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Until now, spatially resolved Raman Spectroscopy has required to scan a sample under investigation in a time-consuming step-by-step procedure. Here, we present a technique that allows the capture of an entire Raman image with only one single exposure. The Raman scattering arising from the sample was collected with a fiber-coupled high-performance astronomy spectrograph. The probe head consisting of an array of 20 x 20 multimode fibers was linked to the camera port of a microscope. To demonstrate the high potential of this new concept, Raman images of reference samples were recorded. Entire chemical maps were received without the need for a scanning procedure.
A long-standing and profound problem in astronomy is the difficulty in obtaining deep near-infrared observations due to the extreme brightness and variability of the night sky at these wavelengths. A solution to this problem is crucial if we are to obtain the deepest possible observations of the early Universe, as redshifted starlight from distant galaxies appears at these wavelengths. The atmospheric emission between 1,000 and 1,800 nm arises almost entirely from a forest of extremely bright, very narrow hydroxyl emission lines that varies on timescales of minutes. The astronomical community has long envisaged the prospect of selectively removing these lines, while retaining high throughput between them. Here we demonstrate such a filter for the first time, presenting results from the first on-sky tests. Its use on current 8 m telescopes and future 30 m telescopes will open up many new research avenues in the years to come.
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.