English

{\tt RapidGBM}: An Efficient Tool for Fermi-GBM Visibility Checking and Data Analysis with a Case Study of EP240617a

High Energy Astrophysical Phenomena 2025-10-08 v2

Abstract

We have developed a lightweight tool, {\tt RapidGBM}, featuring a web-based interface and capabilities of rapid calculation of Fermi Gamma-ray Burst Monitor (GBM) visibilities and performance of basic data analysis. It has two key features: (1) it can immediately check the visibility of Fermi-GBM for new transients, and (2) it can check the light curve and perform spectral analysis after the hourly Time-Tagger Event data are released. The visibility check and the response matrix generation required for spectral analysis can be achieved through the historical pointing file after the orbit calculation, even when the real-time pointing file is not yet available. As a case study, we apply the tool to EP240617a, an X-ray transient triggered by Einstein Probe (EP). We demonstrate the workflow of visibility checking, data processing, and spectral analysis for this event. The results suggest that EP240617a can be classified as an X-ray-rich gamma-ray burst (XRR) and confirm the feasibility of using historical pointing files for rapid analysis. Further, we discuss possible physical interpretations of such events, including implications for jet launching and progenitor scenarios. Therefore, {\tt RapidGBM} is expected to assist EP Transient Advocates, Space-based multiband astronomical Variable Objects Monitor burst advocates, and other members of the community in cross checking high-energy transients. Based on prompt emission parameter relations (e.g. EpE_{\rm p}-Eγ,isoE_{\gamma,\rm iso}), it can also help identify peculiar GRBs (e.g. long-short burst, magnetar giant flare, etc.) and provide useful references (e.g. more accurate T0T_0) for scheduling follow-up observations.

Keywords

Cite

@article{arxiv.2506.20532,
  title  = {{\tt RapidGBM}: An Efficient Tool for Fermi-GBM Visibility Checking and Data Analysis with a Case Study of EP240617a},
  author = {Yun Wang and Jia Ren and Lu-Yao Jiang and Hao Zhou and Yi-Han Iris Yin and Yi-Fang Liang and Zhi-Ping Jin and Yi-Zhong Fan and Da-Ming Wei and Wei Chen and Hui Sun and Jing-Wei Hu and Dong-Yue Li and Jun Yang and Wen-Da Zhang and Yuan Liu and Wei-Min Yuan and Xue-Feng Wu},
  journal= {arXiv preprint arXiv:2506.20532},
  year   = {2025}
}

Comments

12 pages, 8 figures, 1 table, accepted for publication in ApJ