Stars, Planets, and Python
summary
MIT-led TESS launches April 2018 and images wide sky strips, stores ~200 GB onboard and downlinks sectors that observe 20,000 targets per sector at two‑minute cadence; pipelines use Python (numpy pandas matplotlib, lightkurve) and the MAST archive to build brightness time series, detrend jitter, fold periods and vet candidates. Machine learning scores and rare cases (Kepler deep CNN) find tiny transits near one part in 10,000. The team reports ~1,400 candidates from six sectors with ~400 sub‑Neptune detections. RDKit helps sort prospective biosignature molecules.
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chapters (7)
- 0:00Exoplanets and TESS mission
- 8:23How planets are discovered
- 17:32Finding planets with Python
- 22:48TESS workflow and confirmation
- 28:44Machine learning in detection
- 34:42Visiting exoplanets and space travel
- 40:05Remote sensing for life signs
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