Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: New Techniques for High-Contrast Imaging with ADI: the ACORNS-ADI SEEDS
  Data Reduction Pipeline   We describe Algorithms for Calibration, Optimized Registration, and Nulling
the Star in Angular Differential Imaging (ACORNS-ADI), a new, parallelized
software package to reduce high-contrast imaging data, and its application to
data from the SEEDS survey. We implement several new algorithms, including a
method to register saturated images, a trimmed mean for combining an image
sequence that reduces noise by up to ~20%, and a robust and computationally
fast method to compute the sensitivity of a high-contrast observation
everywhere on the field-of-view without introducing artificial sources. We also
include a description of image processing steps to remove electronic artifacts
specific to Hawaii2-RG detectors like the one used for SEEDS, and a detailed
analysis of the Locally Optimized Combination of Images (LOCI) algorithm
commonly used to reduce high-contrast imaging data. ACORNS-ADI is written in
python. It is efficient and open-source, and includes several optional features
which may improve performance on data from other instruments. ACORNS-ADI
requires minimal modification to reduce data from instruments other than
HiCIAO. It is freely available for download at
www.github.com/t-brandt/acorns-adi under a BSD license.

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The categories of this paper are: math-ph
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The categories of this paper are: adap-org
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The categories of this paper are: cond-mat
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The categories of this paper are: q-alg
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The categories of this paper are: alg-geom
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The categories of this paper are: ao-sci
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The categories of this paper are: nlin
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The categories of this paper are: chao-dyn
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The categories of this paper are: q-fin
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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: atom-ph
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The categories of this paper are: plasm-ph
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The categories of this paper are: cmp-lg
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The categories of this paper are: econ
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The categories of this paper are: dg-ga