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A MACHINE-LEARNING METHOD TO INFER FUNDAMENTAL STELLAR PARAMETERS FROM PHOTOMETRIC LIGHT CURVES
Miller, A. A.,Bloom, J. S.,Richards, J. W.,Lee, Y. S.,Starr, D. L.,Butler, N. R.,Tokarz, S.,Smith, N.,Eisner, J. A. IOP Publishing 2015 The Astrophysical journal Vol.798 No.2
<P>A fundamental challenge for wide-field imaging surveys is obtaining follow-up spectroscopic observations: there are >10(9) photometrically cataloged sources, yet modern spectroscopic surveys are limited to similar to fewx10(6) targets. As we approach the Large Synoptic Survey Telescope era, new algorithmic solutions are required to cope with the data deluge. Here we report the development of a machine-learning framework capable of inferring fundamental stellar parameters (T-eff, log g, and [Fe/H]) using photometric-brightness variations and color alone. A training set is constructed from a systematic spectroscopic survey of variables with Hectospec/ Multi-Mirror Telescope. In sum, the training set includes similar to 9000 spectra, for which stellar parameters are measured using the SEGUE Stellar Parameters Pipeline (SSPP). We employed the random forest algorithm to perform a non-parametric regression that predicts Teff, log g, and [Fe/H] from photometric time-domain observations. Our final optimized model produces a cross-validated rms error (RMSE) of 165 K, 0.39 dex, and 0.33 dex for T-eff, log g, and [Fe/H], respectively. Examining the subset of sources for which the SSPP measurements are most reliable, the RMSE reduces to 125 K, 0.37 dex, and 0.27 dex, respectively, comparable to what is achievable via low-resolution spectroscopy. For variable stars this represents a approximate to 12%-20% improvement in RMSE relative to models trained with single-epoch photometric colors. As an application of our method, we estimate stellar parameters for similar to 54,000 known variables. We argue that this method may convert photometric time-domain surveys into pseudo-spectrographic engines, enabling the construction of extremely detailed maps of the Milky Way, its structure, and history.</P>
HECTOMAP AND HORIZON RUN 4: DENSE STRUCTURES AND VOIDS IN THE REAL AND SIMULATED UNIVERSE
Hwang, Ho Seong,Geller, Margaret J.,Park, Changbom,Fabricant, Daniel G.,Kurtz, Michael J.,Rines, Kenneth J.,Kim, Juhan,Diaferio, Antonaldo,Zahid, H. Jabran,Berlind, Perry,Calkins, Michael,Tokarz, Susa American Astronomical Society 2016 The Astrophysical journal Vol.818 No.2
<P>HectoMAP is a dense redshift survey of red galaxies covering a 53 deg(2) strip of the northern sky. HectoMAP is 97% complete for galaxies with r < 20.5, (g-r) > 1.0, and (r -i) > 0.5. The survey enables tests of the physical properties of large-scale structure at intermediate redshift against cosmological models. We use the Horizon Run 4, one of the densest and largest cosmological simulations based on the standard. Cold Dark Matter (Lambda CDM) model, to compare the physical properties of observed large-scale structures with simulated ones in a volume-limited sample covering 8 x 10(6) h(-3) Mpc(3) in the redshift range 0.22 < z < 0.44. We apply the same criteria to the observations and simulations to identify over-and under-dense large-scale features of the galaxy distribution. The richness and size distributions of observed over-dense structures agree well with the simulated ones. Observations and simulations also agree for the volume and size distributions of under-dense structures, voids. The properties of the largest over-dense structure and the largest void in HectoMAP are well within the distributions for the largest structures drawn from 300 Horizon Run 4 mock surveys. Overall the size, richness and volume distributions of observed large-scale structures in the redshift range 0.22 < z < 0.44 are remarkably consistent with predictions of the standard Lambda CDM model.</P>