Conveners
Anomalies
- Huilin Qu (CERN)
Anomalies
- Vinicius Massami Mikuni (Lawrence Berkeley National Lab. (US))
Anomalies
- Michael Kraemer (Particle Physics)
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...
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.
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...
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...
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...
We present improvements to model agnostic resonant anomaly detection based on normalizing flows.
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...
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...
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...
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...
