Traditional cosmic ray filtering algorithms used in X-ray imaging detectors aboard space telescopes perform event reconstruction based on the properties of activated pixels above a certain energy threshold, within 3×3 or 5×5 pixel sliding windows. This approach can reject up to 98% of the cosmic ray background. However, the remaining unrejected background constitutes a significant impediment to studies of low surface brightness objects, which are especially prevalent in the high-redshift universe. The main limitation of the traditional filtering algorithms is their ignorance of the long-range contextual information present in image frames. This becomes particularly problematic when analyzing signals created by secondary particles produced during interactions of cosmic rays with body of the detector. Such signals may look identical to the energy deposition left by X-ray photons, when one considers only the properties within the small sliding window. Additional information is present, however, in the spatial and energy correlations between signals in different parts of the same frame, which can be accessed by modern machine learning (ML) techniques. In this work, we continue the development of an ML-based pipeline for cosmic ray background mitigation. Our latest method consist of two stages: first, a frame classification neural network is used to create class activation maps (CAM), localizing all events within the frame; second, after event reconstruction, a random forest classifier, using features obtained from CAMs, is used to separate X-ray and cosmic ray features. The method delivers > 40% relative improvement over traditional filtering in background rejection in standard 0.3-10 keV energy range, at the expense of only a small (< 2%) level of lost X-ray signal. Our method also provides a convenient way to tune the cosmic ray rejection threshold to adapt to a user’s specific scientific needs.
Bringing artificial intelligence (AI) alongside next-generation X-ray imaging detectors, including CCDs and DEPFET sensors, enhances their sensitivity to achieve many of the flagship science cases targeted by future X-ray observatories, based upon low surface brightness and high redshift sources. Machine learning algorithms operating on the raw frame-level data provide enhanced identification of background vs. astrophysical X-ray events, by considering all of the signals in the context within which they appear within each frame. We have developed prototype machine learning algorithms to identify valid X-ray and cosmic-ray induced background events, trained and tested upon a suite of realistic end-to-end simulations that trace the interaction of cosmic ray particles and their secondaries through the spacecraft and detector. These algorithms demonstrate that AI can reduce the unrejected instrumental background by up to 41.5 per cent compared with traditional filtering methods. Alongside AI algorithms to reduce the instrumental background, next-generation event reconstruction methods, based upon fitting physically-motivated Gaussian models of the charge clouds produced by events within the detector, promise increased accuracy and spectral resolution of the lowest energy photon events.
The sensitivity of astronomical x-ray detectors is limited by the instrumental background. The background is especially important when observing low surface brightness sources that are critical for many of the science cases targeted by future x-ray observatories, including Athena and future U.S.-led flagship or probe-class x-ray missions. Above 2 keV, the background is dominated by signals induced by cosmic rays interacting with the spacecraft and detector. We develop novel machine learning algorithms to identify events in next-generation x-ray imaging detectors and to predict the probability that an event is induced by a cosmic ray vs. an astrophysical x-ray photon, enabling enhanced filtering of the cosmic ray-induced background. We find that by learning the typical correlations between the secondary events that arise from a single primary, machine learning algorithms are able to successfully identify cosmic ray-induced background events that are missed by traditional filtering methods employed on current-generation x-ray missions, reducing the unrejected background by as much as 30 percent.
KEYWORDS: Particles, Signal to noise ratio, Sensors, Camera shutters, Signal attenuation, Imaging systems, Signal detection, X-rays, Photons, X-ray imaging
The Wide Field Imager (WFI) flying on Athena will usher in the next era of studying the hot and energetic Universe. Among Athena’s ambitious science programs are observations of faint, diffuse sources limited by statistical and systematic uncertainty in the background produced by high-energy cosmic ray particles. These particles produce easily identified “cosmic-ray tracks” along with less easily identified signals produced by secondary photons or x-rays generated by particle interactions with the instrument. Such secondaries produce identical signals to the x-rays focused by the optics and cannot be filtered without also eliminating these precious photons. As part of a larger effort to estimate the level of unrejected background and mitigate its effects, we here present results from a study of background-reduction techniques that exploit the spatial correlation between cosmic-ray particle tracks and secondary events. We use Geant4 simulations to generate a realistic particle background signal, sort this into simulated WFI frames, and process those frames in a similar way to the expected flight and ground software to produce a realistic WFI observation containing only particle background. The technique under study, self-anti-coincidence (SAC), then selectively filters regions of the detector around particle tracks, turning the WFI into its own anti-coincidence detector. We show that SAC is effective at improving the systematic uncertainty for observations of faint, diffuse sources, but at the cost of statistical uncertainty due to a reduction in signal. If sufficient pixel pulse-height information is telemetered to the ground for each frame, then this technique can be applied selectively based on the science goals, providing flexibility without affecting the data quality for other science. The results presented here are relevant for any future silicon-based pixelated x-ray imaging detector and could allow the WFI and similar instruments to probe to truly faint x-ray surface brightness.
Space-based X-ray detectors are subject to significant fluxes of charged particles in orbit, notably energetic cosmic ray protons, contributing a significant background. We develop novel machine learning algorithms to detect charged particle events in next-generation X-ray CCDs and DEPFET detectors, with initial studies focusing on the Athena Wide Field Imager (WFI) DEPFET detector. We train and test a prototype convolutional neural network algorithm and find that charged particle and X-ray events are identified with a high degree of accuracy, exploiting correlations between pixels to improve performance over existing event detection algorithms. 99 per cent of frames containing a cosmic ray are identified and the neural network is able to correctly identify up to 40 per cent of the cosmic rays that are missed by current event classification criteria, showing potential to significantly reduce the instrumental background, and unlock the full scientific potential of future X-ray missions such as Athena, Lynx and AXIS.
One of the science goals of the Wide Field Imager (WFI) on ESA’s Athena X-ray observatory is to map hot gas structures in the universe, such as clusters and groups of galaxies and the intergalactic medium. These deep observations of faint diffuse sources require low background and the best possible knowledge of that background. The WFI Background Working Group is approaching this problem from a variety of directions. Here we present analysis of Geant4 simulations of cosmic ray particles interacting with the structures aboard Athena, producing signal in the WFI. We search for phenomenological correlations between these particle tracks and detected events that would otherwise be categorized as X-rays, and explore ways to exploit these correlations to flag or reject such events in ground processing. In addition to reducing the Athena WFI instrumental background, these results are applicable to understanding the particle component in any silicon-based X-ray detector in space.
The world's premier X-ray astronomical observatories, Chandra and XMM-Newton, have been operating for about 20 years. The next flagship X-ray observatory launched will be ESA's Athena mission. We discuss planned US contributions to the Athena Wide Field Imager instrument, which encompass transient source detection, background characterization and reduction, and detector electronics design and testing, in addition to scientific contributions.
The Wide Field Imager (WFI) on ESA’s Athena X-ray observatory will include the Science Products Module, a secondary CPU that can perform special processing on the science data stream. Our goal is to identify on-board processing algorithms that can reduce WFI charged particle background and improve knowledge of the background to reduce systematics. Telemetry limitations require discarding most pixels on-board, keeping just candidate X-ray events, but information in the discarded data may be helpful in identifying background events masquerading as X-ray events. We present full frame data from CCDs on-board Chandra, in high-Earth orbit, and the results of our search for phenomenological correlations between particle tracks and background events that would otherwise be categorized as X-rays. In addition to possibly reducing the Athena instrumental background, these results are applicable to understanding the particle component in any X-ray Silicon-based detector in space.
The Wide Field Imager (WFI) is one of two focal plane detector systems of ESA’s Advanced Telescope for High ENergy Astrophysics (ATHENA) X-ray observatory. The Science Products Module (SPM) will have on-board processing algorithms that will reduce the ATHENA WFI particle background level significantly by improving background rejection on board and in post-processing on the ground. To this end, we examine the full frame observations from existing X-ray telescopes to understand and characterize the physics of the particle background. In particular, we determine phenomenological correlations between high energy particle events and X-ray events to improve the rejection of particle background events. We will present our results from the Swift XRT and XMM-Newton PN full frame data analysis in this talk. We will also discuss how these results could be used to reduce the expected background in the ATHENA WFI observations by the SPM processing.
The Science Products Module (SPM), a US contribution to the Athena Wide Field Imager, is a highly capable secondary CPU that performs special processing on the science data stream. The SPM will have access to both accepted X-ray events and those that were rejected by the on-board event recognition processing. It will include two software modules. The Transient Analysis Module will perform on-board processing of the science images to identify and characterize variability of the prime target and/or detection of serendipitous transient X-ray sources in the field of view. The Background Analysis Module will perform more sophisticated flagging of potential background events as well as improved background characterization, making use of data that are not telemetered to the ground, to provide improved background maps and spectra. We present the preliminary design of the SPM hardware as well as a brief overview of the software algorithms under development.
We summarize nearly two decades of successful operation of the Chandra High Resolution Camera (HRC). The HRC is a pair of cesium–iodide (CsI) coated microchannel plate X-ray detectors launched in July, 1999, one optimized for widefield imaging (HRC-I) and a second as a readout for X-ray transmission gratings (HRC-S). We discuss the temporal evolution of the performance of the flight instrument, the impact of extended exposure to the charged particle environment of high Earth orbit, and lessons learned from nineteen years of flight operations. We also describe our investigation of new algorithms to remove more efficiently the charged particle background from the science data, as we prepare for another decade of operation.
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