Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Machine Learning Enabled Lineshape Analysis in Optical Two-Dimensional
  Coherent Spectroscopy   Optical two-dimensional (2D) coherent spectroscopy excels in studying
coupling and dynamics in complex systems. The dynamical information can be
learned from lineshape analysis to extract the corresponding linewidth.
However, it is usually challenging to fit a 2D spectrum, especially when the
homogeneous and inhomogeneous linewidths are comparable. We implemented a
machine learning algorithm to analyze 2D spectra to retrieve homogeneous and
inhomogeneous linewidths. The algorithm was trained using simulated 2D spectra
with known linewidth values. The trained algorithm can analyze both simulated
(not used in training) and experimental spectra to extract the homogeneous and
inhomogeneous linewidths. This approach can be potentially applied to 2D
spectra with more sophisticated spectral features.

5
The categories of this paper are: gr-qc
17
The categories of this paper are: q-fin
23
The categories of this paper are: solv-int
34
The categories of this paper are: chem-ph
37
The categories of this paper are: supr-con
30
The categories of this paper are: q-alg
8
The categories of this paper are: hep-ex
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The categories of this paper are: hep-th
26
The categories of this paper are: bayes-an
14
The categories of this paper are: math-ph
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The categories of this paper are: econ
21
The categories of this paper are: adap-org
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The categories of this paper are: q-bio
16
The categories of this paper are: stat
22
The categories of this paper are: chao-dyn
24
The categories of this paper are: dg-ga