We present a description of the Dragonfly Wide Field Survey (DWFS), a deep photometric survey of a wide area of sky. The DWFS covers 330 deg2 in the equatorial GAMA fields and the Stripe 82 fields in the SDSS g and r bands. It is carried out with the 48-lens Dragonfly Telephoto Array, a telescope that is optimized for the detection of low surface brightness emission. The main goal of the survey is to study the dwarf galaxy population beyond the Local Group. In this paper, we describe the survey design and show early results. We reach 1σ depths of μ g  ≈ 31 mag arcsec−2 on arcminute scales and show that Milky Way satellites such as Sextans, Bootes, and Ursa Major should be detectable out to D ≳ 10 Mpc. We also provide an overview of the elements and operation of the 48-lens Dragonfly telescope and a detailed description of its data reduction pipeline. The pipeline is fully automated, with individual frames subjected to a rigorous series of quality tests. The sky subtraction is performed in two stages, ensuring that emission features with spatial scales up to ∼0.°9 × 0.°6 are preserved. The DWFS provides unparalleled sensitivity to low surface brightness features on arcminute scales.

Uncertainty in the wide-angle point-spread function (PSF) at large angles (tens of arcseconds and beyond) is one of the dominant sources of error in a number of important quantities in observational astronomy. Examples include the stellar mass and shape of galactic halos and the maximum extent of starlight in the disks of nearby galaxies. However, modeling the wide-angle PSF has long been a challenge in astronomical imaging. In this paper, we present a self-consistent method to model the wide-angle PSF in images. Scattered light from multiple bright stars is fitted simultaneously with a background model to characterize the extended wing of the PSF using a Bayesian framework operating on a pixel-by-pixel level. The method is demonstrated using our software elderflower and is applied to data from the Dragonfly Telephoto Array to model its PSF out to 20′–25′. We compare the wide-angle PSF of Dragonfly to that of a number of other telescopes, including the SDSS PSF and show that, on scales of arcminutes, the scattered light in the Dragonfly PSF is markedly lower than that of other wide-field imaging telescopes. The energy in the wings of the Dragonfly PSF is sufficiently low that optical cleanliness plays an important role in defining the PSF. This component of the PSF can be modeled accurately, highlighting the power of our self-contained approach.

The rapid development of deep learning-based methods has considerably advanced the field of protein structure prediction. The accuracy of predicting the 3D structures of simple proteins is comparable to that of experimentally determined structures, providing broad possibilities for structure-based biological studies. Another critical question is whether and how multistate structures can be predicted from a given protein sequence. In this study, analysis of multiple two-state proteins demonstrated that deep learning-based contact map predictions contain structural information on both states, which suggests that it is probably appropriate to change the target of deep learningbased protein structure prediction from one specific structure to multiple likely structures. Furthermore, by combining deep learning- and physics-based computational methods, we developed a protocol for exploring alternative conformations from a known structure of a given protein, by which we successfully approached the holo-state conformation of a leucine-binding protein from its apo-state structure.