AXIS is a Probe-class mission concept that will provide high-throughput, high-spatial-resolution x-ray spectral imaging, enabling transformative studies of high-energy astrophysical phenomena. To take advantage of the advanced optics and avoid photon pile-up, the AXIS focal plane requires detectors with readout rates at least 20 times faster than previous soft x-ray imaging spectrometers flying aboard missions such as Chandra and Suzaku, while retaining the low noise, excellent spectral performance, and low power requirements of those instruments. We present the design of the AXIS high-speed x-ray camera, which baselines large-format MIT Lincoln Laboratory CCDs employing low-noise pJFET output amplifiers and a single-layer polysilicon gate structure that allows fast, low-power clocking. These detectors are combined with an integrated high-speed, low-noise ASIC readout chip from Stanford University that provides better performance than conventional discrete solutions at a fraction of their power consumption and footprint. Our complementary front-end electronics concept employs state of the art digital video waveform capture and advanced signal processing to deliver low noise at high speed. We review the current performance of this technology, highlighting recent improvements on prototype devices that achieve excellent noise characteristics at the required readout rate. We present measurements of the CCD spectral response across the AXIS energy band, augmenting lab measurements with detector simulations that help us understand sources of charge loss and evaluate the quality of the CCD backside passivation technique. We show that our technology is on a path that will meet our requirements and enable AXIS to achieve world-class science.
Traditional image segmentation methods employed with X-ray imaging detectors aboard X-ray space telescopes consist of two stages: first, a low energy threshold is applied; groups of activated pixels are then classified according to their shapes and identified as valid X-ray events or rejected as being possibly induced by cosmic rays. This method is fast and removes up to 98% of the cosmic ray-induced background. However, these traditional methods fail to address two important problems: first, they struggle to recover the true energies of, and sometimes fail to detect entirely, low-energy photons (photon energies less than 0.5keV); second, they consider only the shape of the active pixel regions, ignoring the longer-range context within the image frames. This limits their sensitivity to a specific type of cosmic ray signal: ”islands” created by secondary particles produced by cosmic rays hitting the body of the telescope (the shapes of which are often indistinguishable from X-ray photon signals). Together, these limitations hinder investigations of faint, diffuse targets, such as the outskirts of galaxies and galaxy clusters, and of ”low energy” targets such as individual stars, galaxies and high redshift systems. Both limitations can, however, be addressed with machine learning (ML) models. This work is part of our effort to develop fast and efficient background reduction methods for future astronomical X-ray missions using ML methods. We highlight several significant improvements in the classification and semantic segmentation of our background filtering pipeline. Our more realistic training and test data now incorporate the effects of readout noise and charge diffusion. In the presence of charge diffusion, our model is able to obtain an 80% relative improvement in lost signal recovery compared to the traditional background reduction techniques. We identify several directions for further development of the model.
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