On the movements of the Exoplanet
This talk shares my journey exploring exoplanet detection after joining the NASA Space Apps Challenge, where I learned how astronomical data can be analyzed with Python alongside researchers from BRIN.
We will explore transit photometry, a method used to detect exoplanets by observing small dips in a star’s brightness when a planet passes in front of it. Participants will work with real datasets from the NASA Exoplanet Archive and MAST, using Python tools such as Lightkurve and simple AI approaches to distinguish real signals from noise.
The session aims to make space-data analysis more approachable for developers and show how Python can be used to explore and detect potential exoplanet signals.
Tell us about your own experience with this topic
I began exploring exoplanet detection after participating in the NASA Space Apps Challenge, where I worked with astronomical datasets and learned from researchers at BRIN. Since then, I’ve experimented individually with Python tools such as Lightkurve and basic ML techniques to analyze transit photometry data from NASA archives.
What discussions can you have with attendees through this talk?
I’d like to discuss how developers from non-astronomy backgrounds can contribute to space science using accessible Python tools and open datasets. I’m also interested in hearing how others approach noisy real-world data, apply simple AI methods for scientific exploration, and build interdisciplinary projects that connect software engineering, data science, and astronomy.
Muhammad Khoirul Ihsan (Ihsan) is a Computer Science student at the State University of Semarang. He works as an AI Engineer at Rubythalib.ai and is involved in AI research activities with MBZUAI.
His work focuses on applied AI, IoT, and interdisciplinary technology projects, particularly those that combine software engineering with real-world experimentation. He previously presented an unconventional Python-based project at PyCon APAC 2025 in the Philippines and continues to explore creative ways of using Python for research and engineering challenges.