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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>
Can a Point-of-Care Troponin I Assay be as Good as a Central Laboratory Assay? A MIDAS Investigation
W. Frank Peacock,Deborah Diercks,Robert Birkhahn,Adam J. Singer,Judd E. Hollander,Richard Nowak,Basmah Safdar,Chadwick D. Miller,Mary Peberdy,Francis Counselman,Abhinav Chandra,Joshua Kosowsky,James N 대한진단검사의학회 2016 Annals of Laboratory Medicine Vol.36 No.5
Background: We aimed to compare the diagnostic accuracy of the Alere Triage Cardio3 Tropinin I (TnI) assay (Alere, Inc., USA) and the PathFast cTnI-II (Mitsubishi Chemical Medience Corporation, Japan) against the central laboratory assay Singulex Erenna TnI assay (Singulex, USA). Methods: Using the Markers in the Diagnosis of Acute Coronary Syndromes (MIDAS) study population, we evaluated the ability of three different assays to identify patients with acute myocardial infarction (AMI). The MIDAS dataset, described elsewhere, is a prospective multicenter dataset of emergency department (ED) patients with suspected acute coronary syndrome (ACS) and a planned objective myocardial perfusion evaluation. Myocardial infarction (MI) was diagnosed by central adjudication. Results: The C-statistic with 95% confidence intervals (CI) for diagnosing MI by using a common population (n=241) was 0.95 (0.91-0.99), 0.95 (0.91-0.99), and 0.93 (0.89-0.97) for the Triage, Singulex, and PathFast assays, respectively. Of samples with detectable troponin, the absolute values had high Pearson (RP) and Spearman (RS) correlations and were RP =0.94 and RS=0.94 for Triage vs Singulex, RP =0.93 and RS=0.85 for Triage vs PathFast, and RP =0.89 and RS=0.73 for PathFast vs Singulex. Conclusions: In a single comparative population of ED patients with suspected ACS, the Triage Cardio3 TnI, PathFast, and Singulex TnI assays provided similar diagnostic performance for MI.