Search arXiv⌕ Search
Home/Archive/arXiv

arXiv · 1907.06842

GRID: a Student Project to Monitor the Transient Gamma-Ray Sky in the Multi-Messenger Astronomy Era

Jiaxing Wen·Xiangyun Long·Xutao Zheng·Yu An·Zhengyang Cai·Jirong Cang·Yuepeng Che·Changyu Chen·Liangjun Chen·Qianjun Chen·Ziyun Chen·Yingjie Cheng·Litao Deng·Wei Deng·Wenqing Ding·Hangci Du·Lian Duan·Quan Gan·Tai Gao·Zhiying Gao·Wenbin Han·Yiying Han·Xinbo He·Xinhao He·Long Hou·Fan Hu·Junling Hu·Bo Huang·Dongyang Huang·Xuefeng Huang·Shihai Jia·Yuchen Jiang·Yifei Jin·Ke Li·Siyao Li·Yurong Li·Jianwei Liang·Yuanyuan Liang·Wei Lin·Chang Liu·Gang Liu·Mengyuan Liu·Rui Liu·Tianyu Liu·Wanqiang Liu·Di'an Lu·Peiyibin Lu·Zhiyong Lu·Xiyu Luo·Sizheng Ma·Yuanhang Ma·Xiaoqing Mao·Yanshan Mo·Qiyuan Nie·Shuiyin Qu·Xiaolong Shan·Gengyuan Shi·Weiming Song·Zhigang Sun·Xuelin Tan·Songsong Tang·Mingrui Tao·Boqin Wang·Yue Wang·Zhiang Wang·Qiaoya Wu·Xuanyi Wu·Yuehan Xia·Hengyuan Xiao·Wenjin Xie·Dacheng Xu·Rui Xu·Weili Xu·Longbiao Yan·Shengyu Yan·Dongxin Yang·Hang Yang·Haoguang Yang·Yi-Si Yang·Yifan Yang·Lei Yao·Huan Yu·Yangyi Yu·Aiqiang Zhang·Bingtao Zhang·Lixuan Zhang·Maoxing Zhang·Shen Zhang·Tianliang Zhang·Yuchong Zhang·Qianru Zhao·Ruining Zhao·Shiyu Zheng·Xiaolong Zhou·Runyu Zhu·Yu Zou·Peng An·Yifu Cai·Hongbing Chen·Zigao Dai
View original source ↗Read source document ↗Export metadata ↓

Abstract

The Gamma-Ray Integrated Detectors (GRID) is a space mission concept dedicated to monitoring the transient gamma-ray sky in the energy range from 10 keV to 2 MeV using scintillation detectors onboard CubeSats in low Earth orbits. The primary targets of GRID are the gamma-ray bursts (GRBs) in the local universe. The scientific goal of GRID is, in synergy with ground-based gravitational wave (GW) detectors such as LIGO and VIRGO, to accumulate a sample of GRBs associated with the merger of two compact stars and study jets and related physics of those objects. It also involves observing and studying other gamma-ray transients such as long GRBs, soft gamma-ray repeaters, terrestrial gamma-ray flashes, and solar flares. With multiple CubeSats in various orbits, GRID is unaffected by the Earth occultation and serves as a full-time and all-sky monitor. Assuming a horizon of 200 Mpc for ground-based GW detectors, we expect to see a few associated GW-GRB events per year. With about 10 CubeSats in operation, GRID is capable of localizing a faint GRB like 170817A with a 90% error radius of about 10 degrees, through triangulation and flux modulation. GRID is proposed and developed by students, with considerable contribution from undergraduate students, and will remain operated as a student project in the future. The current GRID collaboration involves more than 20 institutes and keeps growing. On August 29th, the first GRID detector onboard a CubeSat was launched into a Sun-synchronous orbit and is currently under test.

Explore related subjects

astro-ph.IMastro-ph.HE

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Jiaxing Wen, Xiangyun Long, Xutao Zheng, Yu An, Zhengyang Cai, Jirong Cang, Yuepeng Che, Changyu Chen, Liangjun Chen, Qianjun Chen, Ziyun Chen, Yingjie Cheng, Litao Deng, Wei Deng, Wenqing Ding, Hangci Du, Lian Duan, Quan Gan, Tai Gao, Zhiying Gao, Wenbin Han, Yiying Han, Xinbo He, Xinhao He, Long Hou, Fan Hu, Junling Hu, Bo Huang, Dongyang Huang, Xuefeng Huang, Shihai Jia, Yuchen Jiang, Yifei Jin, Ke Li, Siyao Li, Yurong Li, Jianwei Liang, Yuanyuan Liang, Wei Lin, Chang Liu, Gang Liu, Mengyuan Liu, Rui Liu, Tianyu Liu, Wanqiang Liu, Di'an Lu, Peiyibin Lu, Zhiyong Lu, Xiyu Luo, Sizheng Ma, Yuanhang Ma, Xiaoqing Mao, Yanshan Mo, Qiyuan Nie, Shuiyin Qu, Xiaolong Shan, Gengyuan Shi, Weiming Song, Zhigang Sun, Xuelin Tan, Songsong Tang, Mingrui Tao, Boqin Wang, Yue Wang, Zhiang Wang, Qiaoya Wu, Xuanyi Wu, Yuehan Xia, Hengyuan Xiao, Wenjin Xie, Dacheng Xu, Rui Xu, Weili Xu, Longbiao Yan, Shengyu Yan, Dongxin Yang, Hang Yang, Haoguang Yang, Yi-Si Yang, Yifan Yang, Lei Yao, Huan Yu, Yangyi Yu, Aiqiang Zhang, Bingtao Zhang, Lixuan Zhang, Maoxing Zhang, Shen Zhang, Tianliang Zhang, Yuchong Zhang, Qianru Zhao, Ruining Zhao, Shiyu Zheng, Xiaolong Zhou, Runyu Zhu, Yu Zou, Peng An, Yifu Cai, Hongbing Chen, Zigao Dai. 2019-07-16. GRID: a Student Project to Monitor the Transient Gamma-Ray Sky in the Multi-Messenger Astronomy Era. https://doi.org/10.1007/s10686-019-09636-w

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

Observations of Binary Stars with the 1.3-m Devasthal Fast Optical Telescope Using Speckle Interferometry: An Attempt

We present a feasibility study of implementing optical interferometry and speckle techniques with the 1.3-m Devasthal Fast Optical Telescope (DFOT) at Aryabhatta Research Institute of Observational Sciences (ARIES), which is currently dedicated to photometric observations. Using the sCMOS camera at the DFOT backend, we perform interferometric speckle observations of six binary stars. Standard Speckle Interferometry algorithms are applied to analyze the recorded speckle images. While this study does not aim to achieve the diffraction limit of DFOT or address a full science-driven resolution case, it serves as a crucial testbed for instrumentation, data acquisition, and analysis of Speckles with DFOT. Although the individual speckle patterns exhibit significant positional variations from frame to frame, the Speckle Interferometry analysis successfully recovers the characteristic three-peak structure of the wide binaries, allowing their relative separations and position angles to be determined, demonstrating the viability of the approach. The measured angular relative separations of $γ$ Leo, $γ$ Vir, and $ζ$ Her are $4.744'' \pm 0.012''$, $3.387'' \pm 0.027''$, and $1.582'' \pm 0.026''$, respectively, with corresponding projected position angles of $126.94^{\circ} \pm 0.14^\circ$, $171.57^{\circ} \pm 0.33^\circ$, and $81.06^{\circ} \pm 0.91^\circ$. These measurements are consistent with previously reported values in the literature, which provides strong motivation for more systematic observations and future implementation of optical interferometry techniques at meter-class telescopes.

astro-ph.IM↗
arXiv2026

Time-Domain Synthesis of Gravitational-Wave Detector Glitches using Class-Conditional Derivative Generative Adversarial Networks

Gravitational-wave detectors such as LIGO, Virgo, and KAGRA are highly sensitive instruments susceptible to many noise sources. Short-duration transient noise events, known as glitches, pose a particular challenge for data analysis pipelines, as they can mimic or obscure astrophysical signals. We present GlitchGAN, a class-conditional generative model built on the Conditional Derivative GAN (cDVGAN) architecture, capable of synthesizing seven glitch types from LIGO's third observing run (O3) directly in the time domain. GlitchGAN generalizes effectively, learning to reproduce a diverse glitch space consistent with high-quality DeepExtractor reconstructions, and can generate hybrid glitch morphologies by interpolating across its class-conditioning vector. It generates 1000 glitches in under 22 seconds on a CPU, suitable for detector simulations, mock data challenges, and pipeline validation. Synthetic glitches are validated using the Gravity Spy classifier and UMAP embeddings, both showing strong agreement with real data. To probe residual distributional differences, we train a separate holdout GlitchGAN model and use a downstream CNN to distinguish held-out real glitches from synthetic ones: detectability is high in a clean representation but drops substantially once both populations are injected into realistic detector noise, the condition under which they would typically be used. Despite this, GlitchGAN-generated glitches remain practically useful: augmenting real training sets with synthetic samples matches simple duplication of real data when data is abundant, and increasingly outperforms it as real data becomes scarce. Finally, we highlight a limitation of magnitude-only spectrograms: magnitude Q-transform classifiers can confidently misclassify physically unrealistic glitches from less robust models, underscoring the need for validation methods that preserve phase information.

astro-ph.IM↗
arXiv2026

SGN: A python framework for stream-processing pipelines

We present the Stream Graph Navigator (SGN), a lightweight Python framework for building streaming data applications. In SGN, stream-processing pipelines are built by connecting computational components into directed acyclic graphs that run within an event loop. The time-series extension of the SGN library, SGN-TS, introduces signal processing methods to handle time series data. Together, SGN and SGN-TS provide the foundation for SGNL, a matched-filtering gravitational-wave search pipeline, and are being adopted by multiple projects across the low-latency gravitational-wave data analysis infrastructure as an extensible and maintainable framework for future gravitational-wave observations.

astro-ph.IM↗