Pub Date : 2023-12-01DOI: 10.1088/1538-3873/ad0a72
Ramya M Anche, Ewan Douglas, Kian Milani, Jaren Ashcraft, Maxwell A. Millar-Blanchaer, John H Debes, Julien Milli, Justin Hom
The Nancy Grace Roman Space Telescope Coronagraph Instrument will enable the polarimetric imaging of debris disks and inner dust belts in the optical and near-infrared wavelengths, in addition to the high-contrast polarimetric imaging and spectroscopy of exoplanets. The Coronagraph uses two Wollaston prisms to produce four orthogonally polarized images and is expected to measure the polarization fraction with measurement errors <3% per spatial resolution element. To simulate the polarization observations through the Hybrid Lyot Coronagraph (HLC) and Shaped Pupil Coronagraph (SPC), we model disk scattering, the coronagraphic point-response function, detector noise, speckles, jitter, and instrumental polarization and calculate the Stokes parameters. To illustrate the potential for discovery and a better understanding of known systems with both the HLC and SPC modes, we model the debris disks around Epsilon Eridani and HR 4796A, respectively. For Epsilon Eridani, using astrosilicates with 0.37 ± 0.01 as the peak input polarization fraction in one resolution element, we recover the peak disk polarization fraction of 0.33 ± 0.01. Similarly, for HR 4796A, for a peak input polarization fraction of 0.92 ± 0.01, we obtain the peak output polarization fraction as 0.80 ± 0.03. The Coronagraph design meets the required precision, and forward modeling is needed to accurately estimate the polarization fraction.
{"title":"Simulation of High-contrast Polarimetric Observations of Debris Disks with the Roman Coronagraph Instrument","authors":"Ramya M Anche, Ewan Douglas, Kian Milani, Jaren Ashcraft, Maxwell A. Millar-Blanchaer, John H Debes, Julien Milli, Justin Hom","doi":"10.1088/1538-3873/ad0a72","DOIUrl":"https://doi.org/10.1088/1538-3873/ad0a72","url":null,"abstract":"The Nancy Grace Roman Space Telescope Coronagraph Instrument will enable the polarimetric imaging of debris disks and inner dust belts in the optical and near-infrared wavelengths, in addition to the high-contrast polarimetric imaging and spectroscopy of exoplanets. The Coronagraph uses two Wollaston prisms to produce four orthogonally polarized images and is expected to measure the polarization fraction with measurement errors <3% per spatial resolution element. To simulate the polarization observations through the Hybrid Lyot Coronagraph (HLC) and Shaped Pupil Coronagraph (SPC), we model disk scattering, the coronagraphic point-response function, detector noise, speckles, jitter, and instrumental polarization and calculate the Stokes parameters. To illustrate the potential for discovery and a better understanding of known systems with both the HLC and SPC modes, we model the debris disks around Epsilon Eridani and HR 4796A, respectively. For Epsilon Eridani, using astrosilicates with 0.37 ± 0.01 as the peak input polarization fraction in one resolution element, we recover the peak disk polarization fraction of 0.33 ± 0.01. Similarly, for HR 4796A, for a peak input polarization fraction of 0.92 ± 0.01, we obtain the peak output polarization fraction as 0.80 ± 0.03. The Coronagraph design meets the required precision, and forward modeling is needed to accurately estimate the polarization fraction.","PeriodicalId":20820,"journal":{"name":"Publications of the Astronomical Society of the Pacific","volume":"11 1","pages":""},"PeriodicalIF":3.5,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138684292","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-11-29DOI: 10.1088/1538-3873/ad0a04
Ma Long, Du Jiangbin, Zhao Jiayao, Wang Xuhao, Peng Yangfan
The existing astronomical image restoration and superresolution reconstruction methods have problems such as low efficiency and poor results when dealing with images possessing large fields of view. Furthermore, these methods typically only handle fixed-size images and require step-by-step processing, which is inconvenient. In this paper, a neural network called Res&RecNet is proposed for the restoration and superresolution reconstruction of astronomical images with large fields of view for direct imaging instruments. This network performs feature extraction, feature correction, and progressive generation to achieve image restoration and superresolution reconstruction. The network is constructed using fully convolutional layers, allowing it to handle images of any size. The network can be trained using small samples and can perform image restoration and superresolution reconstruction in an end-to-end manner, resulting in high efficiency. Experimental results show that the network is highly effective in terms of processing astronomical images with complex scenes, generating image restoration results that improve the peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM) by 4.69 (dB)/0.073 and superresolution reconstruction results that improve the PSNR and SSIM by 1.97 (dB)/0.077 over those of the best existing algorithms, respectively.
{"title":"Large-field Astronomical Image Restoration and Superresolution Reconstruction using Deep Learning","authors":"Ma Long, Du Jiangbin, Zhao Jiayao, Wang Xuhao, Peng Yangfan","doi":"10.1088/1538-3873/ad0a04","DOIUrl":"https://doi.org/10.1088/1538-3873/ad0a04","url":null,"abstract":"The existing astronomical image restoration and superresolution reconstruction methods have problems such as low efficiency and poor results when dealing with images possessing large fields of view. Furthermore, these methods typically only handle fixed-size images and require step-by-step processing, which is inconvenient. In this paper, a neural network called Res&RecNet is proposed for the restoration and superresolution reconstruction of astronomical images with large fields of view for direct imaging instruments. This network performs feature extraction, feature correction, and progressive generation to achieve image restoration and superresolution reconstruction. The network is constructed using fully convolutional layers, allowing it to handle images of any size. The network can be trained using small samples and can perform image restoration and superresolution reconstruction in an end-to-end manner, resulting in high efficiency. Experimental results show that the network is highly effective in terms of processing astronomical images with complex scenes, generating image restoration results that improve the peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM) by 4.69 (dB)/0.073 and superresolution reconstruction results that improve the PSNR and SSIM by 1.97 (dB)/0.077 over those of the best existing algorithms, respectively.","PeriodicalId":20820,"journal":{"name":"Publications of the Astronomical Society of the Pacific","volume":"71 1","pages":""},"PeriodicalIF":3.5,"publicationDate":"2023-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138684131","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-11-01DOI: 10.1088/1538-3873/acff86
Ning Li, Na Wang, Zhiyong Liu, Lei Yang
Abstract Radio astronomical observations put stringent requirements on the tracking and pointing accuracy of radio telescope antennas. High inertia, low stiffness, underdamped, and multi-resonant frequencies of a large aperture radio telescope’s antenna make the high-accuracy control difficult. It is not easy to satisfy control performance using only conventional PID controllers. A low-order Active Disturbance Rejection-based double-loop controller for large antenna is designed in this paper and tested on the Green Bank Telescope model. First, the first-order Linear Active Disturbance Rejecting Controller (LADRC) cascading a first-order low-pass filter and a notch filter is designed for the antenna’s velocity loop to achieve the dual-objective optimal velocity tracking. Second, the position loop controller is designed to realize the antenna’s position-tracking control by combining the PD controller and a low-pass filter. Further optimization of the position-loop controller helps improve the dynamic performance of the system. The simulation results indicate that the response curves of the proposed PD-LADRC control are smother than those of the Quantitative Feedback Theory (QFT) based controller; the settling time of the PD-LADRC system is 10.1 s and reduces by about 8.2 s than that of the QFT. While using a better position controller reduces settling time to 5 s. The PD-LADRC system also has better wind-disturbance rejection; the worst disturbance response reduces at the gearbox by 68.3% and 60% at the dish, and the recovery time reduces by more than 15 s than the QFT-based controller. In addition, besides easier parameter tuning, the proposed PD-LADRC has better robustness to systematic parameter perturbations and minor tracking error rms in position tracking.
{"title":"Active Disturbance Rejection-based Double-loop Control Design for Large Antenna's Servo System","authors":"Ning Li, Na Wang, Zhiyong Liu, Lei Yang","doi":"10.1088/1538-3873/acff86","DOIUrl":"https://doi.org/10.1088/1538-3873/acff86","url":null,"abstract":"Abstract Radio astronomical observations put stringent requirements on the tracking and pointing accuracy of radio telescope antennas. High inertia, low stiffness, underdamped, and multi-resonant frequencies of a large aperture radio telescope’s antenna make the high-accuracy control difficult. It is not easy to satisfy control performance using only conventional PID controllers. A low-order Active Disturbance Rejection-based double-loop controller for large antenna is designed in this paper and tested on the Green Bank Telescope model. First, the first-order Linear Active Disturbance Rejecting Controller (LADRC) cascading a first-order low-pass filter and a notch filter is designed for the antenna’s velocity loop to achieve the dual-objective optimal velocity tracking. Second, the position loop controller is designed to realize the antenna’s position-tracking control by combining the PD controller and a low-pass filter. Further optimization of the position-loop controller helps improve the dynamic performance of the system. The simulation results indicate that the response curves of the proposed PD-LADRC control are smother than those of the Quantitative Feedback Theory (QFT) based controller; the settling time of the PD-LADRC system is 10.1 s and reduces by about 8.2 s than that of the QFT. While using a better position controller reduces settling time to 5 s. The PD-LADRC system also has better wind-disturbance rejection; the worst disturbance response reduces at the gearbox by 68.3% and 60% at the dish, and the recovery time reduces by more than 15 s than the QFT-based controller. In addition, besides easier parameter tuning, the proposed PD-LADRC has better robustness to systematic parameter perturbations and minor tracking error rms in position tracking.","PeriodicalId":20820,"journal":{"name":"Publications of the Astronomical Society of the Pacific","volume":"34 7","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135372172","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-11-01DOI: 10.1088/1538-3873/ad0444
L. D. Anderson, B. Liu, Dana. S. Balser, T. M. Bania, L. M. Haffner, Dylan J. Linville, Matteo Luisi, Trey V. Wenger
Abstract The ideal spectral averaging method depends on one’s science goals and the available information about one’s data. Including low-quality data in the average can decrease the signal-to-noise ratio (S/N), which may necessitate an optimization method or a consideration of different weighting schemes. Here, we explore a variety of spectral averaging methods. We investigate the use of three weighting schemes during averaging: weighting by the signal divided by the variance (“intensity-noise weighting”), weighting by the inverse of the variance (“noise weighting”), and uniform weighting. Whereas for intensity-noise weighting the S/N is maximized when all spectra are averaged, for noise and uniform weighting we find that averaging the 35%–45% of spectra with the highest S/N results in the highest S/N average spectrum. With this intensity cutoff, the average spectrum with noise or uniform weighting has ∼95% of the intensity of the spectrum created from intensity-noise weighting. We apply our spectral averaging methods to GBT Diffuse Ionized Gas hydrogen radio recombination line data to determine the ionic abundance ratio, y + , and discuss future applications of the methodology.
{"title":"Methods for Averaging Spectral Line Data","authors":"L. D. Anderson, B. Liu, Dana. S. Balser, T. M. Bania, L. M. Haffner, Dylan J. Linville, Matteo Luisi, Trey V. Wenger","doi":"10.1088/1538-3873/ad0444","DOIUrl":"https://doi.org/10.1088/1538-3873/ad0444","url":null,"abstract":"Abstract The ideal spectral averaging method depends on one’s science goals and the available information about one’s data. Including low-quality data in the average can decrease the signal-to-noise ratio (S/N), which may necessitate an optimization method or a consideration of different weighting schemes. Here, we explore a variety of spectral averaging methods. We investigate the use of three weighting schemes during averaging: weighting by the signal divided by the variance (“intensity-noise weighting”), weighting by the inverse of the variance (“noise weighting”), and uniform weighting. Whereas for intensity-noise weighting the S/N is maximized when all spectra are averaged, for noise and uniform weighting we find that averaging the 35%–45% of spectra with the highest S/N results in the highest S/N average spectrum. With this intensity cutoff, the average spectrum with noise or uniform weighting has ∼95% of the intensity of the spectrum created from intensity-noise weighting. We apply our spectral averaging methods to GBT Diffuse Ionized Gas hydrogen radio recombination line data to determine the ionic abundance ratio, y + , and discuss future applications of the methodology.","PeriodicalId":20820,"journal":{"name":"Publications of the Astronomical Society of the Pacific","volume":"314 5","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135565579","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-11-01DOI: 10.1088/1538-3873/acfdcb
Alison P. Wong, Barnaby R. M. Norris, Vincent Deo, Peter G. Tuthill, Richard Scalzo, David Sweeney, Kyohoon Ahn, Julien Lozi, Sébastien Vievard, Olivier Guyon
Abstract The pyramid wave front sensor (PyWFS) has become increasingly popular to use in adaptive optics (AO) systems due to its high sensitivity. The main drawback of the PyWFS is that it is inherently nonlinear, which means that classic linear wave front reconstruction techniques face a significant reduction in performance at high wave front errors, particularly when the pyramid is unmodulated. In this paper, we consider the potential use of neural networks (NNs) to replace the widely used matrix vector multiplication (MVM) control. We aim to test the hypothesis that the NN's ability to model nonlinearities will give it a distinct advantage over MVM control. We compare the performance of a MVM linear reconstructor against a dense NN, using daytime data acquired on the Subaru Coronagraphic Extreme Adaptive Optics system (SCExAO) instrument. In a first set of experiments, we produce wavefronts generated from 14 Zernike modes and the PyWFS responses at different modulation radii (25, 50, 75, and 100 mas). We find that the NN allows for a far more precise wave front reconstruction at all modulations, with differences in performance increasing in the regime where the PyWFS nonlinearity becomes significant. In a second set of experiments, we generate a data set of atmosphere-like wavefronts, and confirm that the NN outperforms the linear reconstructor. The SCExAO real-time computer software is used as baseline for the latter. These results suggest that NNs are well positioned to improve upon linear reconstructors and stand to bring about a leap forward in AO performance in the near future.
{"title":"Nonlinear Wave Front Reconstruction from a Pyramid Sensor using Neural Networks","authors":"Alison P. Wong, Barnaby R. M. Norris, Vincent Deo, Peter G. Tuthill, Richard Scalzo, David Sweeney, Kyohoon Ahn, Julien Lozi, Sébastien Vievard, Olivier Guyon","doi":"10.1088/1538-3873/acfdcb","DOIUrl":"https://doi.org/10.1088/1538-3873/acfdcb","url":null,"abstract":"Abstract The pyramid wave front sensor (PyWFS) has become increasingly popular to use in adaptive optics (AO) systems due to its high sensitivity. The main drawback of the PyWFS is that it is inherently nonlinear, which means that classic linear wave front reconstruction techniques face a significant reduction in performance at high wave front errors, particularly when the pyramid is unmodulated. In this paper, we consider the potential use of neural networks (NNs) to replace the widely used matrix vector multiplication (MVM) control. We aim to test the hypothesis that the NN's ability to model nonlinearities will give it a distinct advantage over MVM control. We compare the performance of a MVM linear reconstructor against a dense NN, using daytime data acquired on the Subaru Coronagraphic Extreme Adaptive Optics system (SCExAO) instrument. In a first set of experiments, we produce wavefronts generated from 14 Zernike modes and the PyWFS responses at different modulation radii (25, 50, 75, and 100 mas). We find that the NN allows for a far more precise wave front reconstruction at all modulations, with differences in performance increasing in the regime where the PyWFS nonlinearity becomes significant. In a second set of experiments, we generate a data set of atmosphere-like wavefronts, and confirm that the NN outperforms the linear reconstructor. The SCExAO real-time computer software is used as baseline for the latter. These results suggest that NNs are well positioned to improve upon linear reconstructors and stand to bring about a leap forward in AO performance in the near future.","PeriodicalId":20820,"journal":{"name":"Publications of the Astronomical Society of the Pacific","volume":"48 5","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135371451","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-11-01DOI: 10.1088/1538-3873/acff89
John L. Tonry
Abstract The Asteroid Terrestrial-impact Last Alert System (ATLAS) observes the visible sky every night in search of dangerous asteroids. With four (soon five) sites ATLAS is facing new challenges for scheduling observations and linking detections to identify moving asteroids. Flexibility in coping with diverse observation sites and times of detections that can be linked is critical, as is optimization of observing time for coverage versus depth. We present new algorithms to fit orbits rapidly to sky-plane observations, and to test and link sets of detections to find the ones which belong to moving objects. The PUMA algorithm for fitting orbits to angular positions on the sky executes in about a millisecond, orders of magnitude faster than the methods currently in use by the community, without sacrifice in accuracy. The PUMA software should be generally useful to anyone who needs to test many sets of detections for consistency with a real orbit. The PUMALINK algorithm to find linkages among sets of detections has similarities to other approaches, notably HelioLinC, but it functions well at asteroid ranges of a small fraction of an astronomical unit. PUMALINK is fast enough to test 10 million possible tracklets against one another in a half hour of computer time. Candidate linkages are checked by the PUMA library to test that the detections correspond to a real orbit, even at close range, and the false alarm rate is manageable. Sky surveys that produce large numbers of detections from large numbers of exposures may find the PUMALINK software helpful. We present the results of tests of PUMALINK on three data sets which illustrate PUMALINK ’s effectiveness and economy: 2 weeks of all ATLAS detections over the sky, 2 weeks of special ATLAS opposition observations with long exposure time, and 2 weeks of simulated LSST asteroid observations. Detection probabilities of linkages must be traded against false alarm rate, but a representative choice for PUMALINK might be 90% detection probability for real objects while keeping the false alarm rate below 10% for a 100:1 population of false:real. Although optimization of the tradeoffs between detection probability, execution time, and false alarm rate is application specific and beyond the scope of this paper, we provide guidance on methods to distinguish false alarms from correct linkages of real objects.
{"title":"Linking Sky-plane Observations of Moving Objects","authors":"John L. Tonry","doi":"10.1088/1538-3873/acff89","DOIUrl":"https://doi.org/10.1088/1538-3873/acff89","url":null,"abstract":"Abstract The Asteroid Terrestrial-impact Last Alert System (ATLAS) observes the visible sky every night in search of dangerous asteroids. With four (soon five) sites ATLAS is facing new challenges for scheduling observations and linking detections to identify moving asteroids. Flexibility in coping with diverse observation sites and times of detections that can be linked is critical, as is optimization of observing time for coverage versus depth. We present new algorithms to fit orbits rapidly to sky-plane observations, and to test and link sets of detections to find the ones which belong to moving objects. The PUMA algorithm for fitting orbits to angular positions on the sky executes in about a millisecond, orders of magnitude faster than the methods currently in use by the community, without sacrifice in accuracy. The PUMA software should be generally useful to anyone who needs to test many sets of detections for consistency with a real orbit. The PUMALINK algorithm to find linkages among sets of detections has similarities to other approaches, notably HelioLinC, but it functions well at asteroid ranges of a small fraction of an astronomical unit. PUMALINK is fast enough to test 10 million possible tracklets against one another in a half hour of computer time. Candidate linkages are checked by the PUMA library to test that the detections correspond to a real orbit, even at close range, and the false alarm rate is manageable. Sky surveys that produce large numbers of detections from large numbers of exposures may find the PUMALINK software helpful. We present the results of tests of PUMALINK on three data sets which illustrate PUMALINK ’s effectiveness and economy: 2 weeks of all ATLAS detections over the sky, 2 weeks of special ATLAS opposition observations with long exposure time, and 2 weeks of simulated LSST asteroid observations. Detection probabilities of linkages must be traded against false alarm rate, but a representative choice for PUMALINK might be 90% detection probability for real objects while keeping the false alarm rate below 10% for a 100:1 population of false:real. Although optimization of the tradeoffs between detection probability, execution time, and false alarm rate is application specific and beyond the scope of this paper, we provide guidance on methods to distinguish false alarms from correct linkages of real objects.","PeriodicalId":20820,"journal":{"name":"Publications of the Astronomical Society of the Pacific","volume":"66 4","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135564901","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract In recent years, tremendous progress has been made on scientific Complementary Metal Oxide Semiconductor (sCMOS) sensors, making them a promising device for future space X-ray missions. We have customized a large-format sCMOS sensor, G1516BI, dedicated for X-ray applications. In this work, a 200 nm thick aluminum layer is successfully sputtered on the surface of this sensor. This Al-coated sensor, named EP4K, shows consistent performance with the uncoated version. The readout noise of the EP4K sensor is around 2.5 e − and the dark current is less than 0.01 e − pixel −1 s −1 at −30°C. The maximum frame rate is 20 Hz in the current design. The ratio of single pixel events of the sensor is 45.0%. The energy resolution can reach 153.2 eV at 4.51 keV and 174.2 eV at 5.90 keV at −30°C. The optical transmittance of the aluminum layer is approximately 10 −8 to 10 −10 for optical lights from 365 to 880 nm, corresponding to an effective aluminum thickness of around 140 to 160 nm. The good X-ray performance and low optical transmittance of this Al-coated sCMOS sensor make it a good choice for space X-ray missions. The Lobster Eye Imager for Astronomy, which has been working in orbit for about one year, is equipped with four EP4K sensors. Furthermore, 48 EP4K sensors are used on the Wide-field X-ray Telescope on the Einstein Probe satellite, which will be launched at the end of 2023.
{"title":"An Aluminum-coated sCMOS Sensor for X-Ray Astronomy","authors":"Qinyu Wu, Zhixing Ling, Chen Zhang, Shuang-Nan Zhang, Weimin Yuan","doi":"10.1088/1538-3873/ad03d7","DOIUrl":"https://doi.org/10.1088/1538-3873/ad03d7","url":null,"abstract":"Abstract In recent years, tremendous progress has been made on scientific Complementary Metal Oxide Semiconductor (sCMOS) sensors, making them a promising device for future space X-ray missions. We have customized a large-format sCMOS sensor, G1516BI, dedicated for X-ray applications. In this work, a 200 nm thick aluminum layer is successfully sputtered on the surface of this sensor. This Al-coated sensor, named EP4K, shows consistent performance with the uncoated version. The readout noise of the EP4K sensor is around 2.5 e − and the dark current is less than 0.01 e − pixel −1 s −1 at −30°C. The maximum frame rate is 20 Hz in the current design. The ratio of single pixel events of the sensor is 45.0%. The energy resolution can reach 153.2 eV at 4.51 keV and 174.2 eV at 5.90 keV at −30°C. The optical transmittance of the aluminum layer is approximately 10 −8 to 10 −10 for optical lights from 365 to 880 nm, corresponding to an effective aluminum thickness of around 140 to 160 nm. The good X-ray performance and low optical transmittance of this Al-coated sCMOS sensor make it a good choice for space X-ray missions. The Lobster Eye Imager for Astronomy, which has been working in orbit for about one year, is equipped with four EP4K sensors. Furthermore, 48 EP4K sensors are used on the Wide-field X-ray Telescope on the Einstein Probe satellite, which will be launched at the end of 2023.","PeriodicalId":20820,"journal":{"name":"Publications of the Astronomical Society of the Pacific","volume":"310 1-2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135565301","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-11-01DOI: 10.1088/1538-3873/ad0451
Xiya Wei, Carlos Quintero Noda, Lanqiang Zhang, Changhui Rao
Abstract Observations of the Sun provide unique insights into its structure, evolution, and activity, with significant implications for space weather forecasting and solar energy technologies. Ground-based telescopes offer cost-effective and flexible solutions for high-resolution solar observations, but image quality can be affected by atmospheric turbulence. Adaptive optics (AO) systems equipped with Shack–Hartmann wave front sensors (SH-WFS) enable real-time image correction to mitigate these effects. The accuracy of SH-WFS relies on correlation algorithms that measure wave front shifts, but reaching consistent conclusions regarding their accuracy remains challenging. In this study, we conducted an evaluation and comparison of standard correlation algorithms (the Square Difference Function, Normalized Cross-Correlation, Absolute Difference Function, Absolute Difference Function-Squared, and the Covariance Function in the frequency domain (CFF)) using simulated and authentic solar images. We optimized the algorithms through pre-processing techniques and carefully selected the most suitable window function for the CFF algorithm. Additionally, we analyzed the influence of various factors, such as shift ranges, bias, and the size of live images on the accuracy of algorithms. The consistent findings revealed that the CFF algorithm demonstrates superior measurement accuracy and robustness compared to the others. Choosing the CFF algorithm for solar observations can significantly enhance measurement accuracy, AO system performance, and the overall quality of solar research findings, thereby providing crucial support for space weather forecasting and other related scientific fields.
{"title":"Comparative Analysis of Image-shift Measurement Algorithms for Solar Shack–Hartmann Wavefront Sensors","authors":"Xiya Wei, Carlos Quintero Noda, Lanqiang Zhang, Changhui Rao","doi":"10.1088/1538-3873/ad0451","DOIUrl":"https://doi.org/10.1088/1538-3873/ad0451","url":null,"abstract":"Abstract Observations of the Sun provide unique insights into its structure, evolution, and activity, with significant implications for space weather forecasting and solar energy technologies. Ground-based telescopes offer cost-effective and flexible solutions for high-resolution solar observations, but image quality can be affected by atmospheric turbulence. Adaptive optics (AO) systems equipped with Shack–Hartmann wave front sensors (SH-WFS) enable real-time image correction to mitigate these effects. The accuracy of SH-WFS relies on correlation algorithms that measure wave front shifts, but reaching consistent conclusions regarding their accuracy remains challenging. In this study, we conducted an evaluation and comparison of standard correlation algorithms (the Square Difference Function, Normalized Cross-Correlation, Absolute Difference Function, Absolute Difference Function-Squared, and the Covariance Function in the frequency domain (CFF)) using simulated and authentic solar images. We optimized the algorithms through pre-processing techniques and carefully selected the most suitable window function for the CFF algorithm. Additionally, we analyzed the influence of various factors, such as shift ranges, bias, and the size of live images on the accuracy of algorithms. The consistent findings revealed that the CFF algorithm demonstrates superior measurement accuracy and robustness compared to the others. Choosing the CFF algorithm for solar observations can significantly enhance measurement accuracy, AO system performance, and the overall quality of solar research findings, thereby providing crucial support for space weather forecasting and other related scientific fields.","PeriodicalId":20820,"journal":{"name":"Publications of the Astronomical Society of the Pacific","volume":"15 12","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135565766","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-01DOI: 10.1088/1538-3873/acff88
Anne-Lise Maire, Laetitia Delrez, Francisco J. Pozuelos, Juliette Becker, Nestor Espinoza, Jorge Lillo-Box, Alexandre Revol, Olivier Absil, Eric Agol, José M. Almenara, Guillem Anglada-Escudé, Hervé Beust, Sarah Blunt, Emeline Bolmont, Mariangela Bonavita, Wolfgang Brandner, G. Mirek Brandt, Timothy D. Brandt, Garett Brown, Carles Cantero Mitjans, Carolina Charalambous, Gaël Chauvin, Alexandre C. M. Correia, Miles Cranmer, Denis Defrère, Magali Deleuil, Brice-Olivier Demory, Robert J. De Rosa, Silvano Desidera, Martín Dévora-Pajares, Rodrigo F. Díaz, Clarissa Do Ó, Elsa Ducrot, Trent J. Dupuy, Rodrigo Ferrer-Chávez, Clémence Fontanive, Michaël Gillon, Cristian Giuppone, Leonardos Gkouvelis, Gabriel de Oliveira Gomes, Sérgio R. A. Gomes, Maximilian N. Günther, Sam Hadden, Yinuo Han, David M. Hernandez, Emmanuel Jehin, Stephen R. Kane, Pierre Kervella, Flavien Kiefer, Quinn M. Konopacky, Maud Langlois, Benjamin Lanssens, Cecilia Lazzoni, Monika Lendl, Yiting Li, Anne-Sophie Libert, Flavia Lovos, Romina G. Miculán, Zachary Murray, Enric Pallé, Hanno Rein, Laetitia Rodet, Arnaud Roisin, Johannes Sahlmann, Robert Siverd, Manu Stalport, Juan Carlos Suárez, Daniel Tamayo, Jean Teyssandier, Antoine Thuillier, Mathilde Timmermans, Amaury H. M. J. Triaud, Trifon Trifonov, Ema F. S. Valente, Valérie Van Grootel, Malavika Vasist, Jason J. Wang, Mark C. Wyatt, Jerry Xuan, Steven Young, Neil T. Zimmerman
Abstract Exoplanetary systems show a wide variety of architectures, which can be explained by different formation and dynamical evolution processes. Precise orbital monitoring is mandatory to accurately constrain their orbital and dynamical parameters. Although major observational and theoretical advances have been made in understanding the architecture and dynamical properties of exoplanetary systems, many outstanding questions remain. This paper aims to give a brief review of a few current challenges in orbital and dynamical studies of exoplanetary systems and a few future prospects for improving our knowledge. Joint data analyses from several techniques are providing precise measurements of orbits and masses for a growing sample of exoplanetary systems, both with close-in orbits and with wide orbits, as well as different evolutionary stages. The sample of young planets detected around stars with circumstellar disks is also growing, allowing for simultaneous studies of planets and their birthplace environments. These analyses will expand with ongoing and future facilities from both ground and space, allowing for detailed tests of formation, evolution, and atmospheric models of exoplanets. Moreover, these detailed analyses may offer the possibility of finding missing components of exoplanetary systems, such as exomoons, or even finding new exotic configurations such as co-orbital planets. In addition to unveiling the architecture of planetary systems, precise measurements of orbital parameters and stellar properties—in combination with more realistic models for tidal interactions and the integration of such models in N -body codes—will improve the inference of the past history of mature exoplanetary systems in close-in orbits. These improvements will allow a better understanding of planetary formation and evolution, placing the solar system in context.
{"title":"Workshop Summary: Exoplanet Orbits and Dynamics","authors":"Anne-Lise Maire, Laetitia Delrez, Francisco J. Pozuelos, Juliette Becker, Nestor Espinoza, Jorge Lillo-Box, Alexandre Revol, Olivier Absil, Eric Agol, José M. Almenara, Guillem Anglada-Escudé, Hervé Beust, Sarah Blunt, Emeline Bolmont, Mariangela Bonavita, Wolfgang Brandner, G. Mirek Brandt, Timothy D. Brandt, Garett Brown, Carles Cantero Mitjans, Carolina Charalambous, Gaël Chauvin, Alexandre C. M. Correia, Miles Cranmer, Denis Defrère, Magali Deleuil, Brice-Olivier Demory, Robert J. De Rosa, Silvano Desidera, Martín Dévora-Pajares, Rodrigo F. Díaz, Clarissa Do Ó, Elsa Ducrot, Trent J. Dupuy, Rodrigo Ferrer-Chávez, Clémence Fontanive, Michaël Gillon, Cristian Giuppone, Leonardos Gkouvelis, Gabriel de Oliveira Gomes, Sérgio R. A. Gomes, Maximilian N. Günther, Sam Hadden, Yinuo Han, David M. Hernandez, Emmanuel Jehin, Stephen R. Kane, Pierre Kervella, Flavien Kiefer, Quinn M. Konopacky, Maud Langlois, Benjamin Lanssens, Cecilia Lazzoni, Monika Lendl, Yiting Li, Anne-Sophie Libert, Flavia Lovos, Romina G. Miculán, Zachary Murray, Enric Pallé, Hanno Rein, Laetitia Rodet, Arnaud Roisin, Johannes Sahlmann, Robert Siverd, Manu Stalport, Juan Carlos Suárez, Daniel Tamayo, Jean Teyssandier, Antoine Thuillier, Mathilde Timmermans, Amaury H. M. J. Triaud, Trifon Trifonov, Ema F. S. Valente, Valérie Van Grootel, Malavika Vasist, Jason J. Wang, Mark C. Wyatt, Jerry Xuan, Steven Young, Neil T. Zimmerman","doi":"10.1088/1538-3873/acff88","DOIUrl":"https://doi.org/10.1088/1538-3873/acff88","url":null,"abstract":"Abstract Exoplanetary systems show a wide variety of architectures, which can be explained by different formation and dynamical evolution processes. Precise orbital monitoring is mandatory to accurately constrain their orbital and dynamical parameters. Although major observational and theoretical advances have been made in understanding the architecture and dynamical properties of exoplanetary systems, many outstanding questions remain. This paper aims to give a brief review of a few current challenges in orbital and dynamical studies of exoplanetary systems and a few future prospects for improving our knowledge. Joint data analyses from several techniques are providing precise measurements of orbits and masses for a growing sample of exoplanetary systems, both with close-in orbits and with wide orbits, as well as different evolutionary stages. The sample of young planets detected around stars with circumstellar disks is also growing, allowing for simultaneous studies of planets and their birthplace environments. These analyses will expand with ongoing and future facilities from both ground and space, allowing for detailed tests of formation, evolution, and atmospheric models of exoplanets. Moreover, these detailed analyses may offer the possibility of finding missing components of exoplanetary systems, such as exomoons, or even finding new exotic configurations such as co-orbital planets. In addition to unveiling the architecture of planetary systems, precise measurements of orbital parameters and stellar properties—in combination with more realistic models for tidal interactions and the integration of such models in N -body codes—will improve the inference of the past history of mature exoplanetary systems in close-in orbits. These improvements will allow a better understanding of planetary formation and evolution, placing the solar system in context.","PeriodicalId":20820,"journal":{"name":"Publications of the Astronomical Society of the Pacific","volume":"70 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136152450","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract A large fraction of celestial objects exhibit point shapes in CCD images, such as stars and QSOs, which contain less information due to their few pixels. Point source classification based solely on image data may lead to low accuracy. To address this challenge, this paper proposes a Multi-modal Transfer Learning-based classification method for celestial objects with point shape images. Considering that spectral data possess rich features and that there is a correlation between spectral data and image data, the proposed approach fully utilizes the knowledge gained from celestial spectral data and transfers it to the original image-based classification, enhancing the accuracy of classifying stars and QSOs. Initially, a one-dimensional residual network is employed to extract a 128-dimensional spectral feature vector from the original 3700-dimensional spectral data. This spectral feature vector captures important features of the celestial object. The Generative Adversarial Network is then utilized to generate a simulated spectral vector of 128 dimensions, which corresponds to the celestial object image. By generating simulated spectral vectors, data from two modals (spectral and image) for the same celestial object are available, enriching the input features of the model. In the upcoming multimodal classification model, we only require the images of celestial objects along with their corresponding simulated spectral data, and we no longer need real spectral data. With the assistance of spectral data, the proposed method alleviates the above disadvantages of the original image-based classification method. Remarkably, our method has improved the F1-score from 0.93 to 0.9777, while reducing the error rate in classification by 40%. These enhancements significantly increase the classification accuracy of stars and QSOs, providing strong support for the classification of celestial point sources.
{"title":"A Multimodal Transfer Learning Method for Classifying Images of Celestial Point Sources","authors":"Bingjun Wang, Shuxin Hong, Zhiyang Yuan, A-Li Luo, Xiao Kong, Zhiqiang Zou","doi":"10.1088/1538-3873/acfbb9","DOIUrl":"https://doi.org/10.1088/1538-3873/acfbb9","url":null,"abstract":"Abstract A large fraction of celestial objects exhibit point shapes in CCD images, such as stars and QSOs, which contain less information due to their few pixels. Point source classification based solely on image data may lead to low accuracy. To address this challenge, this paper proposes a Multi-modal Transfer Learning-based classification method for celestial objects with point shape images. Considering that spectral data possess rich features and that there is a correlation between spectral data and image data, the proposed approach fully utilizes the knowledge gained from celestial spectral data and transfers it to the original image-based classification, enhancing the accuracy of classifying stars and QSOs. Initially, a one-dimensional residual network is employed to extract a 128-dimensional spectral feature vector from the original 3700-dimensional spectral data. This spectral feature vector captures important features of the celestial object. The Generative Adversarial Network is then utilized to generate a simulated spectral vector of 128 dimensions, which corresponds to the celestial object image. By generating simulated spectral vectors, data from two modals (spectral and image) for the same celestial object are available, enriching the input features of the model. In the upcoming multimodal classification model, we only require the images of celestial objects along with their corresponding simulated spectral data, and we no longer need real spectral data. With the assistance of spectral data, the proposed method alleviates the above disadvantages of the original image-based classification method. Remarkably, our method has improved the F1-score from 0.93 to 0.9777, while reducing the error rate in classification by 40%. These enhancements significantly increase the classification accuracy of stars and QSOs, providing strong support for the classification of celestial point sources.","PeriodicalId":20820,"journal":{"name":"Publications of the Astronomical Society of the Pacific","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136161005","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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