اشترك بالحزمة الذهبية واحصل على وصول غير محدود شمرا أكاديميا
تسجيل مستخدم جديدDifference image analysis: Automatic kernel design using information criteria
154
0
0.0
(
0
)
اسأل ChatGPT حول البحث
ﻻ يوجد ملخص باللغة العربية
We present a selection of methods for automatically constructing an optimal kernel model for difference image analysis which require very few external parameters to control the kernel design. Each method consists of two components; namely, a kernel design algorithm to generate a set of candidate kernel models, and a model selection criterion to select the simplest kernel model from the candidate models that provides a sufficiently good fit to the target image. We restricted our attention to the case of solving for a spatially-invariant convolution kernel composed of delta basis functions, and we considered 19 different kernel solution methods including six employing kernel regularisation. We tested these kernel solution methods by performing a comprehensive set of image simulations and investigating how their performance in terms of model error, fit quality, and photometric accuracy depends on the properties of the reference and target images. We find that the irregular kernel design algorithm employing unregularised delta basis functions, combined with either the Akaike or Takeuchi information criterion, is the best kernel solution method in terms of photometric accuracy. Our results are validated by tests performed on two independent sets of real data. Finally, we provide some important recommendations for software implementations of difference image analysis.
قيم البحث
اقرأ أيضاً
The accumulation of aberrations along the optical path in a telescope produces distortions and speckles in the resulting images, limiting the performance of cameras at high angular resolution. It is important to achieve the highest possible sensitivi
ty to faint sources such as planets, using both hardware and data analysis software. While analytic methods are efficient, real systems are better-modelled numerically, but such models with many parameters can be hard to understand, optimize and apply. Automatic differentiation software developed for machine learning now makes calculating derivatives with respect to aberrations straightforward for arbitrary optical systems. We apply this powerful new tool to enhance high-angular-resolution astronomical imaging. Self-calibrating observables such as the closure phase or bispectrum have been widely used in optical and radio astronomy to mitigate optical aberrations and achieve high-fidelity imagery. Kernel phases are a generalization of closure phases in the limit of small phase errors. Using automatic differentiation, we reproduce existing kernel phase theory within this framework and demonstrate an extension to the Lyot coronagraph, finding self-calibrating combinations of speckles which are resistant to phase noise, but only in the very high-wavefront-quality regime. As an illustrative example, we reanalyze Palomar adaptive optics observations of the binary alpha Ophiuchi, finding consistency between the new pipeline and the existing standard. We present a new Python package morphine that incorporates these ideas, with an interface similar to the popular package poppy, for optical simulation with automatic differentiation. These methods may be useful for designing improved astronomical optical systems by gradient descent.
We present a comparison of several Difference Image Analysis (DIA) techniques, in combination with Machine Learning (ML) algorithms, applied to the identification of optical transients associated with gravitational wave events. Each technique is asse
ssed based on the scoring metrics of Precision, Recall, and their harmonic mean F1, measured on the DIA results as standalone techniques, and also in the results after the application of ML algorithms, on transient source injections over simulated and real data. This simulations cover a wide range of instrumental configurations, as well as a variety of scenarios of observation conditions, by exploring a multi dimensional set of relevant parameters, allowing us to extract general conclusions related to the identification of transient astrophysical events. The newest subtraction techniques, and particularly the methodology published in Zackay et al. (2016) are implemented in an Open Source Python package, named properimage, suitable for many other astronomical image analyses. This together with the ML libraries we describe, provides an effective transient detection software pipeline. Here we study the effects of the different ML techniques, and the relative feature importances for classification of transient candidates, and propose an optimal combined strategy. This constitutes the basic elements of pipelines that could be applied in searches of electromagnetic counterparts to GW sources.
Context: Understanding the source of systematic errors in photometry is essential for their calibration. Aims: We investigate how photometry performed on difference images can be influenced by errors in the photometric scale factor. Methods: We explo
re the equations for difference image analysis (DIA) and we derive an expression describing how errors in the difference flux, the photometric scale factor and the reference flux are propagated to the object photometry. Results: We find that the error in the photometric scale factor is important, and while a few studies have shown that it can be at a significant level, it is currently neglected by the vast majority of photometric surveys employing DIA. Conclusions: Minimising the error in the photometric scale factor, or compensating for it in a post-calibration model, is crucial for reducing the systematic errors in DIA photometry.
The Chinese Small Telescope ARray (CSTAR) carried out high-cadence time-series observations of 27 square degrees centered on the South Celestial Pole during the Antarctic winter seasons of 2008, 2009 and 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 & 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.
Harmoni is the ELTs first light visible and near-infrared integral field spectrograph. It will provide four different spatial scales, ranging from coarse spaxels of 60 x 30 mas best suited for seeing limited observations, to 4 mas spaxels that Nyquis
t sample the diffraction limited point spread function of the ELT at near-infrared wavelengths. Each spaxel scale may be combined with eleven spectral settings, that provide a range of spectral resolving powers from R 3500 to R 20000 and instantaneous wavelength coverage spanning the 0.47 - 2.45 {mu}m wavelength range of the instrument. The consortium consists of several institutes in Europe under leadership of Oxford University. Harmoni is starting its Final Design Phase after a Preliminary Design Phase in November, 2017. The CRAL has the responsibility of the Integral Field Unit design linking the Preoptics to the 4 Spectrographs. It is composed of a field splitter associated with a relay system and an image slicer that create from a rectangular Field of View a very long (540mm) output slit for each spectrograph. In this paper, the preliminary design and performances of Harmoni Image Slicer will be presented including image quality, pupil distortion and slit geometry. It has been designed by CRAL for Harmoni PDR in November, 2017. Special emphases will be put on straylight analysis and slice diffraction. The optimisation of the manufacturing and slit geometry will also be reported.
سجل دخول لتتمكن من نشر تعليقات
التعليقات
جاري جلب التعليقات
سجل دخول لتتمكن من متابعة معايير البحث التي قمت باختيارها
