Anomaly Detection for High Energy Physics (AD4HEP) Workshop

16 Jun 2025, 13:00 → 18 Jun 2025, 13:00 America/New_York

Nevis Science Center (Columbia University, Nevis Laboratories)

Georgia Karagiorgi (Columbia University), Isobel Ojalvo (Princeton University), Julia Gonski (SLAC National Accelerator Laboratory)

--> Please note the extended abstract submission deadline to May 30!  

 

The AD4HEP Workshop brings together people working on and/or interested in experimental and theoretical/phenomenology aspects of anomaly detection in high energy physics or closely related fields.

The workshop includes invited plenary talks and plenary early career-contributed lightning talks, as well as useful hands-on tutorials for anomaly detection applications.

The workshop aims to connect and brew a community of scientists spanning experiment, theory and industry who are interested in a new paradigm for physics discoveries in particle physics and beyond, and to catalyze collaboration to develop new frameworks and tools for model-agnostic searches for new physics.

 

Scientific Organizing Committee:

Giuseppe Cerati, Fermi National Accelerator Laboratory
Marat Freytsis, Anthropic
Julia Gonski, SLAC National Accelerator Laboratory
Georgia Karagiorgi, Columbia University
Ben Nachman, Lawrence Berkeley National Laboratory
Isobel Ojalvo, Princeton University
Adrian Pol, Thomson Reuters
David Shih, Rutgers University

 

Local Organizing Committee:

Julia Gonski, SLAC National Accelerator Laboratory
Georgia Karagiorgi, Columbia University
Isobel Ojalvo, Princeton University
Andrew Loeliger, Princeton University

 

 

South_facade_of_the_Nevis_Mansion.jpg

Monday, 16 June

13:00 → 13:30 Opening Remarks

13:30 → 15:00 Physics Results with AD

13:30 Machine Learning-Driven Anomaly Detection in Dijet Events with ATLAS

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 machine learning strategies to estimate the background in different signal regions, with weakly supervised classifiers trained to differentiate this background estimate from actual data. We focus on high transverse momentum jets reconstructed as large-radius jets, using their mass and substructure as classifier inputs. After a classifier-based selection, we analyze the invariant mass distribution of the jet pairs for potential local excesses. Our model-independent results indicate no significant local excesses and we inject a representative set of signal models into the data to evaluate the sensitivity of our methods. This contribution discusses the used methods and latest results and highlights the potential of machine learning in enhancing the search for new physics in fundamental particle interactions.

Speaker: Dennis Noll

13:48 Results from CMS dijet anomaly search

Speaker: Oz Amram

14:06 Compare/contrast ATLAS YXH and SVJ searches

Speaker: Gabriel Matos (Columbia University)

14:24 Gaia satellite - steller stream finding

Speaker: Matt Buckley

15:00 → 15:30 Coffee Break

15:30 → 17:00 AD for Instrumentation

15:30 CMS AXOL1TL Trigger

15:48 ATLAS AD Trigger (L1+HLT)

Speaker: Max Cohen

16:06 CMS CICADA Trigger

Speaker: Kiley Kennedy

16:24 Towards a Self-Driving Trigger: Adaptive Response to Anomalies in Real Time

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 to respond to them in real time. We develop and benchmark an autonomous, or self-driving trigger framework that integrates anomaly detection with real-time control strategies, dynamically adjusting trigger thresholds and allocations in response to changing conditions. Using feedback-based control and resource-aware optimization that accounts for trigger bandwidth and compute constraints, our system maintains stable trigger rates while enhancing sensitivity to rare or unexpected signals. This work represents a step toward scalable, adaptive trigger systems and highlights new directions that harness the full potential of anomaly detection for real-time data acquisition.

Speaker: Cecilia Tosciri (University of Chicago)

17:30 → 19:30 Poster Session and Reception

Tuesday, 17 June

08:30 → 09:00 Breakfast

09:00 → 10:30 New Directions in AD

09:00 Foundation Models for AD

Speaker: Vinny Mikuni

09:18 AD Interpretation & Phenomenology

Speaker: Anna Hallin

09:36 Incorporating Physical Priors into Weakly-Supervised Anomaly Detection

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 search sensitivity by incorporating physical priors from a class of signal models into the weakly-supervised framework. PAWS pre-trains parameterized neural network classifiers on simulated signal events and then fine-tunes these models to distinguish data from a background reference, with the signal parameters themselves being learnable during this second stage. This approach allows PAWS to achieve the sensitivity of a fully-supervised search for signals within the pre-specified class, without needing to know the exact signal parameters in advance. Applied to the LHC Olympics anomaly detection benchmark, PAWS extends the discovery reach by a factor of 10 in cross section over previous methods. Crucially, PAWS demonstrates remarkable robustness to irrelevant noise features, unlike traditional methods whose performance degrades substantially. This work highlights the power of integrating domain knowledge into machine learning models for high energy physics, offering a promising path towards more sensitive and robust anomaly detection in jet-based searches and beyond.

Speaker: Chi Lung Cheng Cheng (University of Wisconsin-Madison)

09:54 Surrogate Simulation-based Inference (S2BI)

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 studied in the context of resonant anomaly detection, where the background simulation is learned from sidebands. The signal simulation is also learned from data by extending the Residual Anomaly Detection with Density Estimation (R-ANODE) in the context of S2BI. A key challenge with S2BI is that the inference task can be distracted by inaccuracies of the simulation. This is especially acute in the flexible signal case, as the signal probability density can readily absorb artifacts from the learned background model. One solution is to encode prior information into the signal simulation, limiting flexibility enough to avoid absorbing background deficiencies, but still able to find anomalies. We study this idea by extending the Prior-Assisted Weak Supervision (PAWS) method in the context of S2BI. Overall, we find that both methods achieve excellent sensitivity and now allow for direct statistical analysis from their output. S2BI PAWS achieves discovery sensitivity down to an initial signal-to-noise ratio of 0.1. This performance, combined with its statistically robust and interpretable outputs, establishes a new state-of-the-art for anomaly detection sensitivity.

Speaker: Runze Li (Yale University)

10:12 TBD

Speaker: Gaia Grosso

10:30 → 11:00 Coffee Break

11:00 → 12:30 Early Career Session: Anomaly Detection in HEP and Beyond

11:00 Latest improvements to CATHODE

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 specific signal model. One of the leading methods, CATHODE (Classifying Anomalies THrough Outer Density Estimation), is a two-step anomaly detection framework that constructs an in-situ background estimate using a generative model, followed by a classifier to isolate potential signal events.
We present the latest developments to the CATHODE method, aimed to increase its robustness broadening its applicability. These improvements expand its reach to new topologies with new input variables covering all particles in the event.

Speaker: Chitrakshee Yede (Universität Hamburg)

11:15 Hunting axions with the James Webb Space Telescope

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.

Speaker: Elena Pinetti (Flatiron Institute/Simons Foundation)

12:30 → 14:30 Nevis Labs Summer BBQ

14:30 → 15:30 Panel: Lessons Learned in AD

Convener: Julia Gonski (SLAC National Accelerator Laboratory)

15:30 → 16:00 Coffee Break

16:00 → 17:00 Hands-on: Tutorial 1

18:00 → 20:30 Social Outing (TBD)

Wednesday, 18 June

08:30 → 09:00 Breakfast

09:00 → 10:30 Early Career Session: Anomaly Detection in HEP and Beyond

09:00 Real-time anomaly detection on Liquid Argon Time Projection Chamber wire data

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 carry ionization energy deposition information, from Liquid Argon Time Projection Chambers (LArTPCs). In this talk, I will present the physics performance of such networks and share initial benchmarks toward hardware implementation and acceleration in FPGAs.

Speaker: Seokju Chung (Columbia University)

10:30 → 11:00 Coffee Break

11:00 → 12:00 Hands-on: Tutorial 2

12:00 → 12:30 Closeout