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Application of the Trend Filtering Algorithm for Photometric Time Series Data

Gopalan, Giri and Plavchan, Peter and van Eyken, Julian and Ciardi, David and von Braun, Kaspar and Kane, Stephen R. (2016) Application of the Trend Filtering Algorithm for Photometric Time Series Data. Publications of the Astronomical Society of the Pacific, 128 (966). Art. No. 084504. ISSN 0004-6280. http://resolver.caltech.edu/CaltechAUTHORS:20160729-153020557

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Abstract

Detecting transient light curves (e.g., transiting planets) requires high-precision data, and thus it is important to effectively filter systematic trends affecting ground-based wide-field surveys. We apply an implementation of the Trend Filtering Algorithm (TFA) to the 2MASS calibration catalog and select Palomar Transient Factory (PTF) photometric time series data. TFA is successful at reducing the overall dispersion of light curves, however, it may over-filter intrinsic variables and increase "instantaneous" dispersion when a template set is not judiciously chosen. In an attempt to rectify these issues we modify the original TFA from the literature by including measurement uncertainties in its computation, including ancillary data correlated with noise, and algorithmically selecting a template set using clustering algorithms as suggested by various authors. This approach may be particularly useful for appropriately accounting for variable photometric precision surveys and/or combined data sets. In summary, our contributions are to provide a MATLAB software implementation of TFA and a number of modifications tested on synthetics and real data, summarize the performance of TFA and various modifications on real ground-based data sets (2MASS and PTF), and assess the efficacy of TFA and modifications using synthetic light curve tests consisting of transiting and sinusoidal variables. While the transiting variables test indicates that these modifications confer no advantage to transit detection, the sinusoidal variables test indicates potential improvements in detection accuracy.


Item Type:Article
Related URLs:
URLURL TypeDescription
http://dx.doi.org/10.1088/1538-3873/128/966/084504DOIArticle
http://iopscience.iop.org/article/10.1088/1538-3873/128/966/084504/metaPublisherArticle
http://arxiv.org/abs/1603.06001arXivDiscussion Paper
ORCID:
AuthorORCID
Plavchan, Peter0000-0002-8864-1667
Ciardi, David0000-0002-5741-3047
Kane, Stephen R.0000-0002-7084-0529
Additional Information:© 2016 The Astronomical Society of the Pacific. Received 2015 October 3; accepted 2016 March 18; published 2016 June 23. G.G. would like to acknowledge the gracious support of the Caltech Summer Undergraduate Research Fellowship program for supporting this work during the summer of 2009.
Group:IPTF, Infrared Processing and Analysis Center (IPAC)
Funders:
Funding AgencyGrant Number
Caltech Summer Undergraduate Research Fellowship (SURF)UNSPECIFIED
Subject Keywords:methods: data analysis – methods: statistical
Record Number:CaltechAUTHORS:20160729-153020557
Persistent URL:http://resolver.caltech.edu/CaltechAUTHORS:20160729-153020557
Official Citation:Giri Gopalan et al 2016 PASP 128 084504
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:69334
Collection:CaltechAUTHORS
Deposited By: Tony Diaz
Deposited On:29 Jul 2016 22:56
Last Modified:08 Nov 2017 22:04

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