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references.bib
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%% A new BibTex file for the
%% PDAC report
%% Created 2017-02-17 by Chris Suberlak
%% Saved with string encoding Unicode (UTF-8)
@ARTICLE{2017arXiv170309824V,
author = {{VanderPlas}, J.~T.},
title = "{Understanding the Lomb-Scargle Periodogram}",
journal = {ArXiv e-prints},
archivePrefix = "arXiv",
eprint = {1703.09824},
primaryClass = "astro-ph.IM",
keywords = {Astrophysics - Instrumentation and Methods for Astrophysics},
year = 2017,
month = mar,
adsurl = {http://adsabs.harvard.edu/abs/2017arXiv170309824V},
adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}
@ARTICLE{2015ApJ...812...18V,
author = {{VanderPlas}, J.~T. and {Ivezi{\'c}}, {\v Z}.},
title = "{Periodograms for Multiband Astronomical Time Series}",
journal = {\apj},
archivePrefix = "arXiv",
eprint = {1502.01344},
primaryClass = "astro-ph.IM",
keywords = {methods: data analysis, methods: statistical, surveys},
year = 2015,
month = oct,
volume = 812,
eid = {18},
pages = {18},
doi = {10.1088/0004-637X/812/1/18},
adsurl = {http://adsabs.harvard.edu/abs/2015ApJ...812...18V},
adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}
@ARTICLE{2009A&A...496..577Z,
author = {{Zechmeister}, M. and {K{\"u}rster}, M.},
title = "{The generalised Lomb-Scargle periodogram. A new formalism for the floating-mean and Keplerian periodograms}",
journal = {\aap},
archivePrefix = "arXiv",
eprint = {0901.2573},
primaryClass = "astro-ph.IM",
keywords = {methods: data analysis, methods: analytical, methods: statistical, techniques: radial velocities},
year = 2009,
month = mar,
volume = 496,
pages = {577-584},
doi = {10.1051/0004-6361:200811296},
adsurl = {http://adsabs.harvard.edu/abs/2009A%26A...496..577Z},
adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}
@ARTICLE{2010ApJ...708..717S,
author = {{Sesar}, B. and {Ivezi{\'c}}, {\v Z}. and {Grammer}, S.~H. and
{Morgan}, D.~P. and {Becker}, A.~C. and {Juri{\'c}}, M. and
{De Lee}, N. and {Annis}, J. and {Beers}, T.~C. and {Fan}, X. and
{Lupton}, R.~H. and {Gunn}, J.~E. and {Knapp}, G.~R. and {Jiang}, L. and
{Jester}, S. and {Johnston}, D.~E. and {Lampeitl}, H.},
title = "{Light Curve Templates and Galactic Distribution of RR Lyrae Stars from Sloan Digital Sky Survey Stripe 82}",
journal = {\apj},
archivePrefix = "arXiv",
eprint = {0910.4611},
keywords = {Galaxy: halo, Galaxy: stellar content, Galaxy: structure, methods: data analysis, stars: horizontal-branch, stars: statistics, stars: variables: other},
year = 2010,
month = jan,
volume = 708,
pages = {717-741},
doi = {10.1088/0004-637X/708/1/717},
adsurl = {http://adsabs.harvard.edu/abs/2010ApJ...708..717S},
adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}
@ARTICLE{2007AJ....134..973I,
author = {{Ivezi{\'c}}, {\v Z}. and {Smith}, J.~A. and {Miknaitis}, G. and
{Lin}, H. and {Tucker}, D. and {Lupton}, R.~H. and {Gunn}, J.~E. and
{Knapp}, G.~R. and {Strauss}, M.~A. and {Sesar}, B. and {Doi}, M. and
{Tanaka}, M. and {Fukugita}, M. and {Holtzman}, J. and {Kent}, S. and
{Yanny}, B. and {Schlegel}, D. and {Finkbeiner}, D. and {Padmanabhan}, N. and
{Rockosi}, C.~M. and {Juri{\'c}}, M. and {Bond}, N. and {Lee}, B. and
{Stoughton}, C. and {Jester}, S. and {Harris}, H. and {Harding}, P. and
{Morrison}, H. and {Brinkmann}, J. and {Schneider}, D.~P. and
{York}, D.},
title = "{Sloan Digital Sky Survey Standard Star Catalog for Stripe 82: The Dawn of Industrial 1\% Optical Photometry}",
journal = {\aj},
eprint = {astro-ph/0703157},
keywords = {catalogs, instrumentation: photometers, methods: data analysis, standards, surveys, techniques: photometric},
year = 2007,
month = sep,
volume = 134,
pages = {973-998},
doi = {10.1086/519976},
adsurl = {http://adsabs.harvard.edu/abs/2007AJ....134..973I},
adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}
@BOOK{2014sdmm.book.....I,
author = {{Ivezi{\'c}}, {\v Z}. and {Connolly}, A.~J. and {VanderPlas}, J.~T. and
{Gray}, A.},
title = "{Statistics, Data Mining, and Machine Learning in Astronomy}",
booktitle = {Statistics, Data Mining, and Machine Learning in Astronomy},
year = 2014,
publisher = {Princeton University Press},
adsurl = {http://adsabs.harvard.edu/abs/2014sdmm.book.....I},
adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}
@INPROCEEDINGS{VanderPlas:6382200,
author={J. VanderPlas and A. J. Connolly and {\v Z}. {Ivezi{\'c}} and A. Gray},
booktitle={2012 Conference on Intelligent Data Understanding},
title={Introduction to astroML: Machine learning for astrophysics},
year=2012,
pages={47-54},
keywords={astronomy computing;data mining;electromagnetic wave propagation;learning (artificial intelligence);object-oriented programming;statistical analysis;astroML;astronomical data collection;astronomy;astrophysics;data volume;electromagnetic spectrum;machine learning;petabyte domain;python;statistical analysis;Astrophysics;Density measurement;Energy measurement;Extraterrestrial measurements;Noise;Object recognition},
doi={10.1109/CIDU.2012.6382200},
month=oct,
}
@INPROCEEDINGS{Coster:2014:ION,
author = {A. Coster and V. Pankratius and F. Lind and P. Erickson and J. Semeter},
title = "{The Mahali Space Weather Project: Advancing GNSS Ionospheric Science}",
year = 2014,
pages = {1213-1221},
month = sep,
booktitle = {Proceedings of the 27th International Technical Meeting of The Satellite Division of the Institute of Navigation (ION GNSS+ 2014)},
url = {https://www.ion.org/publications/abstract.cfm?articleID=12273},
}
@ARTICLE{Pankratius:7515118,
author={V. Pankratius and J. Li and M. Gowanlock and D. M. Blair and C. Rude and T. Herring and F. Lind and P. J. Erickson and C. Lonsdale},
journal={IEEE Intelligent Systems},
title={Computer-Aided Discovery: Toward Scientific Insight Generation with Machine Support},
year=2016,
volume={31},
number={4},
pages={3-10},
keywords={cognitive systems;natural sciences computing;data volumes;feature detection;geoscience;ground-based sensors;human cognitive limits;intelligent systems;ionospheric studies;machine support;model-based computer aided discovery systems;next generation science;observational astronomy;planetary landing site identification;robotic missions;scientific discovery;scientific insight generation;space-based sensors;spacecraft;volcanics;Analytical models;Computational modeling;Computer aided analysis;Data models;Global Positioning System;Planets;Space research;big data;cloud computing;computer-aided discovery;data mining;discovery science;intelligent analytics;intelligent systems;machine learning},
doi={10.1109/MIS.2016.60},
ISSN={1541-1672},
month=jul,
}