Python programming code for stellar photometry in astrophysics teaching on a cloud computing service
Issued Date
2023-01-01
Resource Type
ISSN
17426588
eISSN
17426596
Scopus ID
2-s2.0-85148043920
Journal Title
Journal of Physics: Conference Series
Volume
2431
Issue
1
Rights Holder(s)
SCOPUS
Bibliographic Citation
Journal of Physics: Conference Series Vol.2431 No.1 (2023)
Suggested Citation
Krittinatham W., Kaewkhong K., Emarat N. Python programming code for stellar photometry in astrophysics teaching on a cloud computing service. Journal of Physics: Conference Series Vol.2431 No.1 (2023). doi:10.1088/1742-6596/2431/1/012038 Retrieved from: https://repository.li.mahidol.ac.th/handle/20.500.14594/82247
Title
Python programming code for stellar photometry in astrophysics teaching on a cloud computing service
Author(s)
Author's Affiliation
Other Contributor(s)
Abstract
Nowadays, there is various software used for both education and astronomy research. For photometry, licensed software and high-performance computer operating systems are required, which is a fund limitation for some schools in Thailand. Thus, in this article, we develop and present the Demonstration Photometry Scripts for Astrophysics Teaching (DPSAT version 1.0). The program is designed to work on cloud computing services via internet browsers to avoid hardware and operation requirement pain points. The DPSAT is programming on flexible, low-cost, on-trend language, Python, and Jupyter Notebook online editor. In advance, our new code supports the home-use image or video file format, i.e., jpg, png, or mp4. Thus, it will be more accessible for teachers and students who do not have standard astronomical instruments. The DPSAT measures the stellar light intensity from the time-series still-images or video files from a smartphone or other digital devices. The code can extract video files into sequenced still images, then transform the RGB color space images into greyscale. The light intensity signal of selected pixels is counted with a simple aperture method in time series. It shows the results, for example, the mean signal, standard variation, measured signal as light intensity versus time, and image of light sources. This will be fruitful for low-cost and easily accessible teaching of astrophysics subjects.