Track reconstruction algorithms are critical for polarization measurements.Convolutional neural networks(CNNs)are a promising alternative to traditional moment-based track reconstruction approaches.However,the hexagon...Track reconstruction algorithms are critical for polarization measurements.Convolutional neural networks(CNNs)are a promising alternative to traditional moment-based track reconstruction approaches.However,the hexagonal grid track images obtained using gas pixel detectors(GPDs)for better anisotropy do not match the classical rectangle-based CNN,and converting the track images from hexagonal to square results in a loss of information.We developed a new hexagonal CNN algorithm for track reconstruction and polarization estimation in X-ray polarimeters,which was used to extract the emission angles and absorption points from photoelectron track images and predict the uncer-tainty of the predicted emission angles.The simulated data from the PolarLight test were used to train and test the hexagonal CNN models.For individual energies,the hexagonal CNN algorithm produced 15%-30%improvements in the modulation factor compared to the moment analysis method for 100%polarized data,and its performance was comparable to that of the rectangle-based CNN algorithm that was recently developed by the Imaging X-ray Polarimetry Explorer team,but at a lower computational and storage cost for preprocessing.展开更多
Gamma-Ray Integrated Detectors(GRID)mis-sion is a student project designed to use multiple gamma-ray detectors carried by nanosatellites(CubeSats),forming a full-time all-sky gamma-ray detection network that monitors ...Gamma-Ray Integrated Detectors(GRID)mis-sion is a student project designed to use multiple gamma-ray detectors carried by nanosatellites(CubeSats),forming a full-time all-sky gamma-ray detection network that monitors the transient gamma-ray sky in the multi-mes-senger astronomy era.A compact CubeSat gamma-ray detector,including its hardware and firmware,was designed and implemented for the mission.The detector employs four Gd 2 Al 2 Ga 3 O 12:Ce(GAGG:Ce)scintillators coupled with four silicon photomultiplier(SiPM)arrays to achieve a high gamma-ray detection efficiency between 10 keV and 2 MeV with low power and small dimensions.The first detector designed by the undergraduate student team onboard a commercial CubeSat was launched into a Sun-synchronous orbit on October 29,2018.The detector was in a normal observation state and accumulated data for approximately one month after on-orbit functional and performance tests,which were conducted in 2019.展开更多
基金supported by the National Natural Science Foundation of China(No.12025301)the Tsinghua University Initiative Scientific Research Program.
文摘Track reconstruction algorithms are critical for polarization measurements.Convolutional neural networks(CNNs)are a promising alternative to traditional moment-based track reconstruction approaches.However,the hexagonal grid track images obtained using gas pixel detectors(GPDs)for better anisotropy do not match the classical rectangle-based CNN,and converting the track images from hexagonal to square results in a loss of information.We developed a new hexagonal CNN algorithm for track reconstruction and polarization estimation in X-ray polarimeters,which was used to extract the emission angles and absorption points from photoelectron track images and predict the uncer-tainty of the predicted emission angles.The simulated data from the PolarLight test were used to train and test the hexagonal CNN models.For individual energies,the hexagonal CNN algorithm produced 15%-30%improvements in the modulation factor compared to the moment analysis method for 100%polarized data,and its performance was comparable to that of the rectangle-based CNN algorithm that was recently developed by the Imaging X-ray Polarimetry Explorer team,but at a lower computational and storage cost for preprocessing.
基金supported by the Tsinghua University Initiative Scientific Research Program,the National Natural Science Foundation of China(Nos.11633003,12025301,and 11821303)the National Key R&D Program of China(Nos.2018YFA0404502 and 2016YFA040080X).
文摘Gamma-Ray Integrated Detectors(GRID)mis-sion is a student project designed to use multiple gamma-ray detectors carried by nanosatellites(CubeSats),forming a full-time all-sky gamma-ray detection network that monitors the transient gamma-ray sky in the multi-mes-senger astronomy era.A compact CubeSat gamma-ray detector,including its hardware and firmware,was designed and implemented for the mission.The detector employs four Gd 2 Al 2 Ga 3 O 12:Ce(GAGG:Ce)scintillators coupled with four silicon photomultiplier(SiPM)arrays to achieve a high gamma-ray detection efficiency between 10 keV and 2 MeV with low power and small dimensions.The first detector designed by the undergraduate student team onboard a commercial CubeSat was launched into a Sun-synchronous orbit on October 29,2018.The detector was in a normal observation state and accumulated data for approximately one month after on-orbit functional and performance tests,which were conducted in 2019.
