Conveners
BSM: Overdensity Methods
- Caterina Doglioni (Lund University (SE))
- Mihoko Nojiri (Theory Center, IPNS, KEK)
BSM: Latent Space Anomaly Detection
- Caterina Doglioni (Lund University (SE))
- Mihoko Nojiri (Theory Center, IPNS, KEK)
BSM: Data Space Searches with Autoencoders
- Caterina Doglioni (Lund University (SE))
- Mihoko Nojiri (Theory Center, IPNS, KEK)
Experiments at a future $e^{+}e^{-}$ collider will be able to search for new particles with masses below the nominal centre-of-mass energy by analyzing collisions with initial-state radiation (radiative return). We show that machine learning methods based on semisupervised and weakly supervised learning can achieve model-independent sensitivity to the production of new particles in radiative...
We propose Classifier-based Anomaly detection THrough Outer Density Estimation (CATHODE), a new approach to search for resonant new physics at the LHC in a model-agnostic way. In CATHODE, we train a conditional density estimator on additional features in the sideband region, interpolate it into the signal region, and sample from it. This produces in a data-driven way events that follow the SM...
We explore the robustness of the CATHODE (Classifier-based Anomaly detection THrough Outer Density Estimation) method against correlation in the input features. We also compare CATHODE to other related approaches, specifically ANODE and CWoLa Hunting. Using the LHCO R&D dataset, we will demonstrate that in the absence of feature correlations, CATHODE outperforms both ANODE and CWoLa Hunting,...
A unsupervised learning tool that searches for localized, overdense regions of the copula space of a multidimensional feature space is discussed. The algorithm, named RanBox, exists in two versions - one which searches multiple times in random subspaces (typically of 8 to 12 dimensions) of the feature space, and a second one (RanBoxIter) which iteratively adds dimensions to the searched space....
The Energy Movers Distance was recently proposed as an advantageous metric to distinguish certain types of signals at the LHC. We explore generalizations of this distance to multiple families of signals and find similar performance anomaly detection through variational autoencoders. We investigate this connection by exploring the correlation of event distances with distances in the latent...
We devise an autoencoder based strategy to facilitate anomaly detection for boosted jets, employ-
ing Graph Neural Networks (GNNs) to do so. To overcome known limitations of GNN autoencoders,
we design a symmetric decoder capable of simultaneously reconstructing edge features and node fea-
tures. Focusing on latent space based discriminators, we find that such setups provide a...
We describe the outcome of a data challenge conducted as part of the Dark Machines Initiative and the Les Houches 2019 workshop on Physics at TeV colliders. The challenge aims at detecting signals of new physics at the LHC using unsupervised learning algorithms. We define and describe a large benchmark dataset, consisting of > 1 Billion simulated LHC events. We then review a wide range of...
We show how an anomaly detection algorithm could be integrated in a typical search for new physics in events with jets at the CERN Large Hadron Collider (LHC). We assume that an anomaly detection algorithm is given, trained to identify rare jet types, such as jets originating from the decay of a highly boosted massive particle. We demonstrate how this algorithm could be integrated in a search...
Models with dark showers represent one of the most challenging possibilities for new physics at the LHC. One of the most difficult examples is a novel collider signature called a Soft Unclustered Energy Pattern (SUEP), which can arise in certain BSM models with a hidden valley sector that is both pseudo-conformal and strongly coupled over a large range of energy scales. Large-angle emissions...
As an alternative approach (w.r.t. deep generative models) for detecting out-of-distribution samples, we explore the possibility of employing jet classifiers as anomalous jet taggers. We also discuss the advantages and limitations of different approaches.
Anomaly detection techniques offer exciting possibilities to significantly extend the search for new physics at the Large Hadron Collider (LHC) in a model-agnostic approach. We study how Generative Adversarial Networks could be used for this purpose, using the LHC Olympics 2020 dataset as an example.
