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Random Forests Applied to High-precision Photometry Analysis with Spitzer IRAC

Krick, Jessica E. and Fraine, Jonathan and Ingalls, Jim and Deger, Sinan (2020) Random Forests Applied to High-precision Photometry Analysis with Spitzer IRAC. Astronomical Journal, 160 (3). Art. No. 99. ISSN 1538-3881. https://resolver.caltech.edu/CaltechAUTHORS:20200824-124355763

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Abstract

We present a new method employing machine-learning techniques for measuring astrophysical features by correcting systematics in IRAC high-precision photometry using random forests. The main systematic in IRAC light-curve data is position changes due to unavoidable telescope motions coupled with an intrapixel response function. We aim to use the large amount of publicly available calibration data for the single pixel used for this type of work (the sweet-spot pixel) to make a fast, easy-to-use, accurate correction to science data. This correction on calibration data has the advantage of using an independent data set instead of the science data themselves, which has the disadvantage of including astrophysical variations. After focusing on feature engineering and hyperparameter optimization, we show that a boosted random forest model can reduce the data such that we measure the median of 10 archival eclipse observations of XO-3b to be 1459 ± 200 ppm. This is a comparable depth to the average of those in the literature done by seven different methods; however, the spread in measurements is 30%–100% larger than those literature values, depending on the reduction method. We also caution others attempting similar methods to check their results with the fiducial data set of XO-3b, as we were also able to find models providing initially great scores on their internal test data sets but whose results significantly underestimated the eclipse depth of that planet.


Item Type:Article
Related URLs:
URLURL TypeDescription
https://doi.org/10.3847/1538-3881/aba11fDOIArticle
https://iopscience.iop.org/article/10.3847/1538-3881/aba11f/metaPublisherArticle
https://arxiv.org/abs/2006.14095arXivDiscussion Paper
https://github.com/jkrick/XGBoost_IRACRelated ItemCode
ORCID:
AuthorORCID
Krick, Jessica E.0000-0002-2413-5976
Fraine, Jonathan0000-0003-0910-5805
Ingalls, Jim0000-0003-4714-1364
Deger, Sinan0000-0003-1943-723X
Additional Information:© 2020 The American Astronomical Society. Received 2020 March 27; revised 2020 June 24; accepted 2020 June 28; published 2020 August 4. We thank the anonymous referee for the time and care in providing very useful comments on this manuscript. This work is based (in part) on observations made with the Spitzer Space Telescope, which is operated by the Jet Propulsion Laboratory, California Institute of Technology, under a contract with NASA. This research has made use of NASA's Astrophysics Data System. This research has made use of the NASA/IPAC Infrared Science Archive, which is operated by the Jet Propulsion Laboratory, California Institute of Technology, under contract with the National Aeronautics and Space Administration. The acknowledgments were compiled using the Astronomy Acknowledgment Generator. This research made use of scikit-learn (Pedregosa et al. 2011). Facility: Spitzer (IRAC).
Group:Infrared Processing and Analysis Center (IPAC)
Funders:
Funding AgencyGrant Number
NASA/JPL/CaltechUNSPECIFIED
Subject Keywords:Exoplanets ; Infrared astronomy ; Random Forests ; Astronomy software ; Space telescopes ; Infrared telescopes ; Astronomy data reduction
Issue or Number:3
Classification Code:Unified Astronomy Thesaurus concepts: Exoplanets (498); Infrared astronomy (786); Random Forests (1935); Astronomy software (1855); Space telescopes (1547); Infrared telescopes (794); Astronomy data reduction (1861)
Record Number:10.3847/1538-3881/aba11f
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20200824-124355763
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:105075
Collection:CaltechAUTHORS
Deposited By: Tony Diaz
Deposited On:24 Aug 2020 20:06
Last Modified:08 Sep 2020 17:25

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