Conveners
Anomaly detection
- Barry Dillon (Ulster University)
Anomaly detection
- Jennifer Ngadiuba (FNAL)
The development of analysis methods that can distinguish potential beyond the Standard Model phenomena in a model-agnostic way can significantly enhance the discovery reach in collider experiments. However, the typical machine learning (ML) algorithms employed for this task require fixed length and ordered inputs that break the natural permutation invariance in collider events. To address this...
There has been significant work recently in developing machine learning (ML) models in high energy physics (HEP) for tasks such as classification, simulation, and anomaly detection. Often these models are adapted from those designed for datasets in computer vision or natural language processing, which lack inductive biases suited to HEP data, such as equivariance to its inherent symmetries....
In the realm of high-energy physics, the use of graph network-based implementations offers the advantage of handling input datasets more closely aligned with their collection process in collider experiments. GNN-based approaches address the graph anomaly detection problem by utilizing information about graph features and structures to effectively learn to score anomalies. We represent a single...
We introduce a model-agnostic search for new physics in the dijet final state. Other than the requirement of a narrow dijet resonance with a mass in the range of 1.8-6 TeV, minimal additional assumptions are placed on the signal hypothesis. Search regions are obtained by utilizing multivariate machine learning methods to select jets with anomalous substructure. A collection of complementary...
A key step in any resonant anomaly detection search is accurate estimation of the background distribution in each signal region. Data-driven methods like CATHODE accomplish this by training separate density estimators on the complement of each signal region, and interpolating them into their corresponding signal regions. Having to re-train the density estimator on essentially the entire...
We introduce TRANSIT, a conditional adversarial network for continuous interpolation of data. It is designed to construct a background data template for semi-supervised searches for new physics processes at the LHC, by smoothly transforming sideband events to match signal region mass distributions.
We demonstrate the performance of TRANSIT using the LHC Olympics R&D dataset. The method...
