Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Using machine learning to speed up new and upgrade detector studies: a
  calorimeter case   In this paper, we discuss the way advanced machine learning techniques allow
physicists to perform in-depth studies of the realistic operating modes of the
detectors during the stage of their design. Proposed approach can be applied to
both design concept (CDR) and technical design (TDR) phases of future detectors
and existing detectors if upgraded. The machine learning approaches may speed
up the verification of the possible detector configurations and will automate
the entire detector R\&D, which is often accompanied by a large number of
scattered studies. We present the approach of using machine learning for
detector R\&D and its optimisation cycle with an emphasis on the project of the
electromagnetic calorimeter upgrade for the LHCb detector\cite{lhcls3}. The
spatial reconstruction and time of arrival properties for the electromagnetic
calorimeter were demonstrated.

18
The categories of this paper are: econ
37
The categories of this paper are: supr-con
7
The categories of this paper are: hep-th
14
The categories of this paper are: math-ph
35
The categories of this paper are: mtrl-th
19
The categories of this paper are: eess
12
The categories of this paper are: nucl-th
31
The categories of this paper are: ao-sci
15
The categories of this paper are: nucl-ex
29
The categories of this paper are: funct-an
9
The categories of this paper are: nlin
17
The categories of this paper are: q-fin
13
The categories of this paper are: quant-ph
5
The categories of this paper are: gr-qc
16
The categories of this paper are: stat
10
The categories of this paper are: q-bio