2025 Project Description

Automatic color adjustment for astronomical image

Supervisors

Kuan-Chou Hou, Kuo-Song Wang, Yu-Hsuan Hwang, Zhen-Kai Gao, and Chin-Fei Lee
Find out more about supervisors on ASIAA website

Task Description and Goals

Astronomical images typically contain a small fraction of sources, while the majority of the image consists of blank sky (noise) with intensity values spanning multiple orders of magnitude. In some cases, the targets may be only slightly brighter than the noise and significantly fainter than other objects. To effectively study these sources, it is essential to properly scale and truncate pixel values to enhance visibility.

In this project, the participant will explore methods for automatic intensity scaling and truncation, including machine learning models and algorithms. These solutions must be lightweight and efficient to handle large astronomical images and data cubes while supporting data across a broad range of wavelengths. The outcome will be a new feature for CARTA, a modern astronomical data visualization software, aiding research in astronomy and astrophysics.

Participants will gain an understanding of the structure of astronomical images and data cubes while learning techniques for visualizing data across various wavelengths.

Students with an interest in astronomy or software engineering are welcome to join!

Required Background

Basic Python programming knowledge is required.