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Most image restoration methods in astronomy rely upon probabilistic tools that infer the best solution for a deconvolution problem. They achieve good performances when the point spread function (PSF) is spatially invariant in the image plane.
However, this condition is not always satisfied in real optical systems. We propose a new method for the restoration of images affected by static and anisotropic aberrations using Deep Neural Networks that can be directly applied to sky images.
The network is trained using simulated sky images corresponding to the T80-S Telescope optical model, a 80-cm survey imager at Cerro Tololo (Chile), which are synthesized using a Zernike polynomial representation of the optical system.
Once trained, the network can be used directly on sky images, outputting a corrected version of the image that has a constant and known PSF across its field of view. The method is to be tested on the T80-S Telescope.
We present the method and results on synthetic data.
Convolutional neural networks (CNNs) have been used for a wide range of applications in astronomy, including for the restoration of degraded images using a spatially invariant point spread function (PSF) across the field of view. Most existing development techniques use a single PSF in the deconvolution process, which is unrealistic when spatially variable PSFs are present in real observation conditions. Such conditions are simulated in this work to yield more realistic data samples. We propose a method that uses a simulated spatially variable PSF for the T80-South (T80-S) telescope, an 80-cm survey imager at Cerro Tololo (Chile). The synthetic data use real parameters from the detector noise and atmospheric seeing to recreate the T80-S observational conditions for the CNN training. The method is tested on real astronomical data from the T80-S telescope. We present the simulation and training methods, the results from real T80-S image CNN prediction, and a comparison with space observatory Gaia. A CNN can fix optical aberrations, which include image distortion, PSF size and profile, and the field position variation while preserving the source's flux. The proposed restoration approach can be applied to other optical systems and to post-process adaptive optics static residual aberrations in large-diameter telescopes.
Aims. We analyze the emission in the 0.3-30 GeV energy range of gamma-ray bursts detected with the Fermi Gamma-ray Space Telescope. We concentrate on bursts that were previously only detected with the Gamma-Ray Burst Monitor in the keV energy range. These bursts will then be compared to the bursts that were individually detected with the Large Area Telescope at higher energies.
Methods. To estimate the emission of faint GRBs we used nonstandard analysis methods and sum over many GRBs to find an average signal that is significantly above background level. We used a subsample of 99 GRBs listed in the Burst Catalog from the first two years of observation.
Results. Although most are not individually detectable, the bursts not detected by the Large Area Telescope on average emit a significant flux in the energy range from 0.3 GeV to 30 GeV, but their cumulative energy fluence is only 8% of that of all GRBs. Likewise, the GeV-to-MeV flux ratio is less and the GeV-band spectra are softer. We confirm that the GeV-band emission lasts much longer than the emission found in the keV energy range. The average allsky energy flux from GRBs in the GeV band is 6.4 x 10(-4) erg cm(-2) yr(-1) or only similar to 4% of the energy flux of cosmic rays above the ankle at 10(18.6) eV.
We perform a statistical clustering analysis of upper main-sequence stars in the Large Magellanic Cloud (LMC) using data from the Visible and Infrared Survey Telescope for Astronomy survey of the Magellanic Clouds. We map over 2500 young stellar structures at 15 significance levels across similar to 120 square degrees centred on the LMC. The structures have sizes ranging from a few parsecs to over 1 kpc. We find that the young structures follow power-law size and mass distributions. From the perimeter-area relation, we derive a perimeter-area dimension of 1.44 +/- 0.20. From the mass-size relation and the size distribution, we derive two-dimensional fractal dimensions of 1.50 +/- 0.10 and 1.61 +/- 0.20, respectively. We find that the surface density distribution is well represented by a lognormal distribution. We apply the Larson relation to estimate the velocity dispersions and crossing times of these structures. Our results indicate that the fractal nature of the young stellar structures has been inherited from the gas clouds from which they form and that this architecture is generated by supersonic turbulence. Our results also suggest that star formation in the LMC is scale-free from 10 to 700 pc.