A probe of the maximum energetics of fast radio bursts through a prolific repeating source

Abstract

Fast radio bursts (FRBs) are sufficiently energetic to be detectable from luminosity distances up to at least seven billion parsecs (redshift $z > 1$). Probing the maximum energies and luminosities of FRBs constrains their emission mechanism and cosmological population. Here we investigate the maximum energetics of a highly active repeater, FRB 20220912A, using 1,500 h of observations. We detect $130$ high-energy bursts and find a break in the burst energy distribution, with a flattening of the power-law slope at higher energy -- consistent with the behaviour of another highly active repeater, FRB 20201124A. There is a roughly equal split of integrated burst energy between the low- and high-energy regimes. Furthermore, we model the rate of the highest-energy bursts and find a turnover at a characteristic spectral energy density of $E^{\textrm{char}}_ν = 2.09^{+3.78}_{-1.04}\times10^{32}$ erg/Hz. This characteristic maximum energy agrees well with observations of apparently one-off FRBs, suggesting a common physical mechanism for their emission. The extreme burst energies push radiation and source models to their limit: at this burst rate a typical magnetar ($B = 10^{15}$ G) would deplete the energy stored in its magnetosphere in $\sim$ 2150 h, assuming a radio efficiency $ε_\mathrm{radio} = 10^{-5}$. We find that the high-energy bursts ($E_ν> 3 \times 10^{30}$ erg Hz$^{-1}$) play an important role in exhausting the energy budget of the source.

Figures (9)

• Figure 1: Dynamic spectra, time and temporal profiles for a subset of bursts. Each subfigure consists of three panels. Shown in the top panel is the burst-id, the time- and frequency resolution at which the data is plotted and the time profile of the burst. The colored bars represent the width for each component of a burst whereas the color of the bars correspond the instrument used to detect the burst. Purple corresponds to Stockert (St), orange to Toruń (Tr) and blue to Westerbork (Wb). The side panel shows the temporal profile which is the sum over the time axis, but only under the colored bars. The white vertical lines are masked channels at the edges of the subbands or the presence of radio frequency interference (RFI) which are indicated by red ticks. The bursts have been corrected for dispersive effects where we used a value of $219.37$ pc cm$^{-3}$ for bursts detected at $1.4~\mathrm{GHz}$ (L-band) and $219.73$ pc cm$^{-3}$ for bursts detected at $0.3~\mathrm{GHz}$ (P-band). For Stockert this correction was applied incoherently (between frequency channels) and for Toruń and Westerbork this correction was applied incoherently and coherently (within frequency channels). The dynamic spectra of all detected bursts are available as part of the Supplementary material.
• Figure 2: Cumulative burst energy distribution of spectral energy densities. In the left panel we show detections by Westerbork (Wb) and Stockert (St), FAST zhang_2023_apj and NRT konijn_2024_mnras at $1.4~\mathrm{GHz}$ (L-band). In order compare between the different observational campaigns we only show bursts that were observed between MJD 59869 and 59910. Comparing the different rates reveals a break in the distribution towards higher energies ($\sim3\times10^{30}$ erg Hz$^{-1}$). The purple and blue vertical line correspond to the completeness threshold as indicated in Table \ref{['tab:coverage']}. The red and black vertical lines denote the point where the distribution can be best described by a single power law as calculated by the Python package powerlaw. Transparent data points which are on the left side of the vertical lines were excluded in the fit. When fitting we set a $20\,\%$ error on the energies and quote two errors. The first error is the $1\sigma$ statistical uncertainty on the fit and the second error is the $1\sigma$ error after the bootstrapping method. In the right panel we show detections observed at $0.3~\mathrm{GHz}$ (P-band).
• Figure 3: Cumulative burst energy distribution for bursts detected by NRT. Left: The cumulative energy distribution for NRT, also shown in Figure \ref{['fig:burst_rate_pl_band']}, fitted with two power laws. The vertical dotted black line is the estimated completeness threshold and the vertical dotted red line indicates the turnover point as estimated using the powerlaw package. The solid black line corresponds to the determined breakpoint of the distribution at $E_{\rm{break}} = 3.2~\times~10^{30}$ erg Hz$^{-1}$. Right: the power law index ($\gamma_{\rm{C}}$) as a function of the spectral energy density calculated by a maximum-likelihood estimation. The horizontal dotted black lines indicate the slopes of the power law used in the left panel.
• Figure 4: Spectral energy densities modelled by a Schechter function. Left: corner plot of the results from the MCMC analysis, thinned by a factor $30$ for visual purposes. The solid lines denote the median values of the posterior distributions and the errors are the $16\,\%$ and $84\,\%$ quantile. Right: The differential distribution in the top panel includes bursts above $3.3~\times~10^{30}$ erg Hz$^{-1}$ (or $24.4$ Jy ms) detected by Westerbork, Stockert, Toruń, NRT, ATA and FAST between MJD 59869 and 59910. The green line is an over-plotted Schechter function based on the median values of the posterior distribution of the MCMC analysis. The vertical orange dashed line is the best fit value for the characteristic energy with the coloured region corresponding to error region of the $16\,\%$ and $84\,\%$ quantile. The bottom panel shows the ratio between the data and the Schechter model.
• Figure 5: Magnetic field constraints for B77-St. Minimum local magnetic field $B$ as a function of neutron period for B77-St assuming coherent curvature radiation lu_maximum_2019cooper_2021_mnras. Solid lines refer to fixed field line curvature radius of $\rho_{\rm c} = 10^{7} \, {\rm cm}$, dot-dashed lines refer to emission along the last open field line (LOFL) at the polar cap co-latitude.