Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Likelihood-free inference with neural compression of DES SV weak lensing
  map statistics   In many cosmological inference problems, the likelihood (the probability of
the observed data as a function of the unknown parameters) is unknown or
intractable. This necessitates approximations and assumptions, which can lead
to incorrect inference of cosmological parameters, including the nature of dark
matter and dark energy, or create artificial model tensions. Likelihood-free
inference covers a novel family of methods to rigorously estimate posterior
distributions of parameters using forward modelling of mock data. We present
likelihood-free cosmological parameter inference using weak lensing maps from
the Dark Energy Survey (DES) SV data, using neural data compression of weak
lensing map summary statistics. We explore combinations of the power spectra,
peak counts, and neural compressed summaries of the lensing mass map using deep
convolution neural networks. We demonstrate methods to validate the inference
process, for both the data modelling and the probability density estimation
steps. Likelihood-free inference provides a robust and scalable alternative for
rigorous large-scale cosmological inference with galaxy survey data (for DES,
Euclid and LSST). We have made our simulated lensing maps publicly available.

22
The categories of this paper are: chao-dyn
11
The categories of this paper are: hep-lat
2
The categories of this paper are: math
12
The categories of this paper are: nucl-th
37
The categories of this paper are: supr-con
30
The categories of this paper are: q-alg
9
The categories of this paper are: nlin
33
The categories of this paper are: plasm-ph
14
The categories of this paper are: math-ph
35
The categories of this paper are: mtrl-th
28
The categories of this paper are: alg-geom
7
The categories of this paper are: hep-th
29
The categories of this paper are: funct-an
21
The categories of this paper are: adap-org
10
The categories of this paper are: q-bio
17
The categories of this paper are: q-fin