E-resources
Peer reviewed
-
Zaborenko, A. D.; Volkov, P. V.; Dudko, L. V.; Perfilov, M. A.
Moscow University physics bulletin, 12/2023, Volume: 78, Issue: Suppl 1Journal Article
Recent advancements in model-independent approaches in high energy physics have encountered challenges due to the limited effectiveness of unsupervised algorithms when compared to their supervised counterparts. In this paper, we present a novel approach utilizing a one-class deep neural network (DNN) to achieve accuracy levels comparable to supervised learning methods. Our proposed novelty detection algorithm uses a multilayer perceptron to learn and distinguish a specific class from simulated noise signals. By training on a single class, our algorithm constructs a hyperplane similar to one-class support vector machines (SVMs) but with enhanced accuracy and significantly reduced training and inference times. This research contributes to the advancement of model-independent techniques for uncovering New Physics phenomena, showcasing the potential of one-class DNNs as a viable alternative to traditional supervised learning approaches. For the demonstration of the method, the distinguishing of flavour changing neutral currents in top quark interactions from the Standard Model processes has been considered. The obtained results demonstrate the effectiveness of our proposed algorithm, paving the way for improved anomaly detection and exploration of uncharted territories in high energy physics.
![loading ... loading ...](themes/default/img/ajax-loading.gif)
Shelf entry
Permalink
- URL:
Impact factor
Access to the JCR database is permitted only to users from Slovenia. Your current IP address is not on the list of IP addresses with access permission, and authentication with the relevant AAI accout is required.
Year | Impact factor | Edition | Category | Classification | ||||
---|---|---|---|---|---|---|---|---|
JCR | SNIP | JCR | SNIP | JCR | SNIP | JCR | SNIP |
Select the library membership card:
If the library membership card is not in the list,
add a new one.
DRS, in which the journal is indexed
Database name | Field | Year |
---|
Links to authors' personal bibliographies | Links to information on researchers in the SICRIS system |
---|
Source: Personal bibliographies
and: SICRIS
The material is available in full text. If you wish to order the material anyway, click the Continue button.