With the coming data deluge from synoptic surveys, there is a growing need for frameworks that can quickly and automatically produce calibrated classification probabilities for newly-observed variables based on a small number of time-series measurements. In this paper, we introduce a methodology for variable-star classification, drawing from modern machine-learning techniques. We describe how to homogenize the information gleaned from light curves by selection and computation of real-numbered metrics (features), detail methods to robustly estimate periodic light-curve features, introduce treeensemble methods for accurate variable star classification, and show how to rigorously evaluate the classification results using cross validation. On a 25-class data set of 1542 well-studied variable stars, we achieve a 22.8% overall classification error using the random forest classifier; this represents a 24% improvement over the best previous classifier on these data. This methodology is effective for identifying samples of specific science classes: for pulsational variables used in Milky Way tomography we obtain a discovery efficiency of 98.2% and for eclipsing systems we find an efficiency of 99.1%, both at 95% purity. We show that the random forest (RF) classifier is superior to other machine-learned methods in terms of accuracy, speed, and relative immunity to features with no useful class information; the RF classifier can also be used to estimate the importance of each feature in classification. Additionally, we present the first astronomical use of hierarchical classification methods to incorporate a known class taxonomy in the classifier, which further reduces the catastrophic error rate to 7.8%. Excluding low-amplitude sources, our overall error rate improves to 14%, with a catastrophic error rate of 3.5%. 5 High-precision photometry missions (Kepler, MOST, CoRoT, etc.) are already challenging the theoretical understanding of the origin of variability and the connection of some specific sources to established classes of variables. 6 General Catalog of Variable Stars, http://www.sai.msu.su/groups/cluster/gcvs/gcvs/ 7 Not discussed herein are the challenges associated with discovery of variability. See Shin et al. (2009) for a review.
We present extensive early photometric (ultraviolet through near-infrared) and spectroscopic (optical and near-infrared) data on supernova (SN) 2008D as well as X-ray data analysis on the associated Swift X-ray transient (XRT) 080109. Our data span a time range of 5 hours before the detection of the X-ray transient to 150 days after its detection, and detailed analysis allowed us to derive constraints on the nature of the SN and its progenitor; throughout we draw comparisons with results presented in the literature and find several key aspects that differ. We show that the X-ray spectrum of XRT 080109 can be fit equally well by an absorbed power law or a superposition of about equal parts of both power law and blackbody. Our data first established that SN 2008D is a spectroscopically normal SN Ib (i.e., showing conspicuous He lines), and show that SN 2008D had a relatively long rise time of 18 days and a modest optical peak luminosity. The early-time light curves of the SN are dominated by a cooling stellar envelope (for ∆t ≈ 0.1 − 4 day, most pronounced in the blue bands) followed by 56 Ni decay. We construct a reliable measurement of the bolometric output for this stripped-envelope SN, and, combined with estimates of E K and M ej from the literature, estimate the stellar radius R ⋆ of its probable Wolf-Rayet progenitor. According to the model of Waxman et al. and of Chevalier & Fransson, we derive R W07 ⋆ = 1.2 ± 0.7 R ⊙ and R CF08 ⋆ = 12 ± 7 R ⊙ , respectively; the latter being more in line with typical WN stars. Spectra obtained at 3 and 4 months after maximum light show double-peaked oxygen lines that we associate with departures from spherical symmetry, as has been suggested for the inner ejecta of a number of SN Ib cores.
We report on strong H 2 and CO absorption from gas within the host galaxy of gamma-ray burst (GRB) 080607. Analysis of our Keck/LRIS afterglow spectrum reveals a very large H I column density (N H I = 10 22.70±0.15 cm −2 ) and strong metal-line absorption at z GRB = 3.0363 with a roughly solar metallicity. We detect a series of A − X bandheads from CO and estimate N (CO) = 10 16.5±0.3 cm −2 and T CO ex > 100 K. We argue that the high excitation temperature results from UV pumping of the CO gas by the GRB afterglow. Similarly, we observe H 2 absorption via the Lyman-Werner bands and estimate N H2 = 10 21.2±0.2 cm −2 with T H2 ex = 10-300 K. The afterglow photometry suggests an extinction law with R V ≈ 4 and A V ≈ 3.2 mag and requires the presence of a modest 2175Å bump. Additionally, modeling of the Swift/XRT X-ray spectrum confirms a large column density with N H = 10 22.58±0.04 cm −2 . Remarkably, this molecular gas has extinction properties, metallicity, and a CO/H 2 ratio comparable to those of translucent molecular clouds of the Milky Way, suggesting that star formation at high z proceeds in similar environments as today. However, the integrated dust-to-metals ratio is sub-Galactic, suggesting the dust is primarily associated with the molecular phase while the atomic gas has a much lower dust-to-gas ratio. Sightlines like GRB 080607 serve as powerful probes of nucleosynthesis and star-forming regions in the young universe and contribute to the population of "dark" GRB afterglows.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
customersupport@researchsolutions.com
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.