Jake Vanderplas

FIRST-YEAR SLOAN DIGITAL SKY SURVEY-II (SDSS-II) SUPERNOVA RESULTS: CONSTRAINTS ON NONSTANDARD COSMOLOGICAL MODELS

We use the new Type Ia supernovae discovered by the Sloan Digital Sky Survey-II supernova survey, together with additional supernova data sets as well as observations of the cosmic microwave background and baryon acoustic oscillations to constrain cosmological models. This complements the standard cosmology analysis presented by Kessler et al. in that we discuss and rank a number of the most popular nonstandard cosmology scenarios. When this combined data set is analyzed using the MLCS2k2 light-curve fitter, we find that more exotic models for cosmic acceleration provide a better fit to the data than the ΛCDM model. For example, the flat Dvali-Gabadadze-Porrati model is ranked higher by our information-criteria (IC) tests than the standard model with a flat universe and a cosmological constant. When the supernova data set is instead analyzed using the SALT-II light-curve fitter, the standard cosmological-constant model fares best. This investigation of how sensitive cosmological model selection is to assumptions about, and within, the light-curve fitters thereby highlights the need for an improved understanding of these unresolved systematic effects. Our investigation also includes inhomogeneous Lemaitre-Tolman-Bondi (LTB) models. While our LTB models can be made to fit the supernova data as well as any other model, the extra parameters they require are not supported by our IC analysis. Finally, we explore more model-independent ways to investigate the cosmic expansion based on this new data set.

SNANA: A Public Software Package for Supernova Analysis

We describe a general analysis package for supernova (SN) light curves, called SNANA, that contains a simulation, a light-curve fitter, and a cosmology fitter. The software is designed with the primary goal of using SNe Ia as distance indicators for the determination of cosmological parameters, but it can also be used to study efficiencies for analyses of SN rates, estimate contamination from non-Ia SNe, and optimize future surveys. Several SN models are available within the same software architecture, allowing technical features such as K-corrections to be consistently used among multiple models, and thus making it easier to make detailed comparisons between models. New and improved light-curve models can be easily added. The software works with arbitrary surveys and telescopes and has already been used by several collaborations, leading to more robust and easy-to-use code. This software is not intended as a final product release, but rather it is designed to undergo continual improvements from the community as more is learned about SNe. We give an overview of the SNANA capabilities, as well as some of its limitations.

First-year Sloan Digital Sky Survey-II supernova results: consistency and constraints with other intermediate-redshift data sets

ABSTRACT We present an analysis of the luminosity distances of Type Ia Supernovae (SNe) from the Sloan Digital Sky Survey-II (SDSS-II) SN Survey in conjunction with other intermediate-redshift (z 97 per cent level from this single data set. We find good agreement between the SN and BAO distance measurements, both consistent with a Λ-dominated cold dark matter cosmology, as demonstrated through an analysis of the distance duality relationship between the luminosity (dL) and angular diameter (dA) distance measures. We then use these data to estimate w within this restricted redshift range (z < 0.4). Our most stringent result comes from the combination of all our intermediate-redshift data (SDSS-II SNe, BAO, ISW and redshift-space distortions), giving w = -0.81+0.16-0.18 (stat) +/- 0.15 (sys) and ΩM = 0.22+0.09-0.08 assuming a flat universe. This value of w and associated errors only change slightly if curvature is allowed to vary, consistent with constraints from the cosmic microwave background. We also consider more limited combinations of the geometrical (SN, BAO) and dynamical (ISW, redshift-space distortions) probes.

REDUCING THE DIMENSIONALITY OF DATA: LOCALLY LINEAR EMBEDDING OF SLOAN GALAXY SPECTRA

We introduce locally linear embedding (LLE) to the astronomical community as a new classification technique, using Sloan Digital Sky Survey spectra as an example data set. LLE is a nonlinear dimensionality reduction technique that has been studied in the context of computer perception. We compare the performance of LLE to well-known spectral classification techniques, e.g., principal component analysis and line-ratio diagnostics. We find that LLE combines the strengths of both methods in a single, coherent technique, and leads to improved classification of emission-line spectra at a relatively small computational cost. We also present a data subsampling technique that preserves local information content, and proves effective for creating small, efficient training samples from large, high-dimensional data sets. Software used in this LLE-based classification is made available.

Astrophysical Tests of Modified Gravity: the Morphology and Kinematics of Dwarf Galaxies

This paper is the third in a series on tests of gravity using observations of stars and nearby dwarf galaxies. We carry out four distinct tests using published data on the kinematics and morphology of dwarf galaxies, motivated by the theoretical work of Hui et al. (2009) and Jain & Vanderplas (2011). In a wide class of gravity theories a scalar field couples to matter and provides an attractive fifth force. Due to their different self-gravity, stars and gas may respond differently to the scalar force leading to several observable deviations from standard gravity. HI gas, red giant stars and main sequence stars can be displaced relative to each other, and the stellar disk can display warps or asymmetric rotation curves aligned with external potential gradients. To distinguish the effects of modified gravity from standard astrophysical phenomena, we use a control sample of galaxies that are expected to be screened from the fifth force. In all cases we find no significant deviation from the null hypothesis of general relativity. The limits obtained from dwarf galaxies are not yet competitive with the limits from cepheids obtained in our first paper, but can be improved to probe regions of parameter space that are inaccessible using other tests. We discuss how our methodology can be applied to new radio and optical observations of nearby galaxies.

THREE-DIMENSIONAL RECONSTRUCTION OF THE DENSITY FIELD: AN SVD APPROACH TO WEAK-LENSING TOMOGRAPHY

We present a new method for constructing three-dimensional mass maps from gravitational lensing shear data. We solve the lensing inversion problem using truncation of singular values (within the context of generalized least-squares estimation) without a priori assumptions about the statistical nature of the signal. This singular value framework allows a quantitative comparison between different filtering methods: we evaluate our method beside the previously explored Wiener-filter approaches. Our method yields near-optimal angular resolution of the lensing reconstruction and allows cluster sized halos to be de-blended robustly. It allows for mass reconstructions which are two to three orders of magnitude faster than the Wiener-filter approach; in particular, we estimate that an all-sky reconstruction with arcminute resolution could be performed on a timescale of hours. We find however that linear, non-parametric reconstructions have a fundamental limitation in the resolution achieved in the redshift direction.

Classification of Stellar Spectra with Local Linear Embedding

We investigate the use of dimensionality reduction techniques for the classification of stellar spectra selected from the SDSS. Using local linear embedding (LLE), a technique that preserves the local (and possibly non-linear) structure within high dimensional data sets, we show that the majority of stellar spectra can be represented as a one dimensional sequence within a three dimensional space. The position along this sequence is highly correlated with spectral temperature. Deviations from this “stellar locus” are indicative of spectra with strong emission lines (including misclassified galaxies) or broad absorption lines (e.g. Carbon stars). Based on this analysis, we propose a hierarchical classification scheme using LLE that progressively identifies and classifies stellar spectra in a manner that requires no feature extraction and that can reproduce the classic MK classifications to an accuracy of one type.

INTERPOLATING MASKED WEAK-LENSING SIGNAL WITH KARHUNEN-LOÈVE ANALYSIS

We explore the utility of Karhunen-Lo?ve (KL) analysis in solving practical problems in the analysis of gravitational shear surveys. Shear catalogs from large-field weak-lensing surveys will be subject to many systematic limitations, notably incomplete coverage and pixel-level masking due to foreground sources. We develop a method to use two-dimensional KL eigenmodes of shear to interpolate noisy shear measurements across masked regions. We explore the results of this method with simulated shear catalogs, using statistics of high-convergence regions in the resulting map. We find that the KL procedure not only minimizes the bias due to masked regions in the field, it also reduces spurious peak counts from shape noise by a factor of ~3 in the cosmologically sensitive regime. This indicates that KL reconstructions of masked shear are not only useful for creating robust convergence maps from masked shear catalogs, but also offer promise of improved parameter constraints within studies of shear peak statistics.

A demonstration of iterative parallel array processing in support of telescope image analysis

In this demonstration, we present AscotDB, a new tool for the analysis of telescope image data. AscotDB results from the integration of ASCOT, a Web-based tool for the collaborative analysis of telescope images and their metadata, and SciDB, a parallel array processing engine. We demonstrate the novel data exploration supported by this integrated tool on a 1 TB dataset comprising scientifically accurate, simulated telescope images. We also demonstrate novel iterative-processing features that we added to SciDB in order to support this use-case.

Cluster-lensing: A Python Package for Galaxy Clusters & Miscentering

We describe a new open source package for calculating properties of galaxy clusters, including Navarro, Frenk, and White halo profiles with and without the effects of cluster miscentering. This pure-Python package, cluster-lensing, provides well-documented and easy-to-use classes and functions for calculating cluster scaling relations, including mass-richness and mass-concentration relations from the literature, as well as the surface mass density and differential surface mass density profiles, probed by weak lensing magnification and shear. Galaxy cluster miscentering is especially a concern for stacked weak lensing shear studies of galaxy clusters, where offsets between the assumed and the true underlying matter distribution can lead to a significant bias in the mass estimates if not accounted for. This software has been developed and released in a public GitHub repository, and is licensed under the permissive MIT license. The cluster-lensing package is archived on Zenodo. Full documentation, source code, and installation instructions are available at http://jesford.github.io/cluster-lensing/.