Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Machine learning using rapidity-mass matrices for event classification
  problems in HEP   Supervised artificial neural networks with the rapidity-mass matrix (RMM)
inputs were studied using several Monte Carlo event samples for various pp
collision processes. The study shows the usability of this approach for general
event classification problems. The proposed standardization of the ANN feature
space can simplify searches for signatures of new physics at the LHC when using
machine learning techniques. In particular, we illustrate how to improve
signal-over-background ratios in searches for new physics, how to filter out
Standard Model events for model-agnostic searches, and how to separate gluon
and quark jets for Standard Model measurements.

34
The categories of this paper are: chem-ph
20
The categories of this paper are: acc-phys
9
The categories of this paper are: nlin
11
The categories of this paper are: hep-lat
1
The categories of this paper are: cs
36
The categories of this paper are: cmp-lg
4
The categories of this paper are: cond-mat
22
The categories of this paper are: chao-dyn
23
The categories of this paper are: solv-int
37
The categories of this paper are: supr-con
30
The categories of this paper are: q-alg
27
The categories of this paper are: comp-gas
25
The categories of this paper are: patt-sol
24
The categories of this paper are: dg-ga
32
The categories of this paper are: atom-ph
29
The categories of this paper are: funct-an