16-17 July 2020
Europe/Berlin timezone
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Contribution List

Displaying 8 contributions out of 8
In this talk we will present a new algorithm to search for new physics called Anomaly Awareness. By making our algorithm 'aware' of the presence of a range of different anomalies, we improve its capability to detect anomalous events even when it hasn't been exposed to them in the past. As an example, we apply this method to boosted jets and use it to uncover new resonances or EFT effects.
Presented by Charanjit Kaur KHOSA
We present results of an anomaly detection method using a Variational Recurrent Neural Network trained on the constituent 4-vectors of large-radius jets. By training on a contaminated dataset of largely light QCD jets with some small amount of signal events, we can identify potential new physics objects due to their unique substructure without the need of a pre-determined model hypothesis. We focu ... More
Presented by Alan KAHN
We have previously proposed Tag N' Train as a new technique for anomaly searches that utilizes the Classification Without Labels method of training on data in a novel way. I will overview the technique and discuss what lessons we learned from the results of Blackbox 1. I will also discuss what questions we would like to answer about the technique going forward and other potential applications.
Presented by Oz AMRAM
We propose a method for unsupervised multiclass classification based on the clustering of events in the embedding space. We show how the method creates unsupervised clusters for different processes and how new physics can be studies with this strategy.
Presented by Vinicius MIKUNI
The search for anomalies in HEP data has to reckon with large-dimensional spaces, with features whose PDF varies widely over their support. RanBox addresses this challenge by the combination of PCA and the integral transform, flattening all marginals and then searching for overdensities in the copula space. The algorithm is able to spot injected signals of down to a few permille fractions in to ... More
Presented by Tommaso DORIGO
We build on the previous application of auto-encoders to particle physics by including an analysis of the latent space variables.
Presented by David JAROSLAWSKI, Kevin NASH
This Letter describes a search for resonant new physics using a machine-learning anomaly detection procedure that does not rely on a signal model hypothesis. Weakly supervised learning is used to train classifiers directly on data to enhance potential signals. The targeted topology is dijet events and the features used for machine learning are the masses of the two jets. The resulting analysis is ... More
Presented by Flavia DIAS
We describe a technique to learn the underlying structure of collider events directly from the data, without having a particular theoretical model in mind. It allows to infer aspects of the theoretical model that may have given rise to this structure, and can be used to cluster or classify the events for analysis purposes. The unsupervised machine-learning technique is based on the probabilistic ( ... More
Presented by Dr. Barry DILLON