We present a new three-dimensional map of dust reddening, based on Gaia parallaxes and stellar photometry from Pan-STARRS 1 and 2MASS. This map covers the sky north of a decl. of −30°, out to a distance of a few kiloparsecs. This new map contains three major improvements over our previous work. First, the inclusion of Gaia parallaxes dramatically improves distance estimates to nearby stars. Second, we incorporate a spatial prior that correlates the dust density across nearby sightlines. This produces a smoother map, with more isotropic clouds and smaller distance uncertainties, particularly to clouds within the nearest kiloparsec. Third, we infer the dust density with a distance resolution that is four times finer than in our previous work, to accommodate the improvements in signal-to-noise enabled by the other improvements. As part of this work, we infer the distances, reddenings, and types of 799 million stars. (Our 3D dust map can be accessed at doi:10.7910/DVN/2EJ9TX or through the Python package dustmaps, while our catalog of stellar parameters can be accessed at doi:10.7910/DVN/AV9GXO. More information about the map, as well as an interactive viewer, can be found at argonaut.skymaps.info.) We obtain typical reddening uncertainties that are ∼30% smaller than those reported in the Gaia DR2 catalog, reflecting the greater number of photometric passbands that enter into our analysis.

Hyper Suprime-Cam (HSC) is a wide-field imaging camera on the prime focus of the 8.2-m Subaru telescope on the summit of Mauna Kea in Hawaii. A team of scientists from Japan, Taiwan, and Princeton University is using HSC to carry out a 300-night multi-band imaging survey of the high-latitude sky. The survey includes three layers: the Wide layer will cover 1400 deg² in five broad bands (grizy), with a 5 σ point-source depth of r ≈ 26. The Deep layer covers a total of 26 deg² in four fields, going roughly a magnitude fainter, while the UltraDeep layer goes almost a magnitude fainter still in two pointings of HSC (a total of 3.5 deg²). Here we describe the instrument, the science goals of the survey, and the survey strategy and data processing. This paper serves as an introduction to a special issue of the Publications of the Astronomical Society of Japan, which includes a large number of technical and scientific papers describing results from the early phases of this survey.

Inference of the physical properties of stellar populations from observed photometry and spectroscopy is a key goal in the study of galaxy evolution. In recent years, the quality and quantity of the available data have increased, and there have been corresponding efforts to increase the realism of the stellar population models used to interpret these observations. Describing the observed galaxy spectral energy distributions in detail now requires physical models with a large number of highly correlated parameters. These models do not fit easily on grids and necessitate a full exploration of the available parameter space. We present Prospector, a flexible code for inferring stellar population parameters from photometry and spectroscopy spanning UV through IR wavelengths. This code is based on forward modeling the data and Monte Carlo sampling the posterior parameter distribution, enabling complex models and exploration of moderate dimensional parameter spaces. We describe the key ingredients of the code and discuss the general philosophy driving the design of these ingredients. We demonstrate some capabilities of the code on several data sets, including mock and real data.

We measure cosmic weak lensing shear power spectra with the Subaru Hyper Suprime-Cam (HSC) survey first-year shear catalog covering 137 deg2 of the sky. Thanks to the high effective galaxy number density of ∼17 arcmin−2, even after conservative cuts such as a magnitude cut of i < 24.5 and photometric redshift cut of 0.3 ≤ z ≤ 1.5, we obtain a high-significance measurement of the cosmic shear power spectra in four tomographic redshift bins, achieving a total signal-to-noise ratio of 16 in the multipole range 300 ≤ ℓ ≤ 1900. We carefully account for various uncertainties in our analysis including the intrinsic alignment of galaxies, scatters and biases in photometric redshifts, residual uncertainties in the shear measurement, and modeling of the matter power spectrum. The accuracy of our power spectrum measurement method as well as our analytic model of the covariance matrix are tested against realistic mock shear catalogs. For a flat Λ cold dark matter model, we find $S\,_{8}\equiv \sigma _8(\Omega _{\rm m}/0.3)^\alpha =0.800^{+0.029}_{-0.028}$ for α = 0.45 ($S\,_8=0.780^{+0.030}_{-0.033}$ for α = 0.5) from our HSC tomographic cosmic shear analysis alone. In comparison with Planck cosmic microwave background constraints, our results prefer slightly lower values of S8, although metrics such as the Bayesian evidence ratio test do not show significant evidence for discordance between these results. We study the effect of possible additional systematic errors that are unaccounted for in our fiducial cosmic shear analysis, and find that they can shift the best-fit values of S8 by up to ∼0.6 σ in both directions. The full HSC survey data will contain several times more area, and will lead to significantly improved cosmological constraints.

Nonparametric star formation histories (SFHs) have long promised to be the “gold standard” for galaxy spectral energy distribution (SED) modeling as they are flexible enough to describe the full diversity of SFH shapes, whereas parametric models rule out a significant fraction of these shapes a priori. However, this flexibility is not fully constrained even with high-quality observations, making it critical to choose a well-motivated prior. Here, we use the SED-fitting code Prospector to explore the effect of different nonparametric priors by fitting SFHs to mock UV–IR photometry generated from a diverse set of input SFHs. First, we confirm that nonparametric SFHs recover input SFHs with less bias and return more accurate errors than do parametric SFHs. We further find that, while nonparametric SFHs robustly recover the overall shape of the input SFH, the primary determinant of the size and shape of the posterior star formation rate as a function of time (SFR(t)) is the choice of prior, rather than the photometric noise. As a practical demonstration, we fit the UV–IR photometry of ∼6000 galaxies from the Galaxy and Mass Assembly survey and measure scatters between priors to be 0.1 dex in mass, 0.8 dex in SFR_{100 Myr}, and 0.2 dex in mass-weighted ages, with the bluest star-forming galaxies showing the most sensitivity. An important distinguishing characteristic for nonparametric models is the characteristic timescale for changes in SFR(t). This difference controls whether galaxies are assembled in bursts or in steady-state star formation, corresponding respectively to (feedback-dominated/accretion-dominated) models of galaxy formation and to (larger/smaller) confidence intervals derived from SED fitting. High-quality spectroscopy has the potential to further distinguish between these proposed models of SFR(t).