Ben Nachman
(Lawrence Berkeley National Laboratory),
David Shih
(Rutgers University),
Gregor Kasieczka
(Institut fuer Experimentalphysik / UHH)
16/07/2020, 16:00
Flavia Dias
(Nikhef), Dr
Flavia de Almeida Dias
(Nikhef)
16/07/2020, 16:20
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...
David Jaroslawski
(Rutgers University),
Kevin Nash
(Rutgers University)
16/07/2020, 16:40
We build on the previous application of auto-encoders to particle physics by including an analysis of the latent space variables.
Oz Amram
(Johns Hopkins University)
16/07/2020, 17:00
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.
Dr
Barry Dillon
(Jozef Stefan Institute)
16/07/2020, 18:00
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...
Vinicius Mikuni
(UZH)
16/07/2020, 18:20
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.
Rute Pedro
(LIP -Laboratorio de Instrumentacao e Fisica Experimental de Particulas)
16/07/2020, 18:40
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...
Tommaso Dorigo
(INFN - sezione di Padova)
17/07/2020, 16:00
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...
Charanjit Kaur Khosa
(University of Sussex)
17/07/2020, 16:20
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.
Mr
Sangeon Park
(Massachusetts Institute of Technology)
17/07/2020, 16:40
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...
Taoli Cheng
(Mila, University of Montreal)
17/07/2020, 17:40
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).
Alan Kahn
(Columbia University)
17/07/2020, 18:00
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...
Ben Nachman
(Lawrence Berkeley National Laboratory),
David Shih
(Rutgers University),
Gregor Kasieczka
(Institut fuer Experimentalphysik / UHH)
17/07/2020, 18:20
