Finding the Particularity of the Active Episode of SGR J1935+2154 during Which FRB 20200428 Occurred: Implication from Statistics of Fermi/GBM X-Ray Bursts

TL;DR

The study investigates self-organized criticality (SOC) in the X-ray bursts (XRBs) of SGR J1935+2154 and examines why the FRB 20200428 event occurred during a particular activity episode. Using Fermi/GBM data from 2014–2021, the authors model waiting times with a Weibull nonstationary Poisson process and analyze the waiting-time and fluence/flux distributions, finding SOC-like statistics and time clustering, with a notable difference in the FRB-associated episode. The waiting-time distribution follows a double Pareto-Lognormal with $\alpha_1=3.85$ and $\alpha_2=0.73$, while fluence/flux follows a smoothly broken power law; together these support SOC behavior in magnetar bursts. They apply a unified scaling law (USL) for earthquakes to classify bursts into dependent and independent categories, discovering a higher fraction of dependent bursts in the FRB episode (≈$0.33$–$0.36$), implying FRB emission could arise from interactions among burst events during a globally active magnetar phase. Overall, the results bolster the magnetar FRB origin scenario and provide a statistical framework linking XRB timing/energy distributions to possible FRB production mechanisms via burst interactions.

Abstract

By using the Fermi/Gamma-ray Burst Monitor data of the X-ray bursts (XRBs) of SGR J1935+2154, we investigate the temporal clustering of the bursts and the cumulative distribution of the waiting time and fluence/flux. It is found that the bursts occurring in the episode hosting FRB 20200428 have obviously shorter waiting times than those in the other episodes. The general statistical properties of the XRBs further indicate they could belong to a self-organized critical (SOC) system (e.g., starquakes), making them very similar to the earthquake phenomena. Then, according to a unified scaling law between the waiting time and energy of the earthquakes as well as their aftershocks, we implement an analogy analysis on the XRBs and find that the FRB episode owns more dependent burst events than the other episodes. It is indicated that the fast radio burst (FRB) emission could be produced by the interaction between different burst events, which could correspond to a collision between different seismic/Alfven waves or different explosion outflows. Such a situation could appear when the magnetar enters into a global intensive activity period.

Figures (7)

• Figure 1: The XRBs (triangle markers) of SGR J1935+2154 detected by GBM within different observation windows (horizontal lines). The length of the lines represents the duration of the windows. The first burst in each window is labelled by a black triangle, which is dropped in our statistics since its waiting time cannot be defined. Then, the windows that only have one burst are excluded (i.e, the grey lines). The remaining windows having sufficiently large number of bursts can be basically separated into four active episodes, as labelled by different colors (see Section \ref{['subsec:cd']} for active episode definition).
• Figure 2: The corner plot of the shape parameter $k$ and the event rate $\lambda$ for the Weibull distribution (see Section \ref{['subsec:tc']}).
• Figure 3: The logarithmic waiting times, $\log(\Delta{t})$, of the XRBs v.s their occurring times, where the time of FRB 20200428 is represented by the green dashed line. The number distribution of the bursts on the waiting times are exhibited in the right panel with the histograms, while the smooth lines give the kernel density of these distributions. The histogram distributions can be approximately fitted by a Gaussian function and its mean value and variance are represented by the orange dot and the short black line in the left panel.
• Figure 4: The CCDs of the waiting time (Left) and fluence/flux (Right) of the XRBs. In the left panel, the dashed line gives the dPLn fitting of the CCD, while the dot and dashed lines in the right panel present the smoothly BPL and SPL fits, respectively.
• Figure 5: The $x-y$ USL of XRBs. The dashed lines give the fits of the data by Eq. (\ref{['eq:usl_bpl']}) for parameter values listed in Table \ref{['tab:usl']}.