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Register a new userThe Umbrella software suite for automated asteroid detection
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We present the Umbrella software suite for asteroid detection, validation, identification and reporting. The current core of Umbrella is an open-source modular library, called Umbrella2, that includes algorithms and interfaces for all steps of the processing pipeline, including a novel detection algorithm for faint trails. Building on the library, we have also implemented a detection pipeline accessible both as a desktop program (ViaNearby) and via a web server (Webrella), which we have successfully used in near real-time data reduction of a few asteroid surveys on the Wide Field Camera of the Isaac Newton Telescope. In this paper we describe the library, focusing on the interfaces and algorithms available, and we present the results obtained with the desktop version on a set of well-curated fields used by the EURONEAR project as an asteroid detection benchmark.
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Near Earth Asteroids (NEAs) are discovered daily, mainly by few major surveys, nevertheless many of them remain unobserved for years, even decades. Even so, there is room for new discoveries, including those submitted by smaller projects and amateur astronomers. Besides the well-known surveys that have their own automated system of asteroid detection, there are only a few software solutions designed to help amateurs and mini-surveys in NEAs discovery. Some of these obtain their results based on the blink method in which a set of reduced images are shown one after another and the astronomer has to visually detect real moving objects in a series of images. This technique becomes harder with the increase in size of the CCD cameras. Aiming to replace manual detection we propose an automated pipeline prototype for asteroids detection, written in Python under Linux, which calls some 3rd party astrophysics libraries.
We describe the development of automated emission line detection software for the Fiber Multi-Object Spectrograph (FMOS), which is a near-infrared spectrograph fed by $400$ fibers from the $0.2$ deg$^2$ prime focus field of view of the Subaru Telescope. The software, FIELD (FMOS software for Image-based Emission Line Detection), is developed and tested mainly for the FastSound survey, which is targeting H$alpha$ emitting galaxies at $z sim 1.3$ to measure the redshift space distortion as a test of general relativity beyond $z sim 1$. The basic algorithm is to calculate the line signal-to-noise ratio ($S/N$) along the wavelength direction, given by a 2-D convolution of the spectral image and a detection kernel representing a typical emission line profile. A unique feature of FMOS is its use of OH airglow suppression masks, requiring the use of flat-field images to suppress noise around the mask regions. Bad pixels on the detectors and pixels affected by cosmic-rays are efficiently removed by using the information obtained from the FMOS analysis pipeline. We limit the range of acceptable line-shape parameters for the detected candidates to further improve the reliability of line detection. The final performance of line detection is tested using a subset of the FastSound data; the false detection rate of spurious objects is examined by using inverted frames obtained by exchanging object and sky frames. The false detection rate is $< 1$% at $S/N > 5$, allowing an efficient and objective emission line search for FMOS data at the line flux level of $gtrsim 1.0 times 10^{-16}$[erg/cm$^2$/s].
Asteroids detection is a very important research field that received increased attention in the last couple of decades. Some major surveys have their own dedicated people, equipment and detection applications, so they are discovering Near Earth Asteroids (NEAs) daily. The interest in asteroids is not limited to those major surveys, it is shared by amateurs and mini-surveys too. A couple of them are using the few existent software solutions, most of which are developed by amateurs. The rest obtain their results in a visual manner: they blink a sequence of reduced images of the same field, taken at a specific time interval, and they try to detect a real moving object in the resulting animation. Such a technique becomes harder with the increase in size of the CCD cameras. Aiming to replace manual detection, we propose an automated blink technique for asteroids detection.
Recent advances in scientific instruments have resulted in dramatic increase in the volumes and velocities of data being generated in every-day laboratories. Scanning electron microscopy is one such example where technological advancements are now overwhelming scientists with critical data for montaging, alignment, and image segmentation -- key practices for many scientific domains, including, for example, neuroscience, where they are used to derive the anatomical relationships of the brain. These instruments now necessitate equally advanced computing resources and techniques to realize their full potential. Here we present a fast out-of-focus detection algorithm for electron microscopy images collected serially and demonstrate that it can be used to provide near-real time quality control for neurology research. Our technique, Multi-scale Histologic Feature Detection, adapts classical computer vision techniques and is based on detecting various fine-grained histologic features. We further exploit the inherent parallelism in the technique by employing GPGPU primitives in order to accelerate characterization. Tests are performed that demonstrate near-real-time detection of out-of-focus conditions. We deploy these capabilities as a funcX function and show that it can be applied as data are collected using an automated pipeline . We discuss extensions that enable scaling out to support multi-beam microscopes and integration with existing focus systems for purposes of implementing auto-focus.
We present an overview of recent developments in the tmLQCD software suite. We summarise the features of the code, including actions and operators implemented. In particular, we discuss the optimisation efforts for modern architectures using the Blue Gene/Q system as an example.
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