Tagging $b$ quarks without tracks using an Artificial Neural Network algorithm
Abstract
Pixel detectors currently in use by high energy physics experiments such as ATLAS, CMS, LHCb, etc., are critical systems for tagging hadrons within particle jets. However, the performance of standard tagging algorithms begins to fall in the case of highly boosted hadrons (). This paper builds on the work of our previous study that uses the jump in hit multiplicity among the pixel layers when a hadron decays within the detector volume. First, multiple interactions within a finite luminous region were found to have little effect. Second, the study has been extended to use the multivariant techniques of an artificial neural network (ANN). After training, the ANN shows significant improvements to the ability to reject light-quark and charm jets; thus increasing the expected significance of the technique.
Cite
@article{arxiv.1701.06832,
title = {Tagging $b$ quarks without tracks using an Artificial Neural Network algorithm},
author = {B. Todd Huffman and Thomas Russell and Jeff Tseng},
journal= {arXiv preprint arXiv:1701.06832},
year = {2017}
}
Comments
14 pages, 9 color figures. arXiv admin note: text overlap with arXiv:1604.05036 Changes: Additional study of changes in search cone. Figures 7, 8, and 9 are new. This version has been re-submitted to Journal of Physics G: Nuclear and Particle Physics