Analog pile-up circuit technique using a single capacitor for the readout of Skipper-CCD detectors

With Skipper-CCD detectors it is possible to take multiple samples of the charge packet collected on each pixel. After averaging the samples, the noise can be extremely reduced allowing the exact counting of electrons per pixel. In this work we present an analog circuit that, with a minimum number of components, applies a double slope integration (DSI) and at the same time averages the multiple samples, producing at its output the pixel value with sub-electron noise. For this purpose, we introduce the technique of using the DSI integrator capacitor to add the skipper samples. An experimental verification using discrete components is presented, together with an analysis of its noise sources and limitations. After averaging 400 samples it was possible to reach a readout noise of 0.18 e- rms/pix, comparable to other available readout systems. Due to its simplicity and significant reduction of the sampling requirements, this circuit technique is of particular interest in particle experiments and cameras with a high density of Skipper-CCDs.

Nondestructive Structural Investigation of Yttria-Stabilized Zirconia Fiber Insulation Tile by Synchrotron X-ray In-Line Phase-Contrast Microtomography

Zirconia (ZrO2) aerogels show excellent insulating performance and have been widely applied as a thermal protector in furnaces, nuclear reactors, and spacecraft. The nondestructive determination of their interior microstructure is significant for evaluating their mechanical and insulating performance. In this study, we performed nondestructive structural investigation of an yttria-stabilized ZrO2 fiber insulation tile using synchrotron X-ray in-line phase-contrast microtomography at a pixel resolution of 6.5 µm. Taking advantage of the edge enhancement of phase-contrast imaging, single yttria-stabilized ZrO2 fibers were clearly distinguished; furthermore, interior aggregates were nondestructively observed at this spatial resolution. This work demonstrates the advantages and potential of synchrotron X-ray microtomography for the structural analysis of porous ceramic materials. By combining higher-brilliance synchrotron radiation sources and CCD detectors with higher spatial and temporal resolutions, we anticipate that we can further understand the relationship between aerogel microstructure and function, especially under in-service conditions at high temperatures.

Bayesian Super-Resolution of Spectroscopic Data

The resolution of spectroscopy, which delivers valuable insights and knowledge in various research fields, has sometimes been limited by the number of multi-channel detectors employed. For example, in Raman spectroscopy using charge coupled device (CCD) detectors, the resolution is limited by the number of the CCD arrays and it is difficult to achieve spectroscopic data acquisition with high resolution over a wide range. Here we describe a methodology to increase the resolution as well as signal-to-noise (S/N) ratio by applying Bayesian super-resolution in the analysis of spectroscopic data. In our present method, first the hyperparameters for the Bayesian super-resolution are determined by a virtual experiment imitating actual experimental data, and the precision of the super-resolution reconstruction is confirmed by the calculation of errors from the ideal values. For validation of the super-resolution of spectroscopic data, we applied this method to the analysis of Raman spectra. From 200 Raman spectra of a reference Si substrate with a resolution of about 0.8 cm-1, super-resolution reconstruction with resolution of 0.01 cm-1 was successfully achieved with the promised precision. From the super-resolution spectrum, the Raman scattering peak of the reference Si substrate was estimated as 520.55 (+ 0.12, -0.09) cm-1, which is comparable to the precisely determined value from previous works. The present methodology can be applied to various kinds of spectroscopic analysis, leading to increased precision in the analysis of spectroscopic data and the ability to detect slight differences in spectral peak positions and shapes.

Bayesian Super-Resolution of Spectroscopic Data

The resolution of spectroscopy, which delivers valuable insights and knowledge in various research fields, has sometimes been limited by the number of multi-channel detectors employed. For example, in Raman spectroscopy using charge coupled device (CCD) detectors, the resolution is limited by the number of the CCD arrays and it is difficult to achieve spectroscopic data acquisition with high resolution over a wide range. Here we describe a methodology to increase the resolution as well as signal-to-noise (S/N) ratio by applying Bayesian super-resolution in the analysis of spectroscopic data. In our present method, first the hyperparameters for the Bayesian super-resolution are determined by a virtual experiment imitating actual experimental data, and the precision of the super-resolution reconstruction is confirmed by the calculation of errors from the ideal values. For validation of the super-resolution of spectroscopic data, we applied this method to the analysis of Raman spectra. From 200 Raman spectra of a reference Si substrate with a resolution of about 0.8 cm-1, super-resolution reconstruction with resolution of 0.01 cm-1 was successfully achieved with the promised precision. From the super-resolution spectrum, the Raman scattering peak of the reference Si substrate was estimated as 520.55 (+0.12, -0.09) cm-1, which is comparable to the precisely determined value from previous works. The present methodology can be applied to various kinds of spectroscopic analysis, leading to increased precision in the analysis of spectroscopic data and the ability to detect slight differences in spectral peak positions and shapes.

Bayesian Super-Resolution of Spectroscopic Data

The resolution of spectroscopy, which delivers valuable insights and knowledge in various research fields, has sometimes been limited by the number of multi-channel detectors employed. For example, in Raman spectroscopy using charge coupled device (CCD) detectors, the resolution is limited by the number of the CCD arrays and it is difficult to achieve spectroscopic data acquisition with high resolution over a wide range. Here we describe a methodology to increase the resolution as well as signal-to-noise (S/N) ratio by applying Bayesian super-resolution in the analysis of spectroscopic data. In our present method, first the hyperparameters for the Bayesian super-resolution are determined by a virtual experiment imitating actual experimental data, and the precision of the super-resolution reconstruction is confirmed by the calculation of errors from the ideal values. For validation of the super-resolution of spectroscopic data, we applied this method to the analysis of Raman spectra. From 200 Raman spectra of a reference Si substrate with a resolution of about 0.8 cm− 1, super-resolution reconstruction with resolution of 0.01 cm− 1 was successfully achieved with the promised precision. From the super-resolution spectrum, the Raman scattering peak of the reference Si substrate was estimated as 520.55 (+ 0.12, -0.09) cm− 1, which is comparable to the precisely determined value from previous works. The present methodology can be applied to various kinds of spectroscopic analysis, leading to increased precision in the analysis of spectroscopic data and the ability to detect slight differences in spectral peak positions and shapes.

Exploring nine simultaneously occurring transients on April 12th 1950

Nine point sources appeared within half an hour on a region within $$\sim $$ ∼ 10 arcmin of a red-sensitive photographic plate taken in April 1950 as part of the historic Palomar Sky Survey. All nine sources are absent on both previous and later photographic images, and absent in modern surveys with CCD detectors which go several magnitudes deeper. We present deep CCD images with the 10.4-m Gran Telescopio Canarias, reaching brightness $$r \sim 26$$ r ∼ 26 mag, that reveal possible optical counterparts, although these counterparts could equally well be just chance projections. The incidence of transients in the investigated photographic plate is far higher than expected from known detection rates of optical counterparts to e.g. flaring dwarf stars, Fast Radio Bursts, Gamma Ray Bursts or microlensing events. One possible explanation is that the plates have been subjected to an unknown type of contamination producing mainly point sources with of varying intensities along with some mechanism of concentration within a radius of $$\sim $$ ∼ 10 arcmin on the plate. If contamination as an explanation can be fully excluded, another possibility is fast (t $$<0.5$$ < 0.5 s) solar reflections from objects near geosynchronous orbits. An alternative route to confirm the latter scenario is by looking for images from the First Palomar Sky Survey where multiple transients follow a line.

Accurate Determination of Conversion Gains of SVOM VT CCDs Based on a Signal-Dependent Charge-Sharing Mechanism

The signal-variance method and the photon transfer curve method are the most valuable tools for calculating the conversion gains of charge-coupled device (CCD) detectors. This paper describes the phenomena that arise in the conversion gain measurements of space multi-band variable object monitor (SVOM) visible telescope (VT) CCDs, where the results of the signal-variance method increase with the image gray level, and the results of the photon transfer curve method appear with nonlinearity, which is caused by the signal-dependent charge sharing mechanism of back-illuminated CCDs. A numerical simulation model based on random variables was adopted to analyze the influence of the mechanism on the gain determination. The model simulates all the signals and noise in the flat field image, including the photon signal and photon-shot noise, readout noise, fixed pattern noise, and the signal-dependent charge-sharing signal, and it demonstrated agreement with the experimental data. Then, we proposed a quadratic polynomial curve-fitting formula for the photon transfer curve, and we quantitatively analyzed the relationship between the fitting coefficients and the gain, the signal-dependent charge sharing coefficient, and the full well capacity using the control variable method. Finally, the formula was used to accurately determine the conversion gains of SVOM VT CCDs.