Preliminary analysis on the noise characteristics of MWISP data

TL;DR

This paper analyzes the noise characteristics of MWISP millimeter-wave data cubes, identifying key noise sources, applying cleaning methods, and examining noise distribution and fluctuations in mosaicked data.

Contribution

It provides a detailed statistical analysis of noise sources and distribution in MWISP data, and demonstrates effective cleaning techniques to improve data quality.

Findings

Noise follows a positively skewed normal distribution after cleaning

Major noise factors include bad channels, edge effects, baseline distortion, line contamination

Noise in mosaicked data is mainly due to cell-to-cell fluctuations

Abstract

Noise is a significant part within a millimeter-wave molecular line datacube. Analyzing the noise improves our understanding of noise characteristics, and further contributes to scientific discoveries. We measure the noise level of a single datacube from MWISP and perform statistical analyses. We identified major factors which increase the noise level of a single datacube, including bad channels, edge effects, baseline distortion and line contamination. Cleaning algorithms are applied to remove or reduce these noise components. As a result, we obtained the cleaned datacube in which noise follows a positively skewed normal distribution. We further analyzed the noise structure distribution of a 3D mosaicked datacube in the range l = 40{\deg}.7 to 43{\deg}.3 and b = -2{\deg}.3 to 0{\deg}.3 and found that noise in the final mosaicked datacube is mainly characterized by noise fluctuation among the cells.