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Cryo-EM structure determination of small proteins by nanobody-binding scaffolds (Legobodies)

We describe a general method that allows structure determination of small proteins by single-particle cryo-electron microscopy (cryo-EM). The method is based on the availability of a target-binding nanobody, which is then rigidly attached to two scaffolds: 1) a Fab fragment of an antibody directed against the nanobody and 2) a nanobody-binding protein A fragment fused to maltose binding protein and Fab-binding domains. The overall ensemble of ~120 kDa, called Legobody, does not perturb the nanobody–target interaction, is easily recognizable in EM images due to its unique shape, and facilitates particle alignment in cryo-EM image processing. The utility of the method is demonstrated for the KDEL receptor, a 23-kDa membrane protein, resulting in a map at 3.2-Å overall resolution with density sufficient for de novo model building, and for the 22-kDa receptor-binding domain (RBD) of SARS-CoV-2 spike protein, resulting in a map at 3.6-Å resolution that allows analysis of the binding interface to the nanobody. The Legobody approach thus overcomes the current size limitations of cryo-EM analysis.

59 BASIC BIOLOGICAL SCIENCES↗

A DEEP, WIDE-FIELD, AND PANCHROMATIC VIEW OF 47 Tuc AND THE SMC WITH HST: OBSERVATIONS AND DATA ANALYSIS METHODS

In Hubble Space Telescope (HST) Cycle 17, we imaged the well-known globular star cluster 47 Tucanae for 121 orbits using the Wide Field Channel of the Advanced Camera for Surveys (ACS) and both the UV/visible (UVIS) and IR channels of the newly installed Wide Field Camera 3 (WFC3) instrument (GO-11677, PI: H. Richer). This unique data set was obtained to address many scientific questions that demand a very deep, panchromatic, and panoramic view of the cluster's stellar populations. In total, the program obtained over 0.75 Ms of imaging exposure time with the three HST cameras, over a time span of 9 months in 2010. The primary ACS field was imaged in the two broadband filters F606W and F814W, at 13 orientations, for all 121 orbits. The parallel WFC3 imaging provides a panchromatic (0.4-1.7 {mu}m) and contiguous imaging swath over a 250 Degree-Sign azimuthal range at impact radii of 6.5-17.9 pc in 47 Tuc. This imaging totals over 60 arcmin{sup 2} in area and utilizes the F390W and F606W broadband filters on WFC3/UVIS and the F110W and F160W broadband filters on WFC3/IR. In this paper, we describe the observational design of the new survey and one of the methods used to analyze all of the imaging data. This analysis combines over 700 full-frame images taken with the three HST cameras into a handful of ultra-deep, well-sampled combined images in each of the six filters. We discuss in detail the methods used to calculate accurate transformations that provide optimal alignment of the input images, the methods used to perform sky background offsets in the input stack and the flagging of deviant pixels, and the balance reached between the input-pixel drop size onto an output supersampled pixel grid. Careful photometric, morphological, and astrometric measurements are performed on the stacks using iterative PSF-fitting techniques, and reveal unprecedented color-magnitude diagrams of the cluster extending to >30th magnitude in the optical, 29th magnitude in the UV, and 27th magnitude in the IR. The data set provides a characterization of the complete stellar populations of 47 Tuc, extending from the faintest hydrogen-burning dwarfs through the main-sequence and giant branches down to very cool white dwarf remnants in the cluster. The imaging also provides the deepest probe of the stellar populations of the background Small Magellanic Cloud galaxy, resolving low-mass main-sequence dwarfs with M {approx}< 0.2 M{sub Sun }.

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DETECTING ACTIVE GALACTIC NUCLEI USING MULTI-FILTER IMAGING DATA. II. INCORPORATING ARTIFICIAL NEURAL NETWORKS

This is the second paper of the series Detecting Active Galactic Nuclei Using Multi-filter Imaging Data. In this paper we review shapelets, an image manipulation algorithm, which we employ to adjust the point-spread function (PSF) of galaxy images. This technique is used to ensure the image in each filter has the same and sharpest PSF, which is the preferred condition for detecting AGNs using multi-filter imaging data as we demonstrated in Paper I of this series. We apply shapelets on Canada-France-Hawaii Telescope Legacy Survey Wide Survey ugriz images. Photometric parameters such as effective radii, integrated fluxes within certain radii, and color gradients are measured on the shapelets-reconstructed images. These parameters are used by artificial neural networks (ANNs) which yield: photometric redshift with an rms of 0.026 and a regression R-value of 0.92; galaxy morphological types with an uncertainty less than 2 T types for z ≤ 0.1; and identification of galaxies as AGNs with 70% confidence, star-forming/starburst (SF/SB) galaxies with 90% confidence, and passive galaxies with 70% confidence for z ≤ 0.1. The incorporation of ANNs provides a more reliable technique for identifying AGN or SF/SB candidates, which could be very useful for large-scale multi-filter optical surveys that also include a modest set of spectroscopic data sufficient to train neural networks.

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Improving the precision of astrometry for space debris

The data reduction method for optical space debris observations has many similarities with the one adopted for surveying near-Earth objects; however, due to several specific issues, the image degradation is particularly critical, which makes it difficult to obtain precise astrometry. An automatic image reconstruction method was developed to improve the astrometry precision for space debris, based on the mathematical morphology operator. Variable structural elements along multiple directions are adopted for image transformation, and then all the resultant images are stacked to obtain a final result. To investigate its efficiency, trial observations are made with Global Positioning System satellites and the astrometry accuracy improvement is obtained by comparison with the reference positions. The results of our experiments indicate that the influence of degradation in astrometric CCD images is reduced, and the position accuracy of both objects and stellar stars is improved distinctly. Our technique will contribute significantly to optical data reduction and high-order precision astrometry for space debris.

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unWISE: Unblurred coadds of the WISE imaging

The Wide-field Infrared Survey Explorer (WISE) satellite observed the full sky in four mid-infrared bands in the 2.8-28 μm range. The primary mission was completed in 2010. The WISE team has done a superb job of producing a series of high-quality, well-documented, complete data releases in a timely manner. However, the 'Atlas Image' coadds that are part of the recent AllWISE and previous data releases were intentionally blurred. Convolving the images by the point-spread function while coadding results in 'matched-filtered' images that are close to optimal for detecting isolated point sources. But these matched-filtered images are sub-optimal or inappropriate for other purposes. For example, we are photometering the WISE images at the locations of sources detected in the Sloan Digital Sky Survey through forward modeling, and this blurring decreases the available signal-to-noise by effectively broadening the point-spread function. This paper presents a new set of coadds of the WISE images that have not been blurred. These images retain the intrinsic resolution of the data and are appropriate for photometry preserving the available signal-to-noise. Users should be cautioned, however, that the W3- and W4-band coadds contain artifacts around large, bright structures (large galaxies, dusty nebulae, etc.); eliminating these artifacts is the subject of ongoing work. These new coadds, and the code used to produce them, are publicly available at http://unwise.me.

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Late-stage galaxy mergers in cosmos to z ∼ 1

The role of major mergers in galaxy and black hole formation is not well-constrained. To help address this, we develop an automated method to identify late-stage galaxy mergers before coalescence of the galactic cores. The resulting sample of mergers is distinct from those obtained using pair-finding and morphological indicators. Our method relies on median-filtering of high-resolution images to distinguish two concentrated galaxy nuclei at small separations. This method does not rely on low surface brightness features to identify mergers, and is therefore reliable to high redshift. Using mock images, we derive statistical contamination and incompleteness corrections for the fraction of late-stage mergers. The mock images show that our method returns an uncontaminated (<10%) sample of mergers with projected separations between 2.2 and 8 kpc out to z∼1. We apply our new method to a magnitude-limited (m{sub FW} {sub 814}<23) sample of 44,164 galaxies from the COSMOS HST/ACS catalog. Using a mass-complete sample with logM{sub ∗}/M{sub ⊙}>10.6 and 0.25<z⩽1.00, we find ∼5% of systems are late-stage mergers. Correcting for incompleteness and contamination, the fractional merger rate increases strongly with redshift as r{sub merge}∝(1+z){sup 3.8±0.9}, in agreement both with earlier studies and with dark matter halo merger rates. Separating the sample into star-forming and quiescent galaxies shows that the merger rate for star-forming galaxies increases strongly with redshift, (1+z){sup 4.5±1.3}, while the merger rate for quiescent galaxies is consistent with no evolution, (1+z){sup 1.1±1.2}. The merger rate also becomes steeper with decreasing stellar mass. Limiting our sample to galaxies with spectroscopic redshifts from zCOSMOS, we find that the star formation rates and X-ray selected active galactic nucleus (AGN) activity in likely late-stage mergers are higher by factors of ∼2 relative to those of a control sample. Combining our sample with more widely separated pairs, we find that 8±5% of star formation and 20±8% of AGN activity are triggered by close encounters (<143 kpc) or mergers, providing additional evidence that major mergers are not the only channels for star formation and black hole growth.

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Difference image analysis of defocused observations with CSTAR

The Chinese Small Telescope ARray carried out high-cadence time-series observations of 27 square degrees centered on the South Celestial Pole during the Antarctic winter seasons of 2008–2010. Aperture photometry of the 2008 and 2010 i-band images resulted in the discovery of over 200 variable stars. Yearly servicing left the array defocused for the 2009 winter season, during which the system also suffered from intermittent frosting and power failures. Despite these technical issues, nearly 800,000 useful images were obtained using g, r, and clear filters. We developed a combination of difference imaging and aperture photometry to compensate for the highly crowded, blended, and defocused frames. We present details of this approach, which may be useful for the analysis of time-series data from other small-aperture telescopes regardless of their image quality. Using this approach, we were able to recover 68 previously known variables and detected variability in 37 additional objects. We also have determined the observing statistics for Dome A during the 2009 winter season; we find the extinction due to clouds to be less than 0.1 and 0.4 mag for 40% and 63% of the dark time, respectively.

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THE DIFFERENCE IMAGING PIPELINE FOR THE TRANSIENT SEARCH IN THE DARK ENERGY SURVEY

We describe the operation and performance of the difference imaging pipeline (DiffImg) used to detect transients in deep images from the Dark Energy Survey Supernova program (DES-SN) in its first observing season from 2013 August through 2014 February. DES-SN is a search for transients in which ten 3 deg{sup 2} fields are repeatedly observed in the g, r, i, z passbands with a cadence of about 1 week. The observing strategy has been optimized to measure high-quality light curves and redshifts for thousands of Type Ia supernovae (SNe Ia) with the goal of measuring dark energy parameters. The essential DiffImg functions are to align each search image to a deep reference image, do a pixel-by-pixel subtraction, and then examine the subtracted image for significant positive detections of point-source objects. The vast majority of detections are subtraction artifacts, but after selection requirements and image filtering with an automated scanning program, there are ∼130 detections per deg{sup 2} per observation in each band, of which only ∼25% are artifacts. Of the ∼7500 transients discovered by DES-SN in its first observing season, each requiring a detection on at least two separate nights, Monte Carlo (MC) simulations predict that 27% are expected to be SNe Ia or core-collapse SNe. Another ∼30% of the transients are artifacts in which a small number of observations satisfy the selection criteria for a single-epoch detection. Spectroscopic analysis shows that most of the remaining transients are AGNs and variable stars. Fake SNe Ia are overlaid onto the images to rigorously evaluate detection efficiencies and to understand the DiffImg performance. The DiffImg efficiency measured with fake SNe agrees well with expectations from a MC simulation that uses analytical calculations of the fluxes and their uncertainties. In our 8 “shallow” fields with single-epoch 50% completeness depth ∼23.5, the SN Ia efficiency falls to 1/2 at redshift z ≈ 0.7; in our 2 “deep” fields with mag-depth ∼24.5, the efficiency falls to 1/2 at z ≈ 1.1. A remaining performance issue is that the measured fluxes have additional scatter (beyond Poisson fluctuations) that increases with the host galaxy surface brightness at the transient location. This bright-galaxy issue has minimal impact on the SNe Ia program, but it may lower the efficiency for finding fainter transients on bright galaxies.

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IMAGING THE ALGOL TRIPLE SYSTEM IN THE H BAND WITH THE CHARA INTERFEROMETER

Algol ({beta} Per) is an extensively studied hierarchical triple system whose inner pair is a prototype semi-detached binary with mass transfer occurring from the sub-giant secondary to the main-sequence primary. We present here the results of our Algol observations made between 2006 and 2010 at the CHARA interferometer with the Michigan Infrared Combiner in the H-band. The use of four telescopes with long baselines allows us to achieve better than 0.5 mas resolution and to unambiguously resolve the three stars. The inner and outer orbital elements, as well as the angular sizes and mass ratios for the three components, are determined independently from previous studies. We report a significantly improved orbit for the inner stellar pair with the consequence of a 15% change in the primary mass compared with previous studies. We also determine the mutual inclination of the orbits to be much closer to perpendicularity than previously established. State-of-the-art image reconstruction algorithms are used to image the full triple system. In particular an image sequence of 55 distinct phases of the inner pair orbit is reconstructed, clearly showing the Roche-lobe-filling secondary revolving around the primary, with several epochs corresponding to the primary and secondary eclipses.

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SPECKLE NOISE SUBTRACTION AND SUPPRESSION WITH ADAPTIVE OPTICS CORONAGRAPHIC IMAGING

Future ground-based direct imaging of exoplanets depends critically on high-contrast coronagraph and wave-front manipulation. A coronagraph is designed to remove most of the unaberrated starlight. Because of the wave-front error, which is inherit from the atmospheric turbulence from ground observations, a coronagraph cannot deliver its theoretical performance, and speckle noise will limit the high-contrast imaging performance. Recently, extreme adaptive optics, which can deliver an extremely high Strehl ratio, is being developed for such a challenging mission. In this publication, we show that barely taking a long-exposure image does not provide much gain for coronagraphic imaging with adaptive optics. We further discuss a speckle subtraction and suppression technique that fully takes advantage of the high contrast provided by the coronagraph, as well as the wave front corrected by the adaptive optics. This technique works well for coronagraphic imaging with conventional adaptive optics with a moderate Strehl ratio, as well as for extreme adaptive optics with a high Strehl ratio. We show how to substrate and suppress speckle noise efficiently up to the third order, which is critical for future ground-based high-contrast imaging. Numerical simulations are conducted to fully demonstrate this technique.

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HIGH-RESOLUTION IMAGING OF THE ATLBS REGIONS: THE RADIO SOURCE COUNTS

The Australia Telescope Low-brightness Survey (ATLBS) regions have been mosaic imaged at a radio frequency of 1.4 GHz with 6'' angular resolution and 72 {mu}Jy beam{sup -1} rms noise. The images (centered at R.A. 00{sup h}35{sup m}00{sup s}, decl. -67 Degree-Sign 00'00'' and R.A. 00{sup h}59{sup m}17{sup s}, decl. -67 Degree-Sign 00'00'', J2000 epoch) cover 8.42 deg{sup 2} sky area and have no artifacts or imaging errors above the image thermal noise. Multi-resolution radio and optical r-band images (made using the 4 m CTIO Blanco telescope) were used to recognize multi-component sources and prepare a source list; the detection threshold was 0.38 mJy in a low-resolution radio image made with beam FWHM of 50''. Radio source counts in the flux density range 0.4-8.7 mJy are estimated, with corrections applied for noise bias, effective area correction, and resolution bias. The resolution bias is mitigated using low-resolution radio images, while effects of source confusion are removed by using high-resolution images for identifying blended sources. Below 1 mJy the ATLBS counts are systematically lower than the previous estimates. Showing no evidence for an upturn down to 0.4 mJy, they do not require any changes in the radio source population down to the limit of the survey. The work suggests that automated image analysis for counts may be dependent on the ability of the imaging to reproduce connecting emission with low surface brightness and on the ability of the algorithm to recognize sources, which may require that source finding algorithms effectively work with multi-resolution and multi-wavelength data. The work underscores the importance of using source lists-as opposed to component lists-and correcting for the noise bias in order to precisely estimate counts close to the image noise and determine the upturn at sub-mJy flux density.

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POINT-SPREAD FUNCTIONS FOR THE EXTREME-ULTRAVIOLET CHANNELS OF SDO/AIA TELESCOPES

We present the stray-light point-spread functions (PSFs) and their inverses we characterized for the Atmospheric Imaging Assembly (AIA) EUV telescopes on board the Solar Dynamics Observatory (SDO) spacecraft. The inverse kernels are approximate inverses under convolution. Convolving the original Level 1 images with them produces images with improved stray-light characteristics. We demonstrate the usefulness of these PSFs by applying them to two specific cases: photometry and differential emission measure (DEM) analysis. The PSFs consist of a narrow Gaussian core, a diffraction component, and a diffuse component represented by the sum of a Gaussian-truncated Lorentzian and a shoulder Gaussian. We determined the diffraction term using the measured geometry of the diffraction pattern identified in flare images and the theoretically computed intensities of the principal maxima of the first few diffraction orders. To determine the diffuse component, we fitted its parameterized model using iterative forward-modeling of the lunar interior in the SDO/AIA images from the 2011 March 4 lunar transit. We find that deconvolution significantly improves the contrast in dark features such as miniature coronal holes, though the effect was marginal in bright features. On a percentage-scattering basis, the PSFs for SDO/AIA are better by a factor of two than that of the EUV telescope on board the Transition Region And Coronal Explorer mission. A preliminary analysis suggests that deconvolution alone does not affect DEM analysis of small coronal loop segments with suitable background subtraction. We include the derived PSFs and their inverses as supplementary digital materials.

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UTILIZATION OF THE WAVEFRONT SENSOR AND SHORT-EXPOSURE IMAGES FOR SIMULTANEOUS ESTIMATION OF QUASI-STATIC ABERRATION AND EXOPLANET INTENSITY

Heretofore, the literature on exoplanet detection with coronagraphic telescope systems has paid little attention to the information content of short exposures and methods of utilizing the measurements of adaptive optics wavefront sensors. This paper provides a framework for the incorporation of the wavefront sensor measurements in the context of observing modes in which the science camera takes millisecond exposures. In this formulation, the wavefront sensor measurements provide a means to jointly estimate the static speckle and the planetary signal. The ability to estimate planetary intensities in as little as a few seconds has the potential to greatly improve the efficiency of exoplanet search surveys. For simplicity, the mathematical development assumes a simple optical system with an idealized Lyot coronagraph. Unlike currently used methods, in which increasing the observation time beyond a certain threshold is useless, this method produces estimates whose error covariances decrease more quickly than inversely proportional to the observation time. This is due to the fact that the estimates of the quasi-static aberrations are informed by a new random (but approximately known) wavefront every millisecond. The method can be extended to include angular (due to diurnal field rotation) and spectral diversity. Numerical experiments are performed with wavefront data from the AEOS Adaptive Optics System sensing at 850 nm. These experiments assume a science camera wavelength {lambda} of 1.1 {mu}, that the measured wavefronts are exact, and a Gaussian approximation of shot-noise. The effects of detector read-out noise and other issues are left to future investigations. A number of static aberrations are introduced, including one with a spatial frequency exactly corresponding the planet location, which was at a distance of Almost-Equal-To 3{lambda}/D from the star. Using only 4 s of simulated observation time, a planetary intensity, of Almost-Equal-To 1 photon ms{sup -1}, and a stellar intensity of Almost-Equal-To 10{sup 5} photons ms{sup -1} (contrast ratio 10{sup 5}), the short-exposure estimation method recovers the amplitudes' static aberrations with 1% accuracy, and the planet brightness with 20% accuracy.

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FOMALHAUT b: INDEPENDENT ANALYSIS OF THE HUBBLE SPACE TELESCOPE PUBLIC ARCHIVE DATA

The nature and even the existence of a putative planet-mass companion ({sup F}omalhaut b{sup )} to Fomalhaut has been debated since 2008. In the present paper, we reanalyze the multi-epoch ACS/STIS/WFC3 Hubble Space Telescope (HST) optical/near-infrared images on which the discovery and some other claims were based. We confirm that the HST images do reveal an object in orbit around Fomalhaut, but the detailed results from our analysis differ in some ways from previous discussions. In particular, we do not confirm flux variability over a two-year interval at 0.6 {mu}m wavelength and we detect Fomalhaut b for the first time at the short wavelength of 0.43 {mu}m. We find that the HST image of Fomalhaut b at 0.8 {mu}m may be extended beyond the point-spread function. We cannot determine from our astrometry if Fomalhaut b will cross or not the dust ring. The optical through mid-infrared spectral energy distribution (SED) of Fomalhaut b cannot be explained as due to direct or scattered radiation from a massive planet. We consider two models to explain the SED: (1) a large circumplanetary disk around an unseen planet and (2) the aftermath of a collision during the past 50-150 yr of two Kuiper-Belt-like objects of radii {approx}50 km.

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Searching for pulsars using image pattern recognition

In the modern era of big data, many fields of astronomy are generating huge volumes of data, the analysis of which can sometimes be the limiting factor in research. Fortunately, computer scientists have developed powerful data-mining techniques that can be applied to various fields. In this paper, we present a novel artificial intelligence (AI) program that identifies pulsars from recent surveys by using image pattern recognition with deep neural nets—the PICS (Pulsar Image-based Classification System) AI. The AI mimics human experts and distinguishes pulsars from noise and interference by looking for patterns from candidate plots. Different from other pulsar selection programs that search for expected patterns, the PICS AI is taught the salient features of different pulsars from a set of human-labeled candidates through machine learning. The training candidates are collected from the Pulsar Arecibo L-band Feed Array (PALFA) survey. The information from each pulsar candidate is synthesized in four diagnostic plots, which consist of image data with up to thousands of pixels. The AI takes these data from each candidate as its input and uses thousands of such candidates to train its ∼9000 neurons. The deep neural networks in this AI system grant it superior ability to recognize various types of pulsars as well as their harmonic signals. The trained AI's performance has been validated with a large set of candidates from a different pulsar survey, the Green Bank North Celestial Cap survey. In this completely independent test, the PICS ranked 264 out of 277 pulsar-related candidates, including all 56 previously known pulsars and 208 of their harmonics, in the top 961 (1%) of 90,008 test candidates, missing only 13 harmonics. The first non-pulsar candidate appears at rank 187, following 45 pulsars and 141 harmonics. In other words, 100% of the pulsars were ranked in the top 1% of all candidates, while 80% were ranked higher than any noise or interference. The performance of this system can be improved over time as more training data are accumulated. This AI system has been integrated into the PALFA survey pipeline and has discovered six new pulsars to date.

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Reducing systematic error in weak lensing cluster surveys

Weak lensing provides an important route toward collecting samples of clusters of galaxies selected by mass. Subtle systematic errors in image reduction can compromise the power of this technique. We use the B-mode signal to quantify this systematic error and to test methods for reducing this error. We show that two procedures are efficient in suppressing systematic error in the B-mode: (1) refinement of the mosaic CCD warping procedure to conform to absolute celestial coordinates and (2) truncation of the smoothing procedure on a scale of 10'. Application of these procedures reduces the systematic error to 20% of its original amplitude. We provide an analytic expression for the distribution of the highest peaks in noise maps that can be used to estimate the fraction of false peaks in the weak-lensing κ-signal-to-noise ratio (S/N) maps as a function of the detection threshold. Based on this analysis, we select a threshold S/N = 4.56 for identifying an uncontaminated set of weak-lensing peaks in two test fields covering a total area of ∼3 deg{sup 2}. Taken together these fields contain seven peaks above the threshold. Among these, six are probable systems of galaxies and one is a superposition. We confirm the reliability of these peaks with dense redshift surveys, X-ray, and imaging observations. The systematic error reduction procedures we apply are general and can be applied to future large-area weak-lensing surveys. Our high-peak analysis suggests that with an S/N threshold of 4.5, there should be only 2.7 spurious weak-lensing peaks even in an area of 1000 deg{sup 2}, where we expect ∼2000 peaks based on our Subaru fields.

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SpArcFiRe: Scalable automated detection of spiral galaxy arm segments

Given an approximately centered image of a spiral galaxy, we describe an entirely automated method that finds, centers, and sizes the galaxy (possibly masking nearby stars and other objects if necessary in order to isolate the galaxy itself) and then automatically extracts structural information about the spiral arms. For each arm segment found, we list the pixels in that segment, allowing image analysis on a per-arm-segment basis. We also perform a least-squares fit of a logarithmic spiral arc to the pixels in that segment, giving per-arc parameters, such as the pitch angle, arm segment length, location, etc. The algorithm takes about one minute per galaxies, and can easily be scaled using parallelism. We have run it on all ∼644,000 Sloan objects that are larger than 40 pixels across and classified as 'galaxies'. We find a very good correlation between our quantitative description of a spiral structure and the qualitative description provided by Galaxy Zoo humans. Our objective, quantitative measures of structure demonstrate the difficulty in defining exactly what constitutes a spiral 'arm', leading us to prefer the term 'arm segment'. We find that pitch angle often varies significantly segment-to-segment in a single spiral galaxy, making it difficult to define the pitch angle for a single galaxy. We demonstrate how our new database of arm segments can be queried to find galaxies satisfying specific quantitative visual criteria. For example, even though our code does not explicitly find rings, a good surrogate is to look for galaxies having one long, low-pitch-angle arm—which is how our code views ring galaxies. SpArcFiRe is available at http://sparcfire.ics.uci.edu.

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Imaging an event horizon: mitigation of scattering toward Sagittarius A*

The image of the emission surrounding the black hole in the center of the Milky Way is predicted to exhibit the imprint of general relativistic (GR) effects, including the existence of a shadow feature and a photon ring of diameter ∼50 μas. Structure on these scales can be resolved by millimeter-wavelength very long baseline interferometry. However, strong-field GR features of interest will be blurred at λ ≥ 1.3 mm due to scattering by interstellar electrons. The scattering properties are well understood over most of the relevant range of baseline lengths, suggesting that the scattering may be (mostly) invertible. We simulate observations of a model image of Sgr A* and demonstrate that the effects of scattering can indeed be mitigated by correcting the visibilities before reconstructing the image. This technique is also applicable to Sgr A* at longer wavelengths.

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