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Register a new userNEARBY Platform for Detecting Asteroids in Astronomical Images Using Cloud-based Containerized Applications
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The continuing monitoring and surveying of the nearby space to detect Near Earth Objects (NEOs) and Near Earth Asteroids (NEAs) are essential because of the threats that this kind of objects impose on the future of our planet. We need more computational resources and advanced algorithms to deal with the exponential growth of the digital cameras performances and to be able to process (in near real-time) data coming from large surveys. This paper presents a software platform called NEARBY that supports automated detection of moving sources (asteroids) among stars from astronomical images. The detection procedure is based on the classic blink detection and, after that, the system supports visual analysis techniques to validate the moving sources, assisted by static and dynamical presentations.
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The survey of the nearby space and continuous monitoring of the Near Earth Objects (NEOs) and especially Near Earth Asteroids (NEAs) are essential for the future of our planet and should represent a priority for our solar system research and nearby space exploration. More computing power and sophisticated digital tracking algorithms are needed to cope with the larger astronomy imaging cameras dedicated for survey telescopes. The paper presents the NEARBY platform that aims to experiment new algorithms for automatic image reduction, detection and validation of moving objects in astronomical surveys, specifically NEAs. The NEARBY platform has been developed and experimented through a collaborative research work between the Technical University of Cluj-Napoca (UTCN) and the University of Craiova, Romania, using observing infrastructure of the Instituto de Astrofisica de Canarias (IAC) and Isaac Newton Group (ING), La Palma, Spain. The NEARBY platform has been developed and deployed on the UTCNs cloud infrastructure and the acquired images are processed remotely by the astronomers who transfer it from ING through the web interface of the NEARBY platform. The paper analyzes and highlights the main aspects of the NEARBY platform development, and the results and conclusions on the EURONEAR surveys.
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.
Many objects on the sky exhibit a centrosymmetric polarization pattern, particularly in cases involving single scattering around a central source. Utilizing a novel liquid crystal device (the ``theta cell) that transforms the coordinate system of linear polarization in an image plane from Cartesian to polar, the observation of centrosymmetric polarization patterns can be improved: instead of measuring Stokes Q and U on the sky, one only needs to measure Stokes Q in the new instrument coordinate system. This reduces the effective exposure time by a factor of two and simplifies the polarization modulator design. According to the manufacturers specifications and to measurements in the lab, the liquid crystal device can be applied in the visible and NIR wavelength range. Astronomical science cases for a``radial polarimeter include exoplanet detection, imaging of circumstellar disks, reflection nebulae and light echos, characterization of planetary atmospheres and diagnostics of the solar K-corona. The first astronomical instrument that utilizes a theta cell for radial polarimetry is the S5T (Small Synoptic Second Solar Spectrum Telescope), which accurately measures scattering polarization signals near the limb of the sun. These observations are crucial for understanding the nature and origin of weak, turbulent magnetic fields in the solar photosphere and elsewhere in the universe. A ``radial polarimeter observing a slightly defocused point source performs one-shot full linear polarimetry. With a theta cell in a pupil plane, a beams linear polarization properties (e.g. for calibration purposes) can be fully controlled through pupil masking.
With the advent of future big-data surveys, automated tools for unsupervised discovery are becoming ever more necessary. In this work, we explore the ability of deep generative networks for detecting outliers in astronomical imaging datasets. The main advantage of such generative models is that they are able to learn complex representations directly from the pixel space. Therefore, these methods enable us to look for subtle morphological deviations which are typically missed by more traditional moment-based approaches. We use a generative model to learn a representation of expected data defined by the training set and then look for deviations from the learned representation by looking for the best reconstruction of a given object. In this first proof-of-concept work, we apply our method to two different test cases. We first show that from a set of simulated galaxies, we are able to detect $sim90%$ of merging galaxies if we train our network only with a sample of isolated ones. We then explore how the presented approach can be used to compare observations and hydrodynamic simulations by identifying observed galaxies not well represented in the models.
We present a new method of interpolation for the pixel brightness estimation in astronomical images. Our new method is simple and easily implementable. We show the comparison of this method with the widely used linear interpolation and other interpolation algorithms using one thousand astronomical images obtained from the Sloan Digital Sky Survey. The comparison shows that our method improves bad pixels brightness estimation with four times lower mean error than the presently most popular linear interpolation and has a better performance than any other examined method. The presented idea is flexible and can be also applied to presently used and future interpolation methods. The proposed method is especially useful for large sky surveys image reduction but can be also applied to single image correction.
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