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 ~few x $10^6$ targets. As we approach the
Large Synoptic Survey Telescope (LSST) 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 (Teff, 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/MMT. In sum, the training set includes ~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 root-mean-square error (RMSE) of 165 K, 0.39 dex, and 0.33 dex for Teff, 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 ~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 ~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.
We present the results of a machine-learning (ML) based search for new R Coronae Borealis (RCB) stars and DY Persei-like stars (DYPers) in the Galaxy using cataloged light curves from the All-Sky Automated Survey (ASAS) Catalog of Variable Stars (ACV
S). RCB stars - a rare class of hydrogen-deficient carbon-rich supergiants - are of great interest owing to the insights they can provide on the late stages of stellar evolution. DYPers are possibly the low-temperature, low-luminosity analogs to the RCB phenomenon, though additional examples are needed to fully establish this connection. While RCB stars and DYPers are traditionally identified by epochs of extreme dimming that occur without regularity, the ML search framework more fully captures the richness and diversity of their photometric behavior. We demonstrate that our ML method can use newly discovered RCB stars to identify additional candidates within the same data set. Our search yields 15 candidates that we consider likely RCB stars/DYPers: new spectroscopic observations confirm that four of these candidates are RCB stars and four are DYPers. Our discovery of four new DYPers increases the number of known Galactic DYPers from two to six; noteworthy is that one of the new DYPers has a measured parallax and is m ~ 7 mag, making it the brightest known DYPer to date. Future observations of these new DYPers should prove instrumental in establishing the RCB connection. We consider these results, derived from a machine-learned probabilistic classification catalog, as an important proof-of-concept for the efficient discovery of rare sources with time-domain surveys.
We present observations and analysis of the broadband afterglow of Swift GRB 071025. Using optical and infrared (RIYJHK) photometry, we derive a photometric redshift of 4.4 < z < 5.2; at this redshift our simultaneous multicolour observations begin a
t ~30 s after the GRB trigger in the host frame and during the initial rising phase of the afterglow. We associate the light curve peak at 580 s in the observer frame with the formation of the forward shock, giving an estimate of the initial Lorentz factor Gamma_0 ~ 200. The red spectral energy distribution (even in regions not affected by the Lyman-alpha break) provides secure evidence of a large dust column. However, the inferred extinction curve shows a prominent flat component between 2000-3000 Angstroms in the rest-frame, inconsistent with any locally observed template but well-fit by models of dust formed by supernovae. Time-dependent fits to the extinction profile reveal no evidence of dust destruction and limit the decrease in the extinction column to Delta A_3000 < 0.54 mag after t = 50 s in the rest frame. Our observations provide evidence of a transition in dust properties at z~5, in agreement with studies of high-z quasars, and suggest that SN-formed dust continues to dominate the opacity of typical galaxies at this redshift.
In this work we present the first results of our imaging campaign at Keck Observatory to identify the host galaxies of dark gamma-ray bursts (GRBs), events with no detected optical afterglow or with detected optical flux significantly fainter than ex
pected from the observed X-ray afterglow. We find that out of a uniform sample of 29 Swift bursts rapidly observed by the Palomar 60-inch telescope through March 2008 (14 of which we classify as dark), all events have either a detected optical afterglow, a probable optical host-galaxy detection, or both. Our results constrain the fraction of Swift GRBs coming from very high redshift (z > 7), such as the recent GRB 090423, to between 0.2-7 percent at 80% confidence. In contrast, a significant fraction of the sample requires large extinction columns (host-frame A_V > 1 mag, with several events showing A_V > 2-6 mag), identifying dust extinction as the dominant cause of the dark GRB phenomenon. We infer that a significant fraction of GRBs (and, by association, of high-mass star formation) occurs in highly obscured regions. However, the host galaxies of dark GRBs seem to have normal optical colors, suggesting that the source of obscuring dust is local to the vicinity of the GRB progenitor or highly unevenly distributed within the host galaxy.
Temporal sampling does more than add another axis to the vector of observables. Instead, under the recognition that how objects change (and move) in time speaks directly to the physics underlying astronomical phenomena, next-generation wide-field syn
optic surveys are poised to revolutionize our understanding of just about anything that goes bump in the night (which is just about everything at some level). Still, even the most ambitious surveys will require targeted spectroscopic follow-up to fill in the physical details of newly discovered transients. We are now building a new system intended to ingest and classify transient phenomena in near real-time from high-throughput imaging data streams. Described herein, the Transient Classification Project at Berkeley will be making use of classification techniques operating on ``features extracted from time series and contextual (static) information. We also highlight the need for a community adoption of a standard representation of astronomical time series data (i.e., ``VOTimeseries).
