16-17 July 2020
Virtual
Europe/Berlin timezone
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Anomaly Detection

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Location: Virtual
Date: from 16 Jul 16:00 to 17 Jul 19:00

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Displaying 17 contributions out of 17
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 on 17/7/2020 at 16:20
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 on 17/7/2020 at 16:00
In this note we review model-agnostic approaches to searches for new physics signatures using normalizing flow and latent variable models. We also propose a bootstrap method that allows us to estimate the continuous densities that form the likelihood ratio between the background and signal-plus-background hypothesis with minimal a priori knowledge of the signal structure.
Presented by Justin TAN on 16/7/2020 at 15:20
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 on 16/7/2020 at 15:00
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 on 16/7/2020 at 18:20
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 on 17/7/2020 at 14:00
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 on 16/7/2020 at 14:40
In a previous paper we observed that Deep Neural Networks trained on specific signals still performed well in discriminating new signals unseen during training, indicating the transferrable nature of Deep Learning in HEP applications and their potential to perform model-independent searches in the LHC data. Recently, we explored semi-supervised learning techniques - both shallow and deep - and com ... More
Presented by Rute PEDRO on 16/7/2020 at 18:40
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, Dr. Flavia DE ALMEIDA DIAS on 16/7/2020 at 16:20
on 17/7/2020 at 16:40
This talk presents a summary of our tested anomaly detection models. We are studying the performance of two approaches on the LHC Olympics datasets, one based on Adversarial Autoencoders and the other using a Normalizing Flows method (arxiv:2003.13913). Combining those methods with traditional “bump-hunting” algorithms (​https://github.com/lovaslin/pyBumpHunter​) , we attempt to uncover th ... More
Presented by Ioan DINU on 17/7/2020 at 17:00
Presented by Gregor KASIECZKA, David SHIH, Ben NACHMAN on 16/7/2020 at 14:00
Motivated by the LHCOlympics games, we explore a few prototyping anomaly detection methods, potentially including Variational Autoencoder based anomalous jet taggers (https://arxiv.org/abs/2007.01850).
Presented by Taoli CHENG on 17/7/2020 at 15:40
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 on 16/7/2020 at 18:00
For many classes of new physics models, there is a broad set of underlying physics features we can assume about any new signal. With QUAK, we aim to embed these assumptions into our search while still preserving the model-independence of the search. The development of this approach would thus open an avenue of quasi-model dependent searches, which we believe can build a bridge between the conventi ... More
Presented by Mr. Sangeon PARK on 17/7/2020 at 16:40
Presented by Gregor KASIECZKA, David SHIH, Ben NACHMAN on 17/7/2020 at 16:20
The Gaia space telescope is mapping the kinematics of the nearest and brightest billion stars in the Milky Way with unprecedented precision. Structures such as streams and tidal debris in the star's phase space can provide evidence for the assembly history of the Galaxy, and perhaps reveal information about the particle physics of dark matter. Identifying such structures in the high-dimensional da ... More
Presented by Prof. Matthew BUCKLEY on 17/7/2020 at 13:40
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