We present a catalog of 23,790 extended low-surface-brightness galaxies (LSBGs) identified in~5000 deg 2 from the first three years of imaging data from the Dark Energy Survey (DES). Based on a single-component Sérsic

We introduce a new software package for modeling the point-spread function (PSF) of astronomical images, called Piff (PSFs In the Full FOV), which we apply to the first three years (known as Y3) of the Dark Energy Survey (DES) data. We describe the relevant details about the algorithms used by Piff to model the PSF, including how the PSF model varies across the field of view (FOV). Diagnostic results show that the systematic errors from the PSF modeling are very small over the range of scales that are important for the DES Y3 weak lensing analysis. In particular, the systematic errors from the PSF modeling are significantly smaller than the corresponding results from the DES year one (Y1) analysis. We also briefly describe some planned improvements to Piff that we expect to further reduce the modeling errors in future analyses.

The largely independent neuroscience literatures on race and status show increasingly that both constructs shape how we evaluate others. Following an overview and comparison of both literatures, we suggest that apparent differences in the brain regions supporting race-based and status-based evaluations may tap into distinct components of a common evaluative network. For example, perceiver motivations and/or category cues (e.g., perceptual vs. knowledge-based) can differ depending on whether one is processing race and/or status, ultimately recruiting distinct mechanisms within this common evaluative network. We emphasize the generalizability of this social neuroscience framework for dimensions beyond race and status and highlight how this framework raises new questions in the study of prejudice-reduction interventions.

We perform a comparison of different approaches to star-galaxy classification using the broad-band photometric data from Year 1 of the Dark Energy Survey. This is done by performing a wide range of tests with and without external 'truth' information, which can be ported to other similar datasets. We make a broad evaluation of the performance of the classifiers in two science cases with DES data that are most affected by this systematic effect: large-scale structure and Milky Way studies. In general, even though the default morphological classifiers used for DES Y1 cosmology studies are sufficient to maintain a low level of systematic contamination from stellar mis-classification, contamination can be reduced to the O(1%) level by using multi-epoch and infrared information from external datasets. For Milky Way studies the stellar sample can be augmented by ∼ 20% for a given flux limit. Reference catalogues used in this work are available at http://des.ncsa.illinois.edu/releases/y1a1.

We contacted a random sample of social/personality psychologists in the United States and asked for copies of their graduate syllabi. We coded more than 3,400 papers referenced on these syllabi for gender of authors as well as other characteristics. Less than 30% of the papers referenced on these syllabi were written by female first authors, with no evidence of a trend toward greater inclusion of papers published by female first authors since the 1980s. The difference in inclusion rates of female first-authored papers could not be explained by a preference for including classic over contemporary papers in syllabi (there was evidence of a recency bias instead) or the relative availability of female first-authored papers in the published literature. Implications are discussed.