No Arabic abstract
ImageJ is a graphical user interface (GUI) driven, public domain, Java-based, software package for general image processing traditionally used mainly in life sciences fields. The image processing capabilities of ImageJ are useful and extendable to other scientific fields. Here we present AstroImageJ (AIJ), which provides an astronomy specific image display environment and tools for astronomy specific image calibration and data reduction. Although AIJ maintains the general purpose image processing capabilities of ImageJ, AIJ is streamlined for time-series differential photometry, light curve detrending and fitting, and light curve plotting, especially for applications requiring ultra-precise light curves (e.g., exoplanet transits). AIJ reads and writes standard FITS files, as well as other common image formats, provides FITS header viewing and editing, and is World Coordinate System (WCS) aware, including an automated interface to the astrometry.net web portal for plate solving images. AIJ provides research grade image calibration and analysis tools with a GUI driven approach, and easily installed cross-platform compatibility. It enables new users, even at the level of undergraduate student, high school student, or amateur astronomer, to quickly start processing, modeling, and plotting astronomical image data with one tightly integrated software package.
ImageJ is a graphical user interface (GUI) driven, public domain, Java-based, software package for general image processing traditionally used mainly in life sciences fields. The image processing capabilities of ImageJ are useful and extendable to other scientific fields. Here we present AstroImageJ (AIJ), which provides an astronomy specific image display environment and tools for astronomy specific image calibration and data reduction. Although AIJ maintains the general purpose image processing capabilities of ImageJ, AIJ is streamlined for time-series differential photometry, light curve detrending and fitting, and light curve plotting, especially for applications requiring ultra-precise light curves (e.g., exoplanet transits). AIJ reads and writes standard FITS files, as well as other common image formats, provides FITS header viewing and editing, and is World Coordinate System (WCS) aware, including an automated interface to the astrometry.net web portal for plate solving images. AIJ provides research grade image calibration and analysis tools with a GUI driven approach, and easily installed cross-platform compatibility. It enables new users, even at the level of undergraduate student, high school student, or amateur astronomer, to quickly start processing, modeling, and plotting astronomical image data with one tightly integrated software package.
Precise astronomical spectroscopic analyses routinely assume that individual pixels in charge-coupled devices (CCDs) have uniform sensitivity to photons. Intra-pixel sensitivity (IPS) variations may already cause small systematic errors in, for example, studies of extra-solar planets via stellar radial velocities and cosmological variability in fundamental constants via quasar spectroscopy, but future experiments requiring velocity precisions approaching ~1 cm/s will be more strongly affected. Laser frequency combs have been shown to provide highly precise wavelength calibration for astronomical spectrographs, but here we show that they can also be used to measure IPS variations in astronomical CCDs in situ. We successfully tested a laser frequency comb system on the Ultra-High Resolution Facility spectrograph at the Anglo-Australian Telescope. By modelling the 2-dimensional comb signal recorded in a single CCD exposure, we find that the average IPS deviates by <8 per cent if it is assumed to vary symmetrically about the pixel centre. We also demonstrate that series of comb exposures with absolutely known offsets between them can yield tighter constraints on symmetric IPS variations from ~100 pixels. We discuss measurement of asymmetric IPS variations and absolute wavelength calibration of astronomical spectrographs and CCDs using frequency combs.
Fireball light curves can give insight into the meteor ablation process which can be used to improve fireball trajectory and mass modelling. To this aim, the Desert Fireball Network (DFN) is developing a low cost add-on fireball radiometer to supplement existing observatories. The objective is to collect radiometric data on fireballs across a wide spectral range at 1000 samples per second with sensitivity to a large dynamic range (mV $in$[-4, -20]), whilst maintaining a low cost. Here we discuss the current prototype design and first light results.
Within the last years, the classification of variable stars with Machine Learning has become a mainstream area of research. Recently, visualization of time series is attracting more attention in data science as a tool to visually help scientists to recognize significant patterns in complex dynamics. Within the Machine Learning literature, dictionary-based methods have been widely used to encode relevant parts of image data. These methods intrinsically assign a degree of importance to patches in pictures, according to their contribution in the image reconstruction. Inspired by dictionary-based techniques, we present an approach that naturally provides the visualization of salient parts in astronomical light curves, making the analogy between image patches and relevant pieces in time series. Our approach encodes the most meaningful patterns such that we can approximately reconstruct light curves by just using the encoded information. We test our method in light curves from the OGLE-III and StarLight databases. Our results show that the proposed model delivers an automatic and intuitive visualization of relevant light curve parts, such as local peaks and drops in magnitude.
Exoplanets, short for `extra solar planets, are planets outside our solar system. They are objects with masses less than around 15 Jupiter-masses that orbit stars other than the Sun. They are small enough so they can not burn deuterium in their cores, yet large enough that they are not so called `dwarf planets like Pluto. To discover life elsewhere in the universe, particularly outside our own solar system, a good starting point would be to search for planets orbiting nearby Sun-like stars, since the only example of life we know of thrives on a planet we call Earth that orbits a G-type dwarf star. Furthermore, understanding the population of exoplanetary systems in the nearby solar neighbourhood allows us to understand the mechanisms that built our own solar system and gave rise to the conditions necessary for our tree of life to flourish. Signal processing is an integral part of exoplanet detection. From improving the signal-to-noise ratio of the observed data to applying advanced statistical signal processing methods, among others, to detect signals (potential planets) in the data, astronomers have tended, and continue to tend, towards signal processing in their quest of finding Earth-like planets. The following methods have been used to detect exoplanets.