Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: LHC Higgs CP Sensitive Observables in H to tau^+ tau^-; tau^pm to
  (3pi)^pm nu and Machine Learning Benefits   In phenomenological preparation for new measurements one searches for the
carriers of quality signatures. Often, the first approach quantities may be
difficult to measure or to provide sufficiently precise predictions for
comparisons.
  Complexity of necessary details grow with precision. To achieve the goal one
can not break the theory principles, and take into account effects which could
be ignored earlier. Mixed approach where dominant effects are taken into
account with intuitive even simplistic approach was developed.
  Non dominant corrections were controlled with the help of Monte Carlo
simulations.
  Concept of Optimal Variables was successfully applied for many measurements.
New techniques, like Machine Learning, offer solutions to exploit
multidimensional signatures. Complementarity of these new and old approaches is
studied for the example of Higgs Boson CP-parity measurements in H to
tau^+tau^-, tau^pm \to nu (3pi)^pm cascade decays.

15
The categories of this paper are: nucl-ex
20
The categories of this paper are: acc-phys
23
The categories of this paper are: solv-int
1
The categories of this paper are: cs
22
The categories of this paper are: chao-dyn
9
The categories of this paper are: nlin
25
The categories of this paper are: patt-sol
36
The categories of this paper are: cmp-lg
5
The categories of this paper are: gr-qc
10
The categories of this paper are: q-bio
17
The categories of this paper are: q-fin
31
The categories of this paper are: ao-sci
34
The categories of this paper are: chem-ph
24
The categories of this paper are: dg-ga
16
The categories of this paper are: stat
2
The categories of this paper are: math