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ASTRONIRCAM is an infrared camera-spectrograph installed at the 2.5-meter telescope of the CMO SAI. The instrument is equipped with the HAWAII-2RG array. A bad pixels classification of the ASTRONIRCAM detector is proposed. The classification is based on histograms of the difference of consecutive non-destructive readouts of a flat field. Bad pixels are classified into 5 groups: hot (saturated on the first readout), warm (the signal accumulation rate is above the mean value by more than 5 standard deviations), cold (the rate is under the mean value by more than 5 standard deviations), dead (no signal accumulation), and inverse (having a negative signal accumulation in the first readouts). Normal pixels of the ASTRONIRCAM detector account for 99.6% of the total. We investigated the dependence between the amount of bad pixels and the number of cooldown cycles of the instrument. While hot pixels remain the same, the bad pixels of other types may migrate between groups. The number of pixels in each group stays roughly constant. We found that the mean and variance of the bad pixels amount in each group and the transitions between groups do not differ noticeably between normal or slow cooldowns.
A fraction of the XMM-Newton/EPIC FOV is obscured by the dysfunctional (i.e. bad) pixels. The fraction varies between different EPIC instruments in a given observation. These complications affect the analysis of extended X-ray sources observed with X
We present a new method of interpolation for the pixel brightness estimation in astronomical images. Our new method is simple and easily implementable. We show the comparison of this method with the widely used linear interpolation and other interpol
In the new era of very large telescopes, where data is crucial to expand scientific knowledge, we have witnessed many deep learning applications for the automatic classification of lightcurves. Recurrent neural networks (RNNs) are one of the models u
Next-generation surveys like the Legacy Survey of Space and Time (LSST) on the Vera C. Rubin Observatory will generate orders of magnitude more discoveries of transients and variable stars than previous surveys. To prepare for this data deluge, we de
The exploitation of present and future synoptic (multi-band and multi-epoch) surveys requires an extensive use of automatic methods for data processing and data interpretation. In this work, using data extracted from the Catalina Real Time Transient