THE CANNON: A DATA-DRIVEN APPROACH TO STELLAR LABEL DETERMINATION

Abstract

New spectroscopic surveys offer the promise of stellar parameters and abundances ("stellar labels") for hundreds of thousands of stars; this poses a formidable spectral modeling challenge. In many cases, there is a subset of reference objects for which the stellar labels are known with high(er) fidelity. We take advantage of this with The Cannon, a new data-driven approach for determining stellar labels from spectroscopic data. The Cannon learns from the "known" labels of reference stars how the continuum-normalized spectra depend on these labels by fitting a flexible model at each wavelength; then, The Cannon uses this model to derive labels for the remaining survey stars. We illustrate The Cannon by training the model on only 542 stars in 19 clusters as reference objects, with T eff , g log , and [Fe H] as the labels, and then applying it to the spectra of 55,000 stars from APOGEE DR10. The Cannon is very accurate. Its stellar labels compare well to the stars for which APOGEE pipeline (ASPCAP) labels are provided in DR10, with rms differences that are basically identical to the stated ASPCAP uncertainties. Beyond the reference labels, The Cannon makes no use of stellar models nor any line-list, but needs a set of reference objects that span label-space. The Cannon performs well at lower signal-to-noise, as it delivers comparably good labels even at one-ninth the APOGEE observing time. We discuss the limitations of The Cannon and its future potential, particularly, to bring different spectroscopic surveys onto a consistent scale of stellar labels.

Apogee Kinematics. I. Overview of the Kinematics of the Galactic Bulge as Mapped by Apogee

“…using TheCannon (Ness et al 2015). The set of reference objects for training TheCannon is composed of a subsample of ≈2150 high signal-to-noise ratio (S/N∼300) APOGEE stars with no STAR BAD set in ASPCAPFLAG, as well as the globular and open cluster stars (Mészáros et al 2013).…”

“…The set of reference objects for training TheCannon is composed of a subsample of ≈2150 high signal-to-noise ratio (S/N∼300) APOGEE stars with no STAR BAD set in ASPCAPFLAG, as well as the globular and open cluster stars (Mészáros et al 2013). We adopt the high-S/N data and their measurements from ASPCAP as our reference objects in order to determine precise stellar parameters for the lower-S/N APOGEE data, as described in Ness et al (2015). We use the corrected…”

“…We also include the metal-poor ( Fe H 1.0 [ ] < -) globular cluster stars with their isochrone-corrected training labels (Ness et al 2015) so as to extend our training set to the lowest metallicities. The Pleiades open cluster, which contains the main-sequence stars in the training set, enables dwarf stars in the DR12 data to be identified by TheCannon and therefore eliminated.…”

Chemical Tagging Can Work: Identification of Stellar Phase-Space Structures Purely by Chemical-Abundance Similarity

“…In what follows, we are going to use APOGEE DR12 data (Alam et al 2015;Holtzman et al 2015) from SDSS-III (Eisenstein et al 2011), in which we can re-derive 15 element abundances (C, N, O, Na, Mg, Al, Si, S, K, Ca, Ti, V, Mn, Fe, Ni)using TheCannon (Ness et al 2015). The APOGEE data set covers a huge radial extent and-because the data are taken in the infrared-is capable of exploring all parts of the disk, including the thin parts.…”

Stellar classification from single-band imaging using machine learning

“…Knowledge of spectral types and stellar parameters such as mass and age for large numbers of stars is of course of direct interest for stellar population studies and to study the formation history of our Galaxy (e.g. Smiljanic et al 2014;Yang & Li 2015;Ness et al 2015).…”