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@kevinkhu | |||||
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NEW PAPER ALERT!
"Scaling K2. I. Revised Parameters for 222,088 K2 Stars and a K2 Planet Radius Valley at 1.9 R⊕"
arxiv.org/abs/2001.11511 (soon in ApJS!)
This is the first paper in the "Scaling K2" series, with the ultimate goal of computing planet occurrence rates. But... pic.twitter.com/1AY4I46jce
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Kevin Hardegree-Ullman
@kevinkhu
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3. velj |
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Before we can do anything with the planets, we must know something about the stars that host them: KNOW THY STAR, KNOW THY PLANET!
It is important to measure things like stellar radii, masses, temperatures, etc. in a homogeneous manner for occurrence rates. pic.twitter.com/L2gLOXQ0yY
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Kevin Hardegree-Ullman
@kevinkhu
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3. velj |
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K2 (revitalized Kepler spacecraft) stared at a bunch of different fields along the ecliptic plane for ~80 days each in search of planets transiting their host star.
To measure the transiting planet radius, we first need to know the host star radius. pic.twitter.com/qMxpcyPQSx
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Kevin Hardegree-Ullman
@kevinkhu
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3. velj |
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The Ecliptic Plane Input Catalog (EPIC; Huber+ 2016) characterized about ~300,000 stars for K2 mostly using proper motions and colors.
Then, something completely expected happened. Gaia DR2 came and changed everything... pic.twitter.com/nLWgCzWtji
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Kevin Hardegree-Ullman
@kevinkhu
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3. velj |
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Gaia measured distances to 1.3 billion stars! But Gaia is not alone in the era of Big Astronomical Data™. Pan-STARRS DR2 is 1.6 petabytes of photometry, and LAMOST DR5 has over 9 million spectra! pic.twitter.com/HWloalVE4w
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Kevin Hardegree-Ullman
@kevinkhu
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3. velj |
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We updated some of the photometry in the EPIC using Pan-STARRS, used the gaia-kepler.fun database to cross-match Gaia and K2 targets, and cross-matched the K2 targets with LAMOST to find ~27,000 K2 targets with a spectrum. pic.twitter.com/ofBlTwRTGu
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Kevin Hardegree-Ullman
@kevinkhu
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3. velj |
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The LAMOST pipeline provides spectral types, temperatures, surface gravities, and metallicities for A, F, G, and K stars, but not M dwarfs. Luckily, I know a thing or two about M dwarfs (arxiv.org/abs/1905.05900). pic.twitter.com/jLh0OIcqQD
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Kevin Hardegree-Ullman
@kevinkhu
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3. velj |
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Using the spectroscopic target photometry and parameters as a training set, we used machine learning to derive spectral types, temperatures, surface gravities, and metallicities for targets without a spectrum. pic.twitter.com/WTAc1dmvVG
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Kevin Hardegree-Ullman
@kevinkhu
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3. velj |
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We used the Stefan-Boltzmann law to compute radii for AFGK stars, and absolute magnitude-radius relations from Mann+ 2015 for M dwarfs. In total we have new stellar parameters for 222,088 stars! pic.twitter.com/84nlRX5ZyI
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Kevin Hardegree-Ullman
@kevinkhu
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3. velj |
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Using our new stellar parameters, we re-derived radii for 299 confirmed and 517 candidate planets and something magical happened: WE FOUND A DEFINITIVE PLANET RADIUS GAP FOR THE FIRST TIME IN A DATA SET OTHER THAN KEPLER! pic.twitter.com/IAlZkHbJhx
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Kevin Hardegree-Ullman
@kevinkhu
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3. velj |
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This means that the Kepler result is not a fluke, and since K2 probes different regions in the Galaxy, this result is not exclusive to the Kepler field!
Here's how the radius valley looks in relation to incident stellar flux (left), and compared Kepler (right). pic.twitter.com/zN5ka4wilO
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Kevin Hardegree-Ullman
@kevinkhu
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3. velj |
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This result would not have been easily identifiable without a uniform set of stellar parameters. pic.twitter.com/nw5qHdkTsB
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Kevin Hardegree-Ullman
@kevinkhu
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3. velj |
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Many thanks to @jonKzink, @aussiastronomer, @AstroDressing, David R. Ciardi, and @JoshuaSchlieder for their important contributions to this paper! pic.twitter.com/Tl9tmNSHyD
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Kevin Hardegree-Ullman
@kevinkhu
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3. velj |
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BUT WAIT.... THERE'S MORE!
Go read "Scaling K2. II. Assembly of a Fully Automated C5 Planet Candidate Catalog Using EDI-Vetter", led by @jonKzink!
arxiv.org/abs/2001.11515 pic.twitter.com/I2nEfPQjmf
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Kevin Hardegree-Ullman
@kevinkhu
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3. velj |
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K2 Planet Occurrence Rates, here we come... pic.twitter.com/wGCqZ8VHy8
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