Post-processing CHARIS integral field spectrograph data with PyKLIP

TL;DR

This paper introduces pyKLIP-CHARIS, a Python-based post-processing pipeline for the CHARIS integral field spectrograph, enhancing data reduction and spectral extraction for high contrast imaging of exoplanets and related objects.

Contribution

The paper presents a new Python pipeline tailored for CHARIS data, integrating pyKLIP algorithms with unique calibration and registration for improved spectral extraction.

Findings

Effective extraction of synthetic source spectra demonstrated

Comparison shows comparable or improved results over existing pipelines

Pipeline supports angular and spectral differential imaging techniques

Abstract

We present the pyKLIP-CHARIS post-processing pipeline, a Python library that reduces high contrast imaging data for the CHARIS integral field spectrograph used with the SCExAO project on the Subaru Telescope. The pipeline is a part of the pyKLIP package, a Python library dedicated to the reduction of direct imaging data of exoplanets, brown dwarfs, and discs. For PSF subtraction, the pyKLIP-CHARIS post-processing pipeline relies on the core algorithms implemented in pyKLIP but uses image registration and calibrations that are unique to CHARIS. We describe the pipeline procedures, calibration results, and capabilities in processing imaging data acquired via the angular differential imaging and spectral differential imaging observing techniques. We showcase its performance on extracting spectra of injected synthetic point sources as well as compare the extracted spectra from real data sets on HD 33632 and HR 8799 to results in the literature. The pipeline is a python-based complement to the SCExAO project supported, widely used (and currently IDL-based) CHARIS data post-processing pipeline (CHARIS DPP) and provides an additional approach to reducing CHARIS data and extracting calibrated planet spectra.