Data Reduction Process and Pipeline for the NIC Polarimetry Mode in Python, NICpolpy

TL;DR

This paper presents NICpolpy, a Python software for systematic data reduction of NIC polarimetry data, introducing new Fourier pattern removal and detailed calibration procedures.

Contribution

The work develops a comprehensive Python-based pipeline for NIC polarimetry data reduction, including novel Fourier pattern removal and calibration updates.

Findings

Fourier pattern removal improves image quality.

Gain factor and readout noise are consistent across pixels.

Calibration frames enhance data accuracy.

Abstract

A systematic way of data reduction for the Nishiharima Infrared Camera (NIC) polarimetry mode has been devised and implemented to an open software called NICpolpy in the programming language python (tested on version 3.8--3.10 as of writing). On top of the classical methods, including vertical pattern removal, a new way of diagonal pattern (Fourier pattern) removal has been implemented. Each image undergoes four reduction steps, resulting in "level 1" to "level 4" products, as well as nightly calibration frames. A simple tutorial and in-depth descriptions are provided, as well as the descriptions of algorithms. The dome flat frames (taken on UT 2020-06-03) were analyzed, and the pixel positions vulnerable to flat error were found. Using the dark and flat frames, the detector parameters, gain factor (the conversion factor), and readout noise are also updated. We found gain factor and readout noise are likely constants over pixel or "quadrant".