Science AMA Series: We’re NASA, MIT and Kepler scientists excited about the launch of our newest planet hunter, TESS. AMA!
Bibliographic record
Abstract
We’re finding planets around other stars! So far we have discovered thousands of these exoplanets with missions like Kepler and K2. Today we’re at Kennedy Space Center eagerly awaiting the launch of NASA’s newest planet hunter. The Transiting Exoplanet Survey Satellite, or TESS mission, will search nearly the entire sky looking for tiny dips in the light from Earth’s closest neighborhood stars that may indicate planets passing in front of the stars. TESS will make a catalog of thousands of worlds for us to study in more detail with future missions like the James Webb Space Telescope. TESS will fly in an orbit that completes two circuits around the Earth for every orbit of the Moon. This special orbit will allow TESS’s cameras to monitor each patch of sky for nearly a month at a time. We are: Natalia Guerrero: I’m a researcher in the TESS Science Office at the MIT Kavli Institute for Astrophysics and Space Research. I measured the TESS camera performance and will lead the team identifying exoplanets and other interesting astrophysical phenomena in the TESS data for further observation by other telescopes. Elisa V. Quintana: I’m an astrophysicist at NASA’s Goddard Space Flight Center in Greenbelt, Md., where I work on the TESS and WFIRST missions. I study exoplanets in extreme environments and am looking forward to finding new ones with TESS. Stephen Rinehart: I’m the project scientist for the TESS mission. I help make sure that the mission will be able to do the great science that was proposed, and I’m excited about all the great science that astronomers will be able to do with data from TESS! And, I enjoy giving snarky answers to questions on reddit. Diana Dragomir: I’m an astronomer at MIT. I study planets around other stars (exoplanets), especially those smaller than Neptune. My research uses data from many telescopes, including the Hubble Space Telescope, Spitzer, the Canadian MOST space telescope and the Las Cumbres Observatory network. Sam Quinn: I’m an astronomer at the Harvard-Smithsonian Center for Astrophysics. I hunt for exoplanets and use their observed properties to study how they form, evolve, and migrate (yes, migrate!). My role in the TESS Science Office is to help organize follow-up observations of TESS planets with ground-based telescopes to measure their masses and characterize their host stars. Learn more about TESS at www.nasa.gov/tess Follow us on @NASA_TESS to stay updated We are now live! Thank you all for your questions. We’ve had a great time answering them, however we’re going to log out now.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.388 | 0.338 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".