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Spitzer/IRAC precision photometry: a machine learning approach

Ingalls, James G. and Krick, Jessica E. and Carey, Sean J. and Lowrance, Patrick J. and Grillmair, Carl J. and Glaccum, William J. and Laine, Seppo and Fraine, Jonathan D. (2018) Spitzer/IRAC precision photometry: a machine learning approach. In: Space Telescopes and Instrumentation 2018: Optical, Infrared, and Millimeter Wave. Proceedings of SPIE. No.10698. Society of Photo-optical Instrumentation Engineers (SPIE) , Bellingham, WA, Art. No. 106985E. ISBN 9781510619494. http://resolver.caltech.edu/CaltechAUTHORS:20181207-145745333

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Abstract

The largest source of noise in exoplanet and brown dwarf photometric time series made with Spitzer/IRAC is the coupling between intra-pixel gain variations and spacecraft pointing fluctuations. Observers typically correct for this systematic in science data by deriving an instrumental noise model simultaneously with the astrophysical light curve and removing the noise model. Such techniques for self-calibrating Spitzer photometric datasets have been extremely successful, and in many cases enabled near-photon-limited precision on exoplanet transit and eclipse depths. Self-calibration, however, can suffer from certain limitations: (1) temporal astrophysical signals can become aliased as part of the instrument model; (2) for some techniques adequate model estimation often requires a high degree of intra-pixel positional redundancy (multiple samples with nearby centroids) over long time spans; (3) many techniques do not account for sporadic high frequency telescope vibrations that smear out the point spread function. We have begun to build independent general-purpose intra-pixel systematics removal algorithms using three machine learning techniques: K-Nearest Neighbors (with kernel regression), Random Decision Forests, and Artificial Neural Networks. These methods remove many of the limitations of self-calibration: (1) they operate on a dedicated calibration database of approximately one million measurements per IRAC waveband (3.6 and 4.5 microns) of non-variable stars, and thus are independent of the time series science data to be corrected; (2) the database covers a large area of the "Sweet Spot, so the methods do not require positional redundancy in the science data; (3) machine learning techniques in general allow for flexibility in training with multiple, sometimes unorthodox, variables, including those that trace PSF smear. We focus in this report on the K-Nearest Neighbors with Kernel Regression technique. (Additional communications are in preparation describing Decision Forests and Neural Networks.)


Item Type:Book Section
Related URLs:
URLURL TypeDescription
https://doi.org/10.1117/12.2313640DOIArticle
ORCID:
AuthorORCID
Carey, Sean J.0000-0002-0221-6871
Lowrance, Patrick J.0000-0001-8014-0270
Grillmair, Carl J.0000-0003-4072-169X
Laine, Seppo0000-0003-1250-8314
Fraine, Jonathan D.0000-0003-0910-5805
Additional Information:© 2018 Society of Photo-Optical Instrumentation Engineers (SPIE).
Group:Infrared Processing and Analysis Center (IPAC)
Subject Keywords:Spitzer, calibration, algorithms, intra-pixel sensitivity, precision photometry, exoplanets, machine learning, k-nearest neighbors
Record Number:CaltechAUTHORS:20181207-145745333
Persistent URL:http://resolver.caltech.edu/CaltechAUTHORS:20181207-145745333
Official Citation:James G. Ingalls, Jessica E. Krick, Sean J. Carey, Patrick J. Lowrance, Carl J. Grillmair, William J. Glaccum, Seppo Laine, Jonathan D. Fraine, "Spitzer/IRAC precision photometry: a machine learning approach," Proc. SPIE 10698, Space Telescopes and Instrumentation 2018: Optical, Infrared, and Millimeter Wave, 106985E (24 July 2018); doi: 10.1117/12.2313640
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:91583
Collection:CaltechAUTHORS
Deposited By: George Porter
Deposited On:07 Dec 2018 23:28
Last Modified:07 Dec 2018 23:28

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