Towards precision distances and 3D dust maps using broadband Period--Magnitude relations of RR Lyrae stars

We determine the period-magnitude relations of RR Lyrae stars in 13 photometric bandpasses from 0.4 to 12 {\mu}m using timeseries observations of 134 stars. The Bayesian formalism, extended from our previous work to include the effects of line-of-sight dust extinction, allows for the simultaneous inference of the posterior distribution of the mean absolute magnitude, slope of the period-magnitude power-law, and intrinsic scatter about a perfect power-law for each bandpass. In addition, the distance modulus and line-of-sight dust extinction to each RR Lyrae star in the calibration sample is determined, yielding a sample median fractional distance error of 0.66%. The intrinsic scatter in all bands appears to be larger than the photometric errors, except in WISE W1 (3.4 {\mu}m) and W2 (4.6 {\mu}m) where the photometric error ($\sigma \approx 0.05$ mag) is to be comparable or larger than the intrinsic scatter. Additional observations at these wavelengths could improve the inferred distances to these sources further. As an application of the methodology, we infer the distance to the RRc-type star RZCep at low Galactic latitude ($b = 5.5^\circ$) to be $\mu=8.0397\pm0.0123$ mag ($405.4\pm2.3$ pc) with colour excess $E(B-V)=0.2461\pm0.0089$ mag. This distance, equivalent to a parallax of $2467\pm14$ microarcsec, is consistent with the published HST parallax measurement but with an uncertainty that is 13 times smaller than the HST measurement. If our measurements (and methodology) hold up to scrutiny, the distances to these stars have been determined to an accuracy comparable to those expected with Gaia. As RR Lyrae are one of the primary components of the cosmic distance ladder, the achievement of sub-1% distance errors within a formalism that accounts for dust extinction may be considered a strong buttressing of the path to eventual 1% uncertainties in Hubble's constant.Comment: 21 pages, 29 figures, 2 tables, abstract abridged for arXiv. Comments solicited on referee report (received June 9, 2014) linked: https://gist.github.com/profjsb/c6c4e2f3a20ea02f1762 . Public archive of code used to generate results and figures: https://github.com/ckleinastro/period_luminosity_relation_fittin

Asteroid Models from Multiple Data Sources

In the past decade, hundreds of asteroid shape models have been derived using the lightcurve inversion method. At the same time, a new framework of 3-D shape modeling based on the combined analysis of widely different data sources such as optical lightcurves, disk-resolved images, stellar occultation timings, mid-infrared thermal radiometry, optical interferometry, and radar delay-Doppler data, has been developed. This multi-data approach allows the determination of most of the physical and surface properties of asteroids in a single, coherent inversion, with spectacular results. We review the main results of asteroid lightcurve inversion and also recent advances in multi-data modeling. We show that models based on remote sensing data were confirmed by spacecraft encounters with asteroids, and we discuss how the multiplication of highly detailed 3-D models will help to refine our general knowledge of the asteroid population. The physical and surface properties of asteroids, i.e., their spin, 3-D shape, density, thermal inertia, surface roughness, are among the least known of all asteroid properties. Apart for the albedo and diameter, we have access to the whole picture for only a few hundreds of asteroids. These quantities are nevertheless very important to understand as they affect the non-gravitational Yarkovsky effect responsible for meteorite delivery to Earth, or the bulk composition and internal structure of asteroids.Comment: chapter that will appear in a Space Science Series book Asteroids I

Correlation of the rate of Type Ia supernovae with the parent galaxy properties: Light and shadows

The identification of the progenitors of Type Ia Supernovae (SNIa) is extremely important in several astrophysical contexts, ranging from stellar evolution in close binary systems to evaluating cosmological parameters. Determining the distribution of the delay times (DTD) of SNIa progenitors can shed light on their nature. In this paper we investigate on the diagnostic capabilities on the DTD of the correlation between the SNIa rate and the parent galaxy properties by examining its systematics with the various parameters at play: simple stellar population models, the adopted description for the star formation history in galaxies, and the way in which the masses of the galaxies are evaluated. We compute models for the correlations of the SNIa rate with the parent galaxy color and specific star formation rate for a variety of input ingredients, and for a few astrophysically motivated DTD laws. The models are compared to the results of three independent observational surveys. We find that the scaling of the SNIa rate with the properties of the parent galaxy is sensitive to all input ingredients mentioned above. This is a severe limitation on the possibility to discriminate alternative DTDs. In addition, current surveys show some discrepancies for the rate measured in the reddest and bluest galaxies, likely due to limited statistics and inhomogeneity of the observations. For galaxies with intermediate colors the rates are in agreement, leading to a robust determination of the productivity of SNIa from stellar populations of $\simeq$ 0.8 events per 1000 \msun. Large stastistics of SNIa events along with accurate measurements of the star formation history in the galaxies are required to derive firm constraints on the DTD. LSST will achieve these results by providing the homogeneous, unbiased and vast database on both SNIa and galaxies.Comment: Astronomy and Astrophysics in press. Includes one more figure in the appendix. Notice the slight change of titl

Approximate Bayesian Image Interpretation using Generative Probabilistic Graphics Programs

The idea of computer vision as the Bayesian inverse problem to computer graphics has a long history and an appealing elegance, but it has proved difficult to directly implement. Instead, most vision tasks are approached via complex bottom-up processing pipelines. Here we show that it is possible to write short, simple probabilistic graphics programs that define flexible generative models and to automatically invert them to interpret real-world images. Generative probabilistic graphics programs consist of a stochastic scene generator, a renderer based on graphics software, a stochastic likelihood model linking the renderer's output and the data, and latent variables that adjust the fidelity of the renderer and the tolerance of the likelihood model. Representations and algorithms from computer graphics, originally designed to produce high-quality images, are instead used as the deterministic backbone for highly approximate and stochastic generative models. This formulation combines probabilistic programming, computer graphics, and approximate Bayesian computation, and depends only on general-purpose, automatic inference techniques. We describe two applications: reading sequences of degraded and adversarially obscured alphanumeric characters, and inferring 3D road models from vehicle-mounted camera images. Each of the probabilistic graphics programs we present relies on under 20 lines of probabilistic code, and supports accurate, approximately Bayesian inferences about ambiguous real-world images.Comment: The first two authors contributed equally to this wor

BayesNAS: A Bayesian Approach for Neural Architecture Search

One-Shot Neural Architecture Search (NAS) is a promising method to significantly reduce search time without any separate training. It can be treated as a Network Compression problem on the architecture parameters from an over-parameterized network. However, there are two issues associated with most one-shot NAS methods. First, dependencies between a node and its predecessors and successors are often disregarded which result in improper treatment over zero operations. Second, architecture parameters pruning based on their magnitude is questionable. In this paper, we employ the classic Bayesian learning approach to alleviate these two issues by modeling architecture parameters using hierarchical automatic relevance determination (HARD) priors. Unlike other NAS methods, we train the over-parameterized network for only one epoch then update the architecture. Impressively, this enabled us to find the architecture on CIFAR-10 within only 0.2 GPU days using a single GPU. Competitive performance can be also achieved by transferring to ImageNet. As a byproduct, our approach can be applied directly to compress convolutional neural networks by enforcing structural sparsity which achieves extremely sparse networks without accuracy deterioration.Comment: International Conference on Machine Learning 201

The Science Case for an Extended Spitzer Mission

Although the final observations of the Spitzer Warm Mission are currently scheduled for March 2019, it can continue operations through the end of the decade with no loss of photometric precision. As we will show, there is a strong science case for extending the current Warm Mission to December 2020. Spitzer has already made major impacts in the fields of exoplanets (including microlensing events), characterizing near Earth objects, enhancing our knowledge of nearby stars and brown dwarfs, understanding the properties and structure of our Milky Way galaxy, and deep wide-field extragalactic surveys to study galaxy birth and evolution. By extending Spitzer through 2020, it can continue to make ground-breaking discoveries in those fields, and provide crucial support to the NASA flagship missions JWST and WFIRST, as well as the upcoming TESS mission, and it will complement ground-based observations by LSST and the new large telescopes of the next decade. This scientific program addresses NASA's Science Mission Directive's objectives in astrophysics, which include discovering how the universe works, exploring how it began and evolved, and searching for life on planets around other stars.Comment: 75 pages. See page 3 for Table of Contents and page 4 for Executive Summar

The Reionization of Carbon

Observations suggest that CII was more abundant than CIV in the intergalactic medium towards the end of the hydrogen reionization epoch. This transition provides a unique opportunity to study the enrichment history of intergalactic gas and the growth of the ionizing background (UVB) at early times. We study how carbon absorption evolves from z=10-5 using a cosmological hydrodynamic simulation that includes a self-consistent multifrequency UVB as well as a well-constrained model for galactic outflows to disperse metals. Our predicted UVB is within 2-4 times that of Haardt & Madau (2012), which is fair agreement given the uncertainties. Nonetheless, we use a calibration in post-processing to account for Lyman-alpha forest measurements while preserving the predicted spectral slope and inhomogeneity. The UVB fluctuates spatially in such a way that it always exceeds the volume average in regions where metals are found. This implies both that a spatially-uniform UVB is a poor approximation and that metal absorption is not sensitive to the epoch when HII regions overlap globally even at column densites of 10^{12} cm^{-2}. We find, consistent with observations, that the CII mass fraction drops to low redshift while CIV rises owing the combined effects of a growing UVB and continued addition of carbon in low-density regions. This is mimicked in absorption statistics, which broadly agree with observations at z=6-3 while predicting that the absorber column density distributions rise steeply to the lowest observable columns. Our model reproduces the large observed scatter in the number of low-ionization absorbers per sightline, implying that the scatter does not indicate a partially-neutral Universe at z=6.Comment: 16 pages, 14 figures, accepted to MNRA