Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: RUN DMC: An efficient, parallel code for analyzing Radial Velocity
  Observations using N-body Integrations and Differential Evolution Markov
  chain Monte Carlo   In the 20+ years of Doppler observations of stars, scientists have uncovered
a diverse population of extrasolar multi-planet systems. A common technique for
characterizing the orbital elements of these planets is Markov chain Monte
Carlo (MCMC), using a Keplerian model with random walk proposals and paired
with the Metropolis-Hastings algorithm. For approximately a couple of dozen
planetary systems with Doppler observations, there are strong planet-planet
interactions due to the system being in or near a mean-motion resonance (MMR).
An N-body model is often required to accurately describe these systems. Further
computational difficulties arise from exploring a high-dimensional parameter
space ($\sim$7 x number of planets) that can have complex parameter
correlations. To surmount these challenges, we introduce a differential
evolution MCMC (DEMCMC) applied to radial velocity data while incorporating
self-consistent N-body integrations. Our Radial velocity Using N-body DEMCMC
(RUN DMC) algorithm improves upon the random walk proposal distribution of the
traditional MCMC by using an ensemble of Markov chains to adaptively improve
the proposal distribution. We describe the methodology behind the algorithm,
along with results of tests for accuracy and performance. We find that most
algorithm parameters have a modest effect on the rate of convergence. However,
the size of the ensemble can have a strong effect on performance. We show that
the optimal choice depends on the number of planets in a system, as well as the
computer architecture used and the resulting extent of parallelization. While
the exact choices of optimal algorithm parameters will inevitably vary due to
the details of individual planetary systems, we offer recommendations for
choosing the DEMCMC algorithm's algorithmic parameters that result in excellent
performance for a wide variety of planetary systems.

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The categories of this paper are: cmp-lg
19
The categories of this paper are: eess
5
The categories of this paper are: gr-qc
26
The categories of this paper are: bayes-an
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The categories of this paper are: hep-lat
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The categories of this paper are: q-alg
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The categories of this paper are: solv-int
3
The categories of this paper are: physics
20
The categories of this paper are: acc-phys
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The categories of this paper are: comp-gas
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The categories of this paper are: ao-sci
7
The categories of this paper are: hep-th
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The categories of this paper are: econ
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The categories of this paper are: q-fin
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The categories of this paper are: stat
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The categories of this paper are: mtrl-th