Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Efficient Antihydrogen Detection in Antimatter Physics by Deep Learning   Antihydrogen is at the forefront of antimatter research at the CERN
Antiproton Decelerator. Experiments aiming to test the fundamental CPT symmetry
and antigravity effects require the efficient detection of antihydrogen
annihilation events, which is performed using highly granular tracking
detectors installed around an antimatter trap. Improving the efficiency of the
antihydrogen annihilation detection plays a central role in the final
sensitivity of the experiments. We propose deep learning as a novel technique
to analyze antihydrogen annihilation data, and compare its performance with a
traditional track and vertex reconstruction method. We report that the deep
learning approach yields significant improvement, tripling event coverage while
simultaneously improving performance by over 5% in terms of Area Under Curve
(AUC).

2
The categories of this paper are: math
27
The categories of this paper are: comp-gas
9
The categories of this paper are: nlin
34
The categories of this paper are: chem-ph
20
The categories of this paper are: acc-phys
11
The categories of this paper are: hep-lat
26
The categories of this paper are: bayes-an
25
The categories of this paper are: patt-sol
24
The categories of this paper are: dg-ga
15
The categories of this paper are: nucl-ex
0
The categories of this paper are: hep-ph
13
The categories of this paper are: quant-ph
14
The categories of this paper are: math-ph
18
The categories of this paper are: econ
22
The categories of this paper are: chao-dyn
6
The categories of this paper are: astro-ph