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@misc{chollet2015keras,
title={Keras},
author={Chollet, Fran\c{c}ois and others},
year={2015},
howpublished={\url{https://keras.io}},
}
@article{Heinze_2018,
doi = {10.3847/1538-3881/aae47f},
url = {https://doi.org/10.3847%2F1538-3881%2Faae47f},
year = 2018,
month = {nov},
publisher = {American Astronomical Society},
volume = {156},
number = {5},
pages = {241},
author = {A. N. Heinze and J. L. Tonry and L. Denneau and H. Flewelling and B. Stalder and A. Rest and K. W. Smith and S. J. Smartt and H. Weiland},
title = {A First Catalog of Variable Stars Measured by the Asteroid Terrestrial-impact Last Alert System ({ATLAS})},
journal = {The Astronomical Journal},
abstract = {The Asteroid Terrestrial-impact Last Alert System (ATLAS) carries out its primary planetary defense mission by surveying about 13,000 deg2 at least four times per night. The resulting data set is useful for the discovery of variable stars to a magnitude limit fainter than r ∼ 18, with amplitudes down to 0.02 mag for bright objects. Here, we present a Data Release One catalog of variable stars based on analyzing the light curves of 142 million stars that were measured at least 100 times in the first two years of ATLAS operations. Using a Lomb–Scargle periodogram and other variability metrics, we identify 4.7 million candidate variables. Through the Space Telescope Science Institute, we publicly release light curves for all of them, together with a vector of 169 classification features for each star. We do this at the level of unconfirmed candidate variables in order to provide the community with a large set of homogeneously analyzed photometry and to avoid pre-judging which types of objects others may find most interesting. We use machine learning to classify the candidates into 15 different broad categories based on light-curve morphology. About 10% (427,000 stars) pass extensive tests designed to screen out spurious variability detections: we label these as “probable” variables. Of these, 214,000 receive specific classifications as eclipsing binaries, pulsating, Mira-type, or sinusoidal variables: these are the “classified” variables. New discoveries among the probable variables number 315,000, while 141,000 of the classified variables are new, including about 10,400 pulsating variables, 2060 Mira stars, and 74,700 eclipsing binaries.}
}
@article{1810.09489,
Author = {A. Udalski and I. Soszyński and P. Pietrukowicz and M. K. Szymański and D. M. Skowron and J. Skowron and P. Mróz and R. Poleski and S. Kozłowski and K. Ulaczyk and K. Rybicki and P. Iwanek and M. Wrona},
Title = {OGLE Collection of Galactic Cepheids},
Year = {2018},
Eprint = {arXiv:1810.09489},
Howpublished = {2018, Acta Astron., 68, 315},
Doi = {10.32023/0001-5237/68.4.1},
}
@article{Gopalan_2015,
doi = {10.1088/0004-637x/809/1/40},
url = {https://doi.org/10.1088%2F0004-637x%2F809%2F1%2F40},
year = 2015,
month = {aug},
publisher = {{IOP} Publishing},
volume = {809},
number = {1},
pages = {40},
author = {Giri Gopalan and Saeqa Dil Vrtilek and Luke Bornn},
title = {{CLASSIFYING} X-{RAY} {BINARIES}: A {PROBABILISTIC} {APPROACH}},
journal = {The Astrophysical Journal},
abstract = {In X-ray binary star systems consisting of a compact object that accretes material from an orbiting secondary star, there is no straightforward means to decide whether the compact object is a black hole or a neutron star. To assist in this process, we develop a Bayesian statistical model that makes use of the fact that X-ray binary systems appear to cluster based on their compact object type when viewed from a three-dimensional coordinate system derived from X-ray spectral data where the first coordinate is the ratio of counts in the mid- to low-energy band (color 1), the second coordinate is the ratio of counts in the high- to low-energy band (color 2), and the third coordinate is the sum of counts in all three bands. We use this model to estimate the probabilities of an X-ray binary system containing a black hole, non-pulsing neutron star, or pulsing neutron star. In particular, we utilize a latent variable model in which the latent variables follow a Gaussian process prior distribution, and hence we are able to induce the spatial correlation which we believe exists between systems of the same type. The utility of this approach is demonstrated by the accurate prediction of system types using Rossi X-ray Timing Explorer All Sky Monitor data, but it is not flawless. In particular, non-pulsing neutron systems containing “bursters” that are close to the boundary demarcating systems containing black holes tend to be classified as black hole systems. As a byproduct of our analyses, we provide the astronomer with the public R code which can be used to predict the compact object type of XRBs given training data.}
}
@article{10.1093/mnras/stu1188,
author = {Morello, V. and Barr, E. D. and Bailes, M. and Flynn, C. M. and Keane, E. F. and van Straten, W.},
title = "{SPINN: a straightforward machine learning solution to the pulsar candidate selection problem}",
journal = {Monthly Notices of the Royal Astronomical Society},
volume = {443},
number = {2},
pages = {1651-1662},
year = {2014},
month = {07},
abstract = "{We describe SPINN (Straightforward Pulsar Identification using Neural Networks), a high-performance machine learning solution developed to process increasingly large data outputs from pulsar surveys. SPINN has been cross-validated on candidates from the southern High Time Resolution Universe (HTRU) survey and shown to identify every known pulsar found in the survey data while maintaining a false positive rate of 0.64 per cent. Furthermore, it ranks 99 per cent of pulsars among the top 0.11 per cent of candidates, and 95 per cent among the top 0.01 per cent. In conjunction with the peasoup pipeline, it has already discovered four new pulsars in a re-processing of the intermediate Galactic latitude area of HTRU, three of which have spin periods shorter than 5 ms. SPINN's ability to reduce the amount of candidates to visually inspect by up to four orders of magnitude makes it a very promising tool for future large-scale pulsar surveys. In an effort to provide a common testing ground for pulsar candidate selection tools and stimulate interest in their development, we also make publicly available the set of candidates on which SPINN was cross-validated.}",
issn = {0035-8711},
doi = {10.1093/mnras/stu1188},
url = {https://doi.org/10.1093/mnras/stu1188},
eprint = {http://oup.prod.sis.lan/mnras/article-pdf/443/2/1651/3623597/stu1188.pdf},
}
@article{Barnes_2016,
doi = {10.3847/0004-637x/829/2/89},
url = {https://doi.org/10.3847%2F0004-637x%2F829%2F2%2F89},
year = 2016,
month = {sep},
publisher = {American Astronomical Society},
volume = {829},
number = {2},
pages = {89},
author = {G. Barnes and K. D. Leka and C. J. Schrijver and T. Colak and R. Qahwaji and O. W. Ashamari and Y. Yuan and J. Zhang and R. T. J. McAteer and D. S. Bloomfield and P. A. Higgins and P. T. Gallagher and D. A. Falconer and M. K. Georgoulis and M. S. Wheatland and C. Balch and T. Dunn and E. L. Wagner},
title = {A {COMPARISON} {OF} {FLARE} {FORECASTING} {METHODS}. I. {RESULTS} {FROM} {THE} {\textquotedblleft}{ALL}-{CLEAR}{\textquotedblright} {WORKSHOP}},
journal = {The Astrophysical Journal},
abstract = {Solar flares produce radiation that can have an almost immediate effect on the near-Earth environment, making it crucial to forecast flares in order to mitigate their negative effects. The number of published approaches to flare forecasting using photospheric magnetic field observations has proliferated, with varying claims about how well each works. Because of the different analysis techniques and data sets used, it is essentially impossible to compare the results from the literature. This problem is exacerbated by the low event rates of large solar flares. The challenges of forecasting rare events have long been recognized in the meteorology community, but have yet to be fully acknowledged by the space weather community. During the interagency workshop on “all clear” forecasts held in Boulder, CO in 2009, the performance of a number of existing algorithms was compared on common data sets, specifically line-of-sight magnetic field and continuum intensity images from the Michelson Doppler Imager, with consistent definitions of what constitutes an event. We demonstrate the importance of making such systematic comparisons, and of using standard verification statistics to determine what constitutes a good prediction scheme. When a comparison was made in this fashion, no one method clearly outperformed all others, which may in part be due to the strong correlations among the parameters used by different methods to characterize an active region. For M-class flares and above, the set of methods tends toward a weakly positive skill score (as measured with several distinct metrics), with no participating method proving substantially better than climatological forecasts.}
}
@article{astropy:2018,
Adsnote = {Provided by the SAO/NASA Astrophysics Data System},
Adsurl = {https://ui.adsabs.harvard.edu/#abs/2018AJ....156..123T},
Author = {{Price-Whelan}, A.~M. and {Sip{\H{o}}cz}, B.~M. and {G{\"u}nther}, H.~M. and {Lim}, P.~L. and {Crawford}, S.~M. and {Conseil}, S. and {Shupe}, D.~L. and {Craig}, M.~W. and {Dencheva}, N. and {Ginsburg}, A. and {VanderPlas}, J.~T. and {Bradley}, L.~D. and {P{\'e}rez-Su{\'a}rez}, D. and {de Val-Borro}, M. and {Paper Contributors}, (Primary and {Aldcroft}, T.~L. and {Cruz}, K.~L. and {Robitaille}, T.~P. and {Tollerud}, E.~J. and {Coordination Committee}, (Astropy and {Ardelean}, C. and {Babej}, T. and {Bach}, Y.~P. and {Bachetti}, M. and {Bakanov}, A.~V. and {Bamford}, S.~P. and {Barentsen}, G. and {Barmby}, P. and {Baumbach}, A. and {Berry}, K.~L. and {Biscani}, F. and {Boquien}, M. and {Bostroem}, K.~A. and {Bouma}, L.~G. and {Brammer}, G.~B. and {Bray}, E.~M. and {Breytenbach}, H. and {Buddelmeijer}, H. and {Burke}, D.~J. and {Calderone}, G. and {Cano Rodr{\'\i}guez}, J.~L. and {Cara}, M. and {Cardoso}, J.~V.~M. and {Cheedella}, S. and {Copin}, Y. and {Corrales}, L. and {Crichton}, D. and {D{\textquoteright}Avella}, D. and {Deil}, C. and {Depagne}, {\'E}. and {Dietrich}, J.~P. and {Donath}, A. and {Droettboom}, M. and {Earl}, N. and {Erben}, T. and {Fabbro}, S. and {Ferreira}, L.~A. and {Finethy}, T. and {Fox}, R.~T. and {Garrison}, L.~H. and {Gibbons}, S.~L.~J. and {Goldstein}, D.~A. and {Gommers}, R. and {Greco}, J.~P. and {Greenfield}, P. and {Groener}, A.~M. and {Grollier}, F. and {Hagen}, A. and {Hirst}, P. and {Homeier}, D. and {Horton}, A.~J. and {Hosseinzadeh}, G. and {Hu}, L. and {Hunkeler}, J.~S. and {Ivezi{\'c}}, {\v{Z}}. and {Jain}, A. and {Jenness}, T. and {Kanarek}, G. and {Kendrew}, S. and {Kern}, N.~S. and {Kerzendorf}, W.~E. and {Khvalko}, A. and {King}, J. and {Kirkby}, D. and {Kulkarni}, A.~M. and {Kumar}, A. and {Lee}, A. and {Lenz}, D. and {Littlefair}, S.~P. and {Ma}, Z. and {Macleod}, D.~M. and {Mastropietro}, M. and {McCully}, C. and {Montagnac}, S. and {Morris}, B.~M. and {Mueller}, M. and {Mumford}, S.~J. and {Muna}, D. and {Murphy}, N.~A. and {Nelson}, S. and {Nguyen}, G.~H. and {Ninan}, J.~P. and {N{\"o}the}, M. and {Ogaz}, S. and {Oh}, S. and {Parejko}, J.~K. and {Parley}, N. and {Pascual}, S. and {Patil}, R. and {Patil}, A.~A. and {Plunkett}, A.~L. and {Prochaska}, J.~X. and {Rastogi}, T. and {Reddy Janga}, V. and {Sabater}, J. and {Sakurikar}, P. and {Seifert}, M. and {Sherbert}, L.~E. and {Sherwood-Taylor}, H. and {Shih}, A.~Y. and {Sick}, J. and {Silbiger}, M.~T. and {Singanamalla}, S. and {Singer}, L.~P. and {Sladen}, P.~H. and {Sooley}, K.~A. and {Sornarajah}, S. and {Streicher}, O. and {Teuben}, P. and {Thomas}, S.~W. and {Tremblay}, G.~R. and {Turner}, J.~E.~H. and {Terr{\'o}n}, V. and {van Kerkwijk}, M.~H. and {de la Vega}, A. and {Watkins}, L.~L. and {Weaver}, B.~A. and {Whitmore}, J.~B. and {Woillez}, J. and {Zabalza}, V. and {Contributors}, (Astropy},
Doi = {10.3847/1538-3881/aabc4f},
Eid = {123},
Journal = {\aj},
Keywords = {methods: data analysis, methods: miscellaneous, methods: statistical, reference systems, Astrophysics - Instrumentation and Methods for Astrophysics},
Month = Sep,
Pages = {123},
Primaryclass = {astro-ph.IM},
Title = {{The Astropy Project: Building an Open-science Project and Status of the v2.0 Core Package}},
Volume = {156},
Year = 2018,
Bdsk-Url-1 = {https://doi.org/10.3847/1538-3881/aabc4f}}
@article{scikit-learn,
title={Scikit-learn: Machine Learning in {P}ython},
author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.
and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.
and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and
Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},
journal={Journal of Machine Learning Research},
volume={12},
pages={2825--2830},
year={2011}
}
@article{scikit-image,
title = {scikit-image: image processing in {P}ython},
author = {van der Walt, {S}t\'efan and {S}ch\"onberger, {J}ohannes {L}. and
{Nunez-Iglesias}, {J}uan and {B}oulogne, {F}ran\c{c}ois and {W}arner,
{J}oshua {D}. and {Y}ager, {N}eil and {G}ouillart, {E}mmanuelle and
{Y}u, {T}ony and the scikit-image contributors},
year = {2014},
month = {6},
keywords = {Image processing, Reproducible research, Education,
Visualization, Open source, Python, Scientific programming},
volume = {2},
pages = {e453},
journal = {PeerJ},
issn = {2167-8359},
url = {https://doi.org/10.7717/peerj.453},
doi = {10.7717/peerj.453}
}
@Misc{scipy,
author = {Eric Jones and Travis Oliphant and Pearu Peterson and others},
title = {{SciPy}: Open source scientific tools for {Python}},
year = {2001--},
url = "http://www.scipy.org/",
note = {[Online; accessed <today>]}
}
@book{oliphant2006guide,
title={A guide to NumPy},
author={Oliphant, Travis E},
volume={1},
year={2006},
publisher={Trelgol Publishing USA}
}
@Article{Hunter:2007,
Author = {Hunter, J. D.},
Title = {Matplotlib: A 2D graphics environment},
Journal = {Computing in Science \& Engineering},
Volume = {9},
Number = {3},
Pages = {90--95},
abstract = {Matplotlib is a 2D graphics package used for Python for
application development, interactive scripting, and publication-quality
image generation across user interfaces and operating systems.},
publisher = {IEEE COMPUTER SOC},
doi = {10.1109/MCSE.2007.55},
year = 2007
}