Conveners
Anomaly Detection
- Elham E Khoda (University of Washington (US))
- Dylan Sheldon Rankin (Massachusetts Inst. of Technology (US))
Anomaly Detection
- Barry Dillon (University of Heidelberg)
- Lawrence Lee Jr (University of Tennessee (US))
Anomaly Detection
- Yuri Gershtein (Rutgers State Univ. of New Jersey (US))
- David Shih
I will give an overview of recent progress in less-than-supervised methods for new physics searches at the LHC.
An application of unsupervised machine learning-based anomaly detection to a generic dijet resonance is presented using the full LHC Run 2 dataset collected by ATLAS. A novel variational recurrent neural network (VRNN) is trained over data, specifically large-radius jets that are modeled using a sequence of constituent four-vectors and substructure variables, to identify anomalous jets based...
Anomaly Detection algorithms are crucial tools for identifying unusual decays from proton collisions at the LHC and are efficient methods for seeking out the possibility of new physics. These detection algorithms should be robust against nuisance kinematic variables and detector conditions. To achieve this robustness, popular detection models built via autoencoders, for example, have to go...
I discuss several approaches to anomaly detection in collider physics, including using variational autoencoders, which rely on the ability to reconstruct certain types of data (background) but not others (signals), and optimal transport distances, which which measures how easily one pT distribution can be changed into another. I discuss advantages and challenges associated with each approach....
We introduce a new model independent technique for constructing background data templates for use in searches for new physics processes at the LHC.
This method, called CURTAINs, uses invertible neural networks to parametrise the distribution of side band data as a function of the resonant observable. The network learns a transformation to map any data point from its value of the resonant...
Machine learning-based anomaly detection techniques offer exciting possibilities to significantly extend the search for new physics at the Large Hadron Collider (LHC) and elsewhere by reducing the model dependence. In this work, we focus on resonant anomaly detection, where generative models can be trained in sideband regions and interpolated into a signal region to provide an estimate of the...
Resonant anomaly detection is a promising framework for model-independent searches for new particles. Weakly supervised resonant anomaly detection methods compare data with a potential signal against a template of the Standard Model (SM) background inferred from sideband regions. We propose a means to generate this background template that uses a normalizing flow to create a mapping between...
We introduce a new technique named Latent CATHODE (LaCATHODE) for performing "enhanced bump hunts", a type of resonant anomaly search that combines conventional one-dimensional bump hunts with a model-agnostic anomaly score in an auxiliary feature space where potential signals could also be localized. The main advantage of LaCATHODE over existing methods is that it provides an anomaly score...
Following the previous work of leveraging Standard Model jet classifiers as generic anomalous jet taggers (https://arxiv.org/abs/2201.07199), we present an analysis of regularized SM jet classifiers serving as anti-QCD taggers. In the second part of the presentation, from the perspective of interdisciplinary research, we initiate a discussion on the opportunities and challenges involved in the...
We apply the artificial event variable technique, a deep neural network with an information bottleneck, to strongly coupled hidden sector models. These models of physics beyond the standard model predict collider production of invisible, composite dark matter candidates mixed with regular hadrons in the form of semivisible jets. We explore different resonant production mechanisms to determine...
There is a growing recent interest in endowing the space of collider events with a metric structure calculated directly in the space of its inputs. For quarks and gluons, the recently developed energy mover's distance has allowed for a quantification of what is different between physical events. However, the large number of particles within jets makes using metrics and interpreting these...
We present a novel computational approach for extracting weak signals, whose exact location and width may be unknown, from complex background distributions with an arbitrary functional form. We focus on datasets that can be naturally presented as binned integer counts, demonstrating our approach on the datasets from the Large Hadron Collider. Our approach is based on Gaussian Process (GP)...
