Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Learning an Astronomical Catalog of the Visible Universe through
  Scalable Bayesian Inference   Celeste is a procedure for inferring astronomical catalogs that attains
state-of-the-art scientific results. To date, Celeste has been scaled to at
most hundreds of megabytes of astronomical images: Bayesian posterior inference
is notoriously demanding computationally. In this paper, we report on a
scalable, parallel version of Celeste, suitable for learning catalogs from
modern large-scale astronomical datasets. Our algorithmic innovations include a
fast numerical optimization routine for Bayesian posterior inference and a
statistically efficient scheme for decomposing astronomical optimization
problems into subproblems.
  Our scalable implementation is written entirely in Julia, a new high-level
dynamic programming language designed for scientific and numerical computing.
We use Julia's high-level constructs for shared and distributed memory
parallelism, and demonstrate effective load balancing and efficient scaling on
up to 8192 Xeon cores on the NERSC Cori supercomputer.

23
The categories of this paper are: solv-int
27
The categories of this paper are: comp-gas
22
The categories of this paper are: chao-dyn
15
The categories of this paper are: nucl-ex
0
The categories of this paper are: hep-ph
20
The categories of this paper are: acc-phys
2
The categories of this paper are: math
5
The categories of this paper are: gr-qc
12
The categories of this paper are: nucl-th
3
The categories of this paper are: physics
28
The categories of this paper are: alg-geom
32
The categories of this paper are: atom-ph
34
The categories of this paper are: chem-ph
4
The categories of this paper are: cond-mat
8
The categories of this paper are: hep-ex
35
The categories of this paper are: mtrl-th