High-Resolution Imaging for Statistical Validation of TESS Planet Candidates
The short version
- I have no experience in astronomy, yet facilitated one of the largest studies of exoplanet discovery and validation ever conducted using data from NASA’s TESS satellite.
- We statistically validated 64 new exoplanets, making it through a rigorous peer review process.
- Last week the paper was formally accepted by The Astronomical Journal.
- Using AI, I created an automated data processing pipeline to process hundreds of gigabytes of data, integrating many disparate systems that typically would require manual work in…
- Ultimately I was able to make enough progress to get the attention of astrophysicists, who helped guide me through getting the results peer-review ready.
The story
First author here. I have no experience in astronomy, yet facilitated one of the largest studies of exoplanet discovery and validation ever conducted using data from NASA’s TESS satellite. We statistically validated 64 new exoplanets, making it through a rigorous peer review process.
Last week the paper was formally accepted by The Astronomical Journal. Using AI, I created an automated data processing pipeline to process hundreds of gigabytes of data, integrating many disparate systems that typically would require manual work in Jupyter notebooks. Ultimately I was able to make enough progress to get the attention of astrophysicists, who helped guide me through getting the results peer-review ready. Happy to share more if there are questions!
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