Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Learning the Lantern: Neural network applications to broadband photonic
  lantern modelling   Photonic lanterns allow the decomposition of highly multimodal light into a
simplified modal basis such as single-moded and/or few-moded. They are
increasingly finding uses in astronomy, optics and telecommunications.
Calculating propagation through a photonic lantern using traditional algorithms
takes $\sim 1$ hour per simulation on a modern CPU. This paper demonstrates
that neural networks can bridge the disparate opto-electronic systems, and when
trained can achieve a speed-up of over 5 orders of magnitude. We show that this
approach can be used to model photonic lanterns with manufacturing defects as
well as successfully generalising to polychromatic data. We demonstrate two
uses of these neural network models, propagating seeing through the photonic
lantern as well as performing global optimisation for purposes such as photonic
lantern funnels and photonic lantern nullers.

37
The categories of this paper are: supr-con
34
The categories of this paper are: chem-ph
4
The categories of this paper are: cond-mat
20
The categories of this paper are: acc-phys
30
The categories of this paper are: q-alg
33
The categories of this paper are: plasm-ph
24
The categories of this paper are: dg-ga
11
The categories of this paper are: hep-lat
17
The categories of this paper are: q-fin
16
The categories of this paper are: stat
14
The categories of this paper are: math-ph
13
The categories of this paper are: quant-ph
8
The categories of this paper are: hep-ex
28
The categories of this paper are: alg-geom
32
The categories of this paper are: atom-ph
1
The categories of this paper are: cs