Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Fast and precise model calculation for KATRIN using a neural network   We present a fast and precise method to approximate the physics model of the
Karlsruhe Tritium Neutrino (KATRIN) experiment using a neural network. KATRIN
is designed to measure the effective electron anti-neutrino mass using the
kinematics of beta-decay with a sensitivity of 200 meV at 90% confidence level.
To achieve this goal, a highly accurate model prediction with relative errors
below the 1e-4-level is required. Using the regular numerical model for the
analysis of the final KATRIN dataset is computationally extremely costly or
requires approximations to decrease the computation time. Our solution to
reduce the computational requirements is to train a neural network to learn the
predicted beta-spectrum and its dependence on all relevant input parameters.
This results in a speed-up of the calculation by about three orders of
magnitude, while meeting the stringent accuracy requirements of KATRIN.

21
The categories of this paper are: adap-org
34
The categories of this paper are: chem-ph
0
The categories of this paper are: hep-ph
23
The categories of this paper are: solv-int
37
The categories of this paper are: supr-con
28
The categories of this paper are: alg-geom
4
The categories of this paper are: cond-mat
31
The categories of this paper are: ao-sci
29
The categories of this paper are: funct-an
33
The categories of this paper are: plasm-ph
12
The categories of this paper are: nucl-th
19
The categories of this paper are: eess
24
The categories of this paper are: dg-ga
10
The categories of this paper are: q-bio
13
The categories of this paper are: quant-ph
14
The categories of this paper are: math-ph