ML4Jets2023

Session

Anomalies

8 Nov 2023, 16:00

Main Auditorium (DESY)

Conveners

Anomalies

• Huilin Qu (CERN)

Anomalies

• Vinicius Massami Mikuni (Lawrence Berkeley National Lab. (US))

Anomalies

• Michael Kraemer (Particle Physics)

Presentation materials

Zoom link

46. Back to the Roots: Tree-Based Algorithms for Weakly Supervised Anomaly Detection

Marie Hein (RWTH Aachen University)

08/11/2023, 16:00

Weakly supervised methods have emerged as a powerful tool for model agnostic anomaly detection at the LHC. While these methods have shown remarkable performance on specific signatures such as di-jet resonances, their application in a more model-agnostic manner requires dealing with a larger number of potentially noisy input features. We show that neural networks struggle with noisy input...

53. Robust Anomaly Detection in the Presence of Irrelevant Features

Yik Chuen San

08/11/2023, 16:15

Recent data-driven anomaly detection methods, such as CWoLA and ANODE, have shown promising results. However, they all suffer from performance degradation when irrelevant features are included. We demonstrate how these methods can be made robust even when the dataset is dominated by irrelevant features. The key idea is to employ Boosted Decision Tree (BDT)-based algorithms for...

20. Drapes: Diffusion for weak supervision

Mr Matthew Leigh (University of Geneva)

08/11/2023, 16:30

We employ the diffusion framework to generate background enriched templates to be used in a downstream Anomaly Detection task (generally with CWoLa). We show how Drapes can provide an analogue to many different methods of template generation, common in literature, and show good performance on the public RnD LHCO dataset.

5. The Interplay of Machine Learning–based Resonant Anomaly Detection Methods

Radha Mastandrea (University of California, Berkeley)

08/11/2023, 16:45

Machine learning--based anomaly detection (AD) methods are promising tools for extending the coverage of searches for physics beyond the Standard Model (BSM). One class of AD methods that has received significant attention is resonant anomaly detection, where the BSM is assumed to be localized in at least one known variable. While there have been many methods proposed to identify such a BSM...

128. Full Phase Space Resonant Anomaly Detection

Cedric Ewen

08/11/2023, 17:00

Physics beyond the Standard Model that is resonant in one or more dimensions has been the subject of many anomaly detection studies. This resonant anomaly detection is well-suited for weakly supervised machine learning, where sideband information can be used to generate synthetic datasets representing the Standard Model background. One effective strategy is to learn a conditional generative...

92. Combining resonant and tail-based anomaly detection

Gerrit Bickendorf (Universität Bonn)

08/11/2023, 17:15

In many well-motivated models of the electroweak scale, cascade decays of new particles can result in highly boosted hadronic resonances (e.g. $Z/W/h$). This can make these models rich and promising targets for recently developed resonant anomaly detection methods powered by modern machine learning. We demonstrate this using the state-of-the-art CATHODE method applied to supersymmetry...

86. Non-resonant Anomaly Detection with Background Extrapolation

Kehang Bai (University of Oregon (US))

09/11/2023, 09:00

Searching for non-resonant signals at the LHC is a relatively underexplored, yet challenging approach to discover new physics. These signals could arise from off-shell effects or final states with significant missing energy. This talk explores the potential of using weakly supervised anomaly detection to identify new non-resonant phenomena at the LHC. Our approach extends existing resonant...

68. R-ANODE

Ranit Das (Rutgers University)

09/11/2023, 09:15

We present improvements to model agnostic resonant anomaly detection based on normalizing flows.

132. Anomaly Detection in Collider Physics via Factorized Observables

Raymond Wynne (MIT)

09/11/2023, 09:30

To maximize the discovery potential of high-energy colliders, experimental searches should be sensitive to unforeseen new physics scenarios. This goal has motivated the use of machine learning for unsupervised anomaly detection. In this paper, we introduce a new anomaly detection strategy called FORCE: factorized observables for regressing conditional expectations. Our approach is based on the...

127. Unsupervised tagging of semivisible jets with normalized autoencoders in CMS

Florian Eble (ETH Zurich (CH))

09/11/2023, 09:45

Semivisible jets are a novel signature of dark matter scenarios where the dark sector is confining and couples to the Standard Model via a portal. They consist of jets of visible hadrons intermixed with invisible stable particles that escape detection. In this work, we use normalized autoencoders to tag semivisible jets in proton-proton collisions at the CMS experiment. Unsupervised models are...

12. Cluster Scanning

Mr Ivan Oleksiyuk (UNIGE)

09/11/2023, 16:15

We propose a new model independent method of new physics searches called cluster scanning (CS). It utilises
k-means algorithm to perform clustering in the space of low-level event or jet observables, and separates
potentially anomalous clusters to construct the anomaly rich region from the rest that form the anomaly
poor region. The spectra of the invariant mass in these two regions are...

42. Anomaly Detection in High Energy Physics via Non-Gaussian Variational Autoencoders

Thomas Dartnall Stern (University of Cape Town (ZA))

09/11/2023, 16:30

In particle physics, the search for phenomena outside the well-established predictions of the Standard Model (SM) is of great importance. For more than four decades, the SM has been the established theory of fundamental particles and their interactions. However, some aspects of nature remain elusive to the explanatory power of the SM. Thus, researchers' attention turns to the pursuit of new...

100. Quantum anomaly detection in the latent space of proton collision events at the LHC

Vasilis Belis (ETH Zurich (CH))

09/11/2023, 16:45

Exploring innovative methods and emerging technologies holds the promise of enhancing the capabilities of LHC experiments and contributing to scientific discoveries. In this work, we propose a new strategy for anomaly detection at the LHC based on unsupervised quantum machine learning algorithms. To accommodate the constraints on the problem size dictated by the limitations of current quantum...

139. Invited Talk: Low Latency and Anomaly Detection

Thea Aarrestad (ETH Zurich (CH))