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

TL;DR

The paper tackles the need for rapid assessment of Fermi-GBM visibility and quick-look spectral analysis for transients detected by EP. It introduces RapidGBM, a lightweight, web-based toolkit that leverages historical pointing data to compute GBM visibility, generate detector responses, and perform fast spectral diagnostics with PyXspec, demonstrated on a case study of EP240617a. The results show that RapidGBM can identify sub-threshold GBM signals and provide spectral parameters (e.g., $E_{\rm p}$, $T_{90}$, fluence) consistent with real-time pointing within small pointing deviations, enabling timely follow-up and classification of peculiar GRBs via empirical relations such as $E_{\rm p,z}$–$E_{\gamma,\rm iso}$. Overall, the tool supports rapid decision-making for EP-TAs and the broader transient community, and lays groundwork for future multi-messenger follow-up and refined workflow integration.

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. $E_{\rm p}$-$E_{γ,\rm iso}$), it can also help identify peculiar GRBs (e.g. long-short burst, magnetar giant flare, etc.) and provide useful references (e.g. more accurate $T_0$) for scheduling follow-up observations.

Figures (8)

• Figure 1: Distribution of angular deviations obtained by simulating the use of historical poshist files.
• Figure 2: GBM visibility map at the time of EP240617a. In the upper panel, the blue area indicates the region obscured by the Earth, while the red pentagram marks the source position. In the lower panel, the red area represents the SAA region, and the satellite icon traces the orbit 20 minutes before and after the event.
• Figure 3: Light curve of EP240617a generated from nb and b1 detector TTE data. The blue shaded area indicates the interval used for background fitting, the blue and orange solid lines are the fitted background and the count rate corresponding to SNR = 3, respectively, and the red solid line is the trigger time of EP240617a.
• Figure 4: The light curve of EP240617a. Panel (A) shows the background-subtracted light curves for WXT and GBM, with the red solid line indicating the Bayesian block result. The green-shaded region indicates the $T_{90}$ interval of the GBM data. Panel (B) shows the GBM light curve without background subtraction. The blue curve represents the orbit-averaged background, while the orange line indicates the background-derived count rate corresponding to SNR = 2. The black arrow indicates a time around 80 seconds, at which a corresponding increase is also observed in the WXT data.
• Figure 5: The spectral analysis of EP240617a for intervals a and b. The top panel shows the observed count spectra with best-fit models, with shaded regions indicating the 90% confidence intervals. The middle panel presents the corresponding $\nu F_\nu$ spectra with the same confidence regions, and the bottom panel displays the data-to-model ratios, with interval a in light blue and interval b in light orange.