1 HiCannon: Data-driven spectroscopy of cool stars at high spectral resolution Aida Behmard1, Erik Petigura2,3, Andrew Howard2 1Division of Geological.

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Presentation transcript:

1 HiCannon: Data-driven spectroscopy of cool stars at high spectral resolution Aida Behmard1, Erik Petigura2,3, Andrew Howard2 1Division of Geological and Planetary Science, Caltech 2Cahill Center for Astrophysics, Caltech 3Hubble Fellow ExSoCal: Sept. 18 2018

How do we determine stellar parameters / abundances before? 2 How do we determine stellar parameters / abundances before? Synthetic spectral libraries SpecMatch-Syn (Petigura+2015) hv Teff = 5600 K Teff = 4800 K Construct model stellar atmospheres, model radiative transfer of photons as they pass through the photosphere towards observer Synthetic spectra generated from SpecMatch-Syn – they all have identical input parameters other than Teff = 4000 K

Luminosity (compared to Sun) 3 Synthetic spectral libraries make good abundance / parameter predictions for these stars 106 Luminosity (compared to Sun) 10 These? Not so much. Why are M dwarfs a popular choice for exoplanet host stars? “Coolest stars, in particular, offer observational advantages: larger reflex motions, deeper transits (for well-aligned systems), and better star-planet contrast ratios.” 10-4 Teff [K] O B A F G K M 35000 20000 11000 7500 5900 5200 3800

The Cannon 4 Data-driven (ML) approach for predicting stellar parameters + elemental abundances from spectra Example labels: Temperature Teff Bulk metallicity [Fe/H] Stellar radius R* Makes no use of synthetic spectral models / libraries – good for K and M dwarfs!

The Cannon “Training Step” Construct flux model 5 “Training Step” Construct flux model fjn = V(ln) θj + noise “Test Step” Apply fjn to test set spectra

High Resolution Echelle Spectrometer (HIRES) sample 6 High Resolution Echelle Spectrometer (HIRES) sample 141 stars, K and M dwarfs: Teff < 5200 K No giants (R* < 1 R) Teff, R*, [Fe/H]: Interferometry SED Modeling Gaia Parallaxes

7 The Cannon cannot make predictions for spectra not well-represented in training set! GL896A: a fast rotator?

…diversify the training set! 8 …diversify the training set! SpecMatch-Syn (Petigura+2015) Copy each spectrum in training set and artificially broaden Here: +0-20 km/s

9 GL896A Before: After:

10 Results

11 Future work Adapt HiCannon to include prior information (line lists, etc.) – Bayesian framework? Elemental abundance studies: C/O?