Constructing A Flexible Likelihood Function For Spectroscopic Inference

We present a modular, extensible likelihood framework for spectroscopic inference based on synthetic model spectra. The subtraction of an imperfect model from a continuously sampled spectrum introduces covariance between adjacent datapoints (pixels) into the residual spectrum. For the high signal-to-noise data with large spectral range that is commonly employed in stellar astrophysics, that covariant structure can lead to dramatically underestimated parameter uncertainties (and, in some cases, biases). We construct a likelihood function that accounts for the structure of the covariance matrix, utilizing the machinery of Gaussian process kernels. This framework specifically address the common problem of mismatches in model spectral line strengths (with respect to data) due to intrinsic model imperfections (e.g., in the atomic/molecular databases or opacity prescriptions) by developing a novel local covariance kernel formalism that identifies and self-consistently downweights pathological spectral line "outliers." By fitting many spectra in a hierarchical manner, these local kernels provide a mechanism to learn about and build data-driven corrections to synthetic spectral libraries. An open-source software implementation of this approach is available at http://iancze.github.io/Starfish, including a sophisticated probabilistic scheme for spectral interpolation when using model libraries that are sparsely sampled in the stellar parameters. We demonstrate some salient features of the framework by fitting the high resolution $V$-band spectrum of WASP-14, an F5 dwarf with a transiting exoplanet, and the moderate resolution $K$-band spectrum of Gliese 51, an M5 field dwarf.Comment: Accepted to ApJ. Incorporated referees' comments. New figures 1, 8, 10, 12, and 14. Supplemental website: http://iancze.github.io/Starfish

Hyperspectral Unmixing Overview: Geometrical, Statistical, and Sparse Regression-Based Approaches

Imaging spectrometers measure electromagnetic energy scattered in their instantaneous field view in hundreds or thousands of spectral channels with higher spectral resolution than multispectral cameras. Imaging spectrometers are therefore often referred to as hyperspectral cameras (HSCs). Higher spectral resolution enables material identification via spectroscopic analysis, which facilitates countless applications that require identifying materials in scenarios unsuitable for classical spectroscopic analysis. Due to low spatial resolution of HSCs, microscopic material mixing, and multiple scattering, spectra measured by HSCs are mixtures of spectra of materials in a scene. Thus, accurate estimation requires unmixing. Pixels are assumed to be mixtures of a few materials, called endmembers. Unmixing involves estimating all or some of: the number of endmembers, their spectral signatures, and their abundances at each pixel. Unmixing is a challenging, ill-posed inverse problem because of model inaccuracies, observation noise, environmental conditions, endmember variability, and data set size. Researchers have devised and investigated many models searching for robust, stable, tractable, and accurate unmixing algorithms. This paper presents an overview of unmixing methods from the time of Keshava and Mustard's unmixing tutorial [1] to the present. Mixing models are first discussed. Signal-subspace, geometrical, statistical, sparsity-based, and spatial-contextual unmixing algorithms are described. Mathematical problems and potential solutions are described. Algorithm characteristics are illustrated experimentally.Comment: This work has been accepted for publication in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensin

Evidence for the accelerated expansion of the Universe from weak lensing tomography with COSMOS

We present a tomographic cosmological weak lensing analysis of the HST COSMOS Survey. Applying our lensing-optimized data reduction, principal component interpolation for the ACS PSF, and improved modelling of charge-transfer inefficiency, we measure a lensing signal which is consistent with pure gravitational modes and no significant shape systematics. We carefully estimate the statistical uncertainty from simulated COSMOS-like fields obtained from ray-tracing through the Millennium Simulation. We test our pipeline on simulated space-based data, recalibrate non-linear power spectrum corrections using the ray-tracing, employ photometric redshifts to reduce potential contamination by intrinsic galaxy alignments, and marginalize over systematic uncertainties. We find that the lensing signal scales with redshift as expected from General Relativity for a concordance LCDM cosmology, including the full cross-correlations between different redshift bins. For a flat LCDM cosmology, we measure sigma_8(Omega_m/0.3)^0.51=0.75+-0.08 from lensing, in perfect agreement with WMAP-5, yielding joint constraints Omega_m=0.266+0.025-0.023, sigma_8=0.802+0.028-0.029 (all 68% conf.). Dropping the assumption of flatness and using HST Key Project and BBN priors only, we find a negative deceleration parameter q_0 at 94.3% conf. from the tomographic lensing analysis, providing independent evidence for the accelerated expansion of the Universe. For a flat wCDM cosmology and prior w in [-2,0], we obtain w<-0.41 (90% conf.). Our dark energy constraints are still relatively weak solely due to the limited area of COSMOS. However, they provide an important demonstration for the usefulness of tomographic weak lensing measurements from space. (abridged)Comment: 26 pages, 25 figures, matches version accepted for publication by Astronomy and Astrophysic

Cosmological Parameters from Observations of Galaxy Clusters

Studies of galaxy clusters have proved crucial in helping to establish the standard model of cosmology, with a universe dominated by dark matter and dark energy. A theoretical basis that describes clusters as massive, multi-component, quasi-equilibrium systems is growing in its capability to interpret multi-wavelength observations of expanding scope and sensitivity. We review current cosmological results, including contributions to fundamental physics, obtained from observations of galaxy clusters. These results are consistent with and complementary to those from other methods. We highlight several areas of opportunity for the next few years, and emphasize the need for accurate modeling of survey selection and sources of systematic error. Capitalizing on these opportunities will require a multi-wavelength approach and the application of rigorous statistical frameworks, utilizing the combined strengths of observers, simulators and theorists.Comment: 53 pages, 21 figures. To appear in Annual Review of Astronomy & Astrophysic