Large Scale Structure at 24 Microns in the SWIRE Survey

We present initial results of galaxy clustering at 24μm by analyzing statistics of the projected galaxy distribution from counts-in-cells. This study focuses on the ELAIS-North1 SWIRE field. The sample covers ≃5.9 deg^2 and contains 24,715 sources detected at 24μm to a 5.6σ limit of 250μJy (in the lowest coverage regions). We have explored clustering as a function of 3.6 - 24μm and 24μm flux density using angular-averaged two-point correlation functions derived from the variance of counts-in-cells on scales 0°.05-0°.7. Using a power-law parameterization, w_2(θ)=A(θ/deg)^(1-γ), we find [A,γ] = [(5.43±0.20)×10^(-4),2.01±0.02] for the full sample (1σ errors throughout). We have inverted Limber's equation and estimated a spatial correlation length of r_0=3.32±0.19 h^(-1)Mpc for the full sample, assuming stable clustering and a redshift model consistent with observed 24μm counts. We also find that blue [f_ν(24)/f_ν(3.6)≤5.5] and red [f_ν(24)/f_ν(3.6)≥6.5] galaxies have the lowest and highest r_0 values respectively, implying that redder galaxies are more clustered (by a factor of ≈3 on scales ≳ 0°.2). Overall, the clustering estimates are smaller than those derived from optical surveys, but in agreement with results from IRAS and ISO in the mid-infrared. This extends the notion to higher redshifts that infrared selected surveys show weaker clustering than optical surveys

Host Galaxy Contribution to the Colours of `Red' Quasars

We describe an algorithm that measures self-consistently the relative galaxy contribution in a sample of radio-quasars from their optical spectra alone. This is based on a spectral fitting method which uses the size of the characteristic 4000\AA~ feature of elliptical galaxy SEDs. We apply this method to the Parkes Half-Jansky Flat Spectrum sample of Drinkwater et al. (1997) to determine whether emission from the host galaxy can significantly contribute to the very red optical-to-near-infrared colours observed. We find that at around $2\sigma$ confidence, most of the reddening in unresolved (mostly quasar-like) sources is unlikely to be due to contamination by a red stellar component.Comment: 11 pages, 11 figures. Accepted for Publication in Monthly Notices of the Royal Astronomical Societ

Automated Classification of Periodic Variable Stars detected by the Wide-field Infrared Survey Explorer

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.Comment: 48 pages, 17 figures, 1 table, accepted by A

AWAIC: A WISE Astronomical Image Co-adder

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 background matching and outlier detection using robust frame-stack statistics. Frame co-addition is based on using the detector's 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.Comment: 16 pages, 6 figures. Invited paper to appear in Proceedings of ADASS XVIII Conferenc

The Calibration of the WISE W1 and W2 Tully-Fisher Relation

In order to explore local large-scale structures and velocity fields, accurate galaxy distance measures are needed. We now extend the well-tested recipe for calibrating the correlation between galaxy rotation rates and luminosities -- capable of providing such distance measures -- to the all-sky, space-based imaging data from the Wide-field Infrared Survey Explorer (WISE) W1 ($3.4\mu$m) and W2 ($4.6\mu$m) filters. We find a linewidth to absolute magnitude correlation (known as the Tully-Fisher Relation, TFR) of $\mathcal{M}^{b,i,k,a}_{W1} = -20.35 - 9.56 (\log W^i_{mx} - 2.5)$ (0.54 magnitudes rms) and $\mathcal{M}^{b,i,k,a}_{W2} = -19.76 - 9.74 (\log W^i_{mx} - 2.5)$ (0.56 magnitudes rms) from 310 galaxies in 13 clusters. We update the I-band TFR using a sample 9% larger than in Tully & Courtois (2012). We derive $\mathcal{M}^{b,i,k}_I = -21.34 - 8.95 (\log W^i_{mx} - 2.5)$ (0.46 magnitudes rms). The WISE TFRs show evidence of curvature. Quadratic fits give $\mathcal{M}^{b,i,k,a}_{W1} = -20.48 - 8.36 (\log W^i_{mx} - 2.5) + 3.60 (\log W^i_{mx} - 2.5)^2$ (0.52 magnitudes rms) and $\mathcal{M}^{b,i,k,a}_{W2} = -19.91 - 8.40 (\log W^i_{mx} - 2.5) + 4.32 (\log W^i_{mx} - 2.5)^2$ (0.55 magnitudes rms). We apply an I-band -- WISE color correction to lower the scatter and derive $\mathcal{M}_{C_{W1}} = -20.22 - 9.12 (\log W^i_{mx} - 2.5)$ and $\mathcal{M}_{C_{W2}} = -19.63 - 9.11 (\log W^i_{mx} - 2.5)$ (both 0.46 magnitudes rms). Using our three independent TFRs (W1 curved, W2 curved and I-band), we calibrate the UNION2 supernova Type Ia sample distance scale and derive $H_0 = 74.4 \pm 1.4$(stat) $\pm\ 2.4$(sys) kms$^{-1}$ Mpc$^{-1}$ with 4% total error.Comment: 22 page, 21 figures, accepted to ApJ, Table 1 data at http://spartan.srl.caltech.edu/~neill/tfwisecal/table1.tx

Caltrans Keeps the Spitzer Pipelines Moving

The computer pipelines used to process digital infrared astronomical images from NASA's Spitzer Space Telescope require various input calibration-data files for characterizing the attributes and behaviors of the onboard focal-plane-arrays and their detector pixels, such as operability, darkcurrent offset, linearity, non-uniformity, muxbleed, droop, and point-response functions. The telescope has three very different science instruments, each with three or four spectral-band-pass channels, depending on the instrument. Moreover, each instrument has various operating modes (e.g., full array or sub-array in one case) and parameters (e.g., integration time). Calibration data that depend on these considerations are needed by pipelines for generating both science products (production pipelines) and higher-level calibration products (calibration pipelines). The calibration files are created in various formats either "off- line" or by the aforementioned calibration pipelines, depending on the above configuration details. Also, the calibration files are generally applicable to a certain time period and therefore must be selected accordingly for a given raw input image to be correctly processed. All of this complexity in selecting and retrieving calibration files for pipeline processing is handled by a procedural software program called "caltrans". This software, which is implemented in C and interacts with an Informix database, was developed at the Spitzer Science Center (SSC) and is now deployed in SSC daily operations. The software is rule-based, very flexible, and, for efficiency, capable of retrieving multiple calibration files with a single software-execution command