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SPCA: An Open-Source, Modular, and Automated Pipeline for Spitzer Phase Curve Analyses

Published onJun 01, 2020
SPCA: An Open-Source, Modular, and Automated Pipeline for Spitzer Phase Curve Analyses

We present the easy-to-use, automatable, and customizable Spitzer Phase Curve Analysis (SPCA) open-source pipeline. To demonstrate the capabilities of SPCA, we present the analysis of two new Spitzer phasecurves (ultra-hot Jupiters KELT-16b and MASCARA-1b) and a uniform re-analysis of previously published Spitzer phasecurves. The thermal phasecurve observations collected by Spitzer have been one of its greatest scientific legacies. Spitzer demonstrated that we can detect the variations in disk-integrated flux from an exoplanet as a function of orbital phase, allowing us to probe atmospheric dynamics and heat transport. The success of phasecurve observations from Spitzer and Hubble has ushered in the era of comparative atmospheric dynamics, and JWST and ARIEL will carry on the legacy in the 2020s and beyond. However, reaching the level of precision required to make phasecurve observations with Spitzer has been challenging, as strong intra-pixel sensitivity variations can be an order of magnitude larger than the astrophysical phase variations. Many parametric and non-parametric methods have been developed to model out these detector systematics, each with strengths and weaknesses, and most research groups have their own preferred method and code. Some of these codes are open source, but those who want to compare different decorrelation techniques are stuck learning (or building) new packages. SPCA seeks to reduce the cost of entry for all while providing flexibility and effectiveness. SPCA has implementations of 2D polynomial, Pixel Level Decorrelation, BiLinearly-Interpolated Sub-pixel Sensitivity mapping, and Gaussian Process decorrelation methods for Spitzer/IRAC observations of exoplanets, allowing the user to change between techniques by setting a single variable. The modular structure of the code also allows the user to integrate custom astrophysical models and decorrelation methods. Built with automation in mind, SPCA can reduce and decorrelate multiple datasets with a single command. We recommend that these principles of open-source and modular code be applied in the coming JWST era, reducing redundant labour and increasing reproducibility.

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