Project Status Note
In development. Not yet deployed in production research environments.
The Challenge
Raw spectroscopic data is extremely noisy, often dominated by the host star's light. Identifying the faint dip caused by an atmosphere requires filtering terabytes of data with extreme precision.
The Solution
We created a cloud-native processing pipeline concept that can scale to thousands of cores. The frontend uses WebGL to render volumetric models of the atmospheres based on the chemical signatures detected.
The Outcome
Pipeline architecture and visualization tools are in active development. Seeking research partnerships for validation with real telescope data.