass around 1 eV can decay into near-infrared photons. Utilising blank-sky observations from the James Webb Space Telescope, I search for a narrow emission line due to decaying dark matter and derive leading constraints on the axion-photon coupling in the eV-scale mass range.
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Seokju Chung (Columbia University)Early Career Lightning Talk
Real-time anomaly triggers based on autoencoders have been successfully demonstrated at the CMS experiment, including CICADA (operating on raw calorimeter data from ECAL and HCAL) and AXOL1TL (operating on trigger-level reconstructed objects such as electrons, taus, and jets). Motivated by the CICADA project, we explore the feasibility of applying similar techniques to raw wire data, which...
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Gaia Grosso
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