Clustering-based Redshift Estimation: Comparison to Spectroscopic Redshifts

We investigate the potential and accuracy of clustering-based redshift estimation using the method proposed by M\'enard et al. (2013). This technique enables the inference of redshift distributions from measurements of the spatial clustering of arbitrary sources, using a set of reference objects for which redshifts are known. We apply it to a sample of spectroscopic galaxies from the Sloan Digital Sky Survey and show that, after carefully controlling the sampling efficiency over the sky, we can estimate redshift distributions with high accuracy. Probing the full colour space of the SDSS galaxies, we show that we can recover the corresponding mean redshifts with an accuracy ranging from $\delta$z=0.001 to 0.01. We indicate that this mapping can be used to infer the redshift probability distribution of a single galaxy. We show how the lack of information on the galaxy bias limits the accuracy of the inference and show comparisons between clustering redshifts and photometric redshifts for this dataset. This analysis demonstrates, using real data, that clustering-based redshift inference provides a powerful data-driven technique to explore the redshift distribution of arbitrary datasets, without any prior knowledge on the spectral energy distribution of the sources.Comment: 13 pages. Submitted to MNRAS. Comments welcom

Geometry Analysis of an Inverse-Geometry Volumetric CT System With Multiple Detector Arrays

An inverse-geometry volumetric CT (IGCT) system for imaging in a single fast rotation without cone-beam artifacts is being developed. It employs a large scanned source array and a smaller detector array. For a single-source/single-detector implementation, the FOV is limited to a fraction of the source size. Here we explore options to increase the FOV without increasing the source size by using multiple detectors spaced apart laterally to increase the range of radial distances sampled. We also look at multiple source array systems for faster scans. To properly reconstruct the FOV, Radon space must be sufficiently covered and sampled in a uniform manner. Optimal placement of the detectors relative to the source was determined analytically given system constraints (5cm detector width, 25cm source width, 45cm source-to-isocenter distance). For a 1x3 system (three detectors per source) detector spacing (DS) was 18deg and source-to-detector distances (SDD) were 113, 100 and 113cm to provide optimum Radon sampling and a FOV of 44cm. For multiple-source systems, maximum angular spacing between sources cannot exceed 125deg since detectors corresponding to one source cannot be occluded by a second source. Therefore, for 2x3 and 3x3 systems using the above DS and SDD, optimum spacing between sources is 115deg and 61deg respectively, requiring minimum scan rotations of 115deg and 107deg. Also, a 3x3 system can be much faster for full 360deg dataset scans than a 2x3 system (120deg vs. 245deg). We found that a significantly increased FOV can be achieved while maintaining uniform radial sampling as well as a substantial reduction in scan time using several different geometries. Further multi-parameter optimization is underway

Cosmic Shear Results from the Deep Lens Survey - II: Full Cosmological Parameter Constraints from Tomography

We present a tomographic cosmic shear study from the Deep Lens Survey (DLS), which, providing a limiting magnitude r_{lim}~27 (5 sigma), is designed as a pre-cursor Large Synoptic Survey Telescope (LSST) survey with an emphasis on depth. Using five tomographic redshift bins, we study their auto- and cross-correlations to constrain cosmological parameters. We use a luminosity-dependent nonlinear model to account for the astrophysical systematics originating from intrinsic alignments of galaxy shapes. We find that the cosmological leverage of the DLS is among the highest among existing >10 sq. deg cosmic shear surveys. Combining the DLS tomography with the 9-year results of the Wilkinson Microwave Anisotropy Probe (WMAP9) gives Omega_m=0.293_{-0.014}^{+0.012}, sigma_8=0.833_{-0.018}^{+0.011}, H_0=68.6_{-1.2}^{+1.4} km/s/Mpc, and Omega_b=0.0475+-0.0012 for LCDM, reducing the uncertainties of the WMAP9-only constraints by ~50%. When we do not assume flatness for LCDM, we obtain the curvature constraint Omega_k=-0.010_{-0.015}^{+0.013} from the DLS+WMAP9 combination, which however is not well constrained when WMAP9 is used alone. The dark energy equation of state parameter w is tightly constrained when Baryonic Acoustic Oscillation (BAO) data are added, yielding w=-1.02_{-0.09}^{+0.10} with the DLS+WMAP9+BAO joint probe. The addition of supernova constraints further tightens the parameter to w=-1.03+-0.03. Our joint constraints are fully consistent with the final Planck results and also the predictions of a LCDM universe.Comment: Accepted for publication in Ap

Passive Multi-Target Tracking Using the Adaptive Birth Intensity PHD Filter

Passive multi-target tracking applications require the integration of multiple spatially distributed sensor measurements to distinguish true tracks from ghost tracks. A popular multi-target tracking approach for these applications is the particle filter implementation of Mahler's probability hypothesis density (PHD) filter, which jointly updates the union of all target state space estimates without requiring computationally complex measurement-to-track data association. Although this technique is attractive for implementation in computationally limited platforms, the performance benefits can be significantly overshadowed by inefficient sampling of the target birth particles over the region of interest. We propose a multi-sensor extension of the adaptive birth intensity PHD filter described in (Ristic, 2012) to achieve efficient birth particle sampling driven by online sensor measurements from multiple sensors. The proposed approach is demonstrated using distributed time-difference-of-arrival (TDOA) and frequency-difference-of-arrival (FDOA) measurements, in which we describe exact techniques for sampling from the target state space conditioned on the observations. Numerical results are presented that demonstrate the increased particle density efficiency of the proposed approach over a uniform birth particle sampler.Comment: 21st International Conference on Information Fusio