The Palomar Transient Factory (PTF) is a multi-epochal robotic survey of the northern sky that acquires data for the scientific study of transient and variable astrophysical phenomena. The camera and telescope provide for wide-field imaging in optica
l bands. In the five years of operation since first light on December 13, 2008, images taken with Mould-R and SDSS-g camera filters have been routinely acquired on a nightly basis (weather permitting), and two different H-alpha filters were installed in May 2011 (656 nm and 663 nm). The PTF image-processing and data-archival program at the Infrared Processing and Analysis Center (IPAC) is tailored to receive and reduce the data, and, from it, generate and preserve astrometrically and photometrically calibrated images, extracted source catalogs, and coadded reference images. Relational databases have been deployed to track these products in operations and the data archive. The fully automated system has benefited by lessons learned from past IPAC projects and comprises advantageous features that are potentially incorporable into other ground-based observatories. Both off-the-shelf and in-house software have been utilized for economy and rapid development. The PTF data archive is curated by the NASA/IPAC Infrared Science Archive (IRSA). A state-of-the-art custom web interface has been deployed for downloading the raw images, processed images, and source catalogs from IRSA. Access to PTF data products is currently limited to an initial public data release (M81, M44, M42, SDSS Stripe 82, and the Kepler Survey Field). It is the intent of the PTF collaboration to release the full PTF data archive when sufficient funding becomes available.
We describe a methodology to classify periodic variable stars identified using photometric time-series measurements constructed from the Wide-field Infrared Survey Explorer (WISE) full-mission single-exposure Source Databases. This will assist in the
future construction of a WISE Variable Source Database that assigns variables to specific science classes as constrained by the WISE observing cadence with statistically meaningful classification probabilities. We have analyzed the WISE light curves of 8273 variable stars identified in previous optical variability surveys (MACHO, GCVS, and ASAS) and show that Fourier decomposition techniques can be extended into the mid-IR to assist with their classification. Combined with other periodic light-curve features, this sample is then used to train a machine-learned classifier based on the random forest (RF) method. Consistent with previous classification studies of variable stars in general, the RF machine-learned classifier is superior to other methods in terms of accuracy, robustness against outliers, and relative immunity to features that carry little or redundant class information. For the three most common classes identified by WISE: Algols, RR Lyrae, and W Ursae Majoris type variables, we obtain classification efficiencies of 80.7%, 82.7%, and 84.5% respectively using cross-validation analyses, with 95% confidence intervals of approximately +/-2%. These accuracies are achieved at purity (or reliability) levels of 88.5%, 96.2%, and 87.8% respectively, similar to that achieved in previous automated classification studies of periodic variable stars.
ICORE is a command-line driven co-addition, mosaicking and resolution enhancement (HiRes) tool for creating science quality products from image data in FITS format and with World Coordinate System information following the FITS-WCS standard. It inclu
des preparatory steps such as image background matching, photometric gain-matching, and pixel-outlier rejection. Co-addition and/or HiResing can be performed in either the inertial WCS, or in the rest frame of a moving object. Three interpolation methods are supported: overlap-area weighting, drizzle, and weighting by the detector Point Response Function (PRF). The latter enables the creation of matched-filtered products for optimal point-source detection, but most importantly allows for resolution enhancement using a spatially-dependent deconvolution method. This is a variant of the classic Richardson-Lucy algorithm with the added benefit to simultaneously register and co-add multiple images to optimize signal-to-noise and sampling of the instrumental PSF. It can assume real (or otherwise flat) image priors, mitigate ringing artifacts, and assess the quality of image solutions using statistically-motivated convergence criteria. Uncertainties are also estimated and internally validated for all products. The software supports multithreading that can be configured for different architectures. Numerous example scripts are included (with test data) to co-add and/or HiRes image data from Spitzer-IRAC/MIPS, WISE and Herschel-SPIRE.
The Saha equation describes the relative number density of consecutive ionization levels of a given atomic species under conditions of thermodynamic equilibrium in an ionized gas. Because the number density in the denominator may be very small, speci
al steps must be taken to ensure numerical stability. In this paper we recast the equation into a form in which each ionization fraction is normalized by the total number density of the atomic species, analogous to the Boltzmann equation describing the distribution of excitation states for a given ion.
The Two Micron All-Sky Survey (2MASS) has provided a uniform photometric catalog to search for previously unknown red AGN and QSOs. We have extended the search to the southern equatorial sky by obtaining spectra for 1182 AGN candidates using the 6dF
multifibre spectrograph on the UK Schmidt Telescope. These were scheduled as auxiliary targets for the 6dF Galaxy Redshift Survey. The candidates were selected using a single color cut of J - Ks > 2 to Ks ~ 15.5 and a galactic latitude of |b|>30 deg. 432 spectra were of sufficient quality to enable a reliable classification. 116 sources (or ~27%) were securely classified as type 1 AGN, 20 as probable type 1s, and 57 as probable type 2 AGN. Most of them span the redshift range 0.05<z<0.5 and only 8 (or ~6%) were previously identified as AGN or QSOs. Our selection leads to a significantly higher AGN identification rate amongst local galaxies (>20%) than in any previous galaxy survey. A small fraction of the type 1 AGN could have their optical colors reddened by optically thin dust with A_V<2 mag relative to optically selected QSOs. A handful show evidence for excess far-IR emission. The equivalent width (EW) and color distributions of the type 1 and 2 AGN are consistent with AGN unified models. In particular, the EW of the [OIII] emission line weakly correlates with optical--near-IR color in each class of AGN, suggesting anisotropic obscuration of the AGN continuum. Overall, the optical properties of the 2MASS red AGN are not dramatically different from those of optically-selected QSOs. Our near-IR selection appears to detect the most near-IR luminous QSOs in the local universe to z~0.6 and provides incentive to extend the search to deeper near-IR surveys.
We describe a new image co-addition tool, AWAIC, to support the creation of a digital Image Atlas from the multiple frame exposures acquired with the Wide-field Infrared Survey Explorer (WISE). AWAIC includes preparatory steps such as frame backgroun
d matching and outlier detection using robust frame-stack statistics. Frame co-addition is based on using the detectors Point Response Function (PRF) as an interpolation kernel. This kernel reduces the impact of prior-masked pixels; enables the creation of an optimal matched filtered product for point source detection; and most important, it allows for resolution enhancement (HiRes) to yield a model of the sky that is consistent with the observations to within measurement error. The HiRes functionality allows for non-isoplanatic PRFs, prior noise-variance weighting, uncertainty estimation, and includes a ringing-suppression algorithm. AWAIC also supports the popular overlap-area weighted interpolation method, and is generic enough for use on any astronomical image data that supports the FITS and WCS standards.
