100,000 Crab giant pulses at 215 MHz detected with an SKA-Low prototype station

Abstract

We report detection and analysis of the largest low-frequency (200 - 231.25 MHz) sample of Crab giant pulses (GPs) reported in the literature. In total about 95000 GPs were detected. The observations were performed in 2024/2025 with the EDA2, a prototype station of the SKA-Low telescope. The fluence distribution of GPs in the entire sample is very well characterised with a single power law (no flattening at higher fluences) N(F) $\propto$ F$^α$, where $α= -3.17\pm0.02$ for all GPs, and $α_{MP} = -3.13\pm0.02$ and $α_{IP} = -3.59\pm0.06$ for GPs at the phases of the main pulse and interpulse respectively. The index of the power law fluence distribution remained approximately constant over the observing period, but the normalisation of the distribution was strongly correlated with the scatter broadening time ($τ$). As a result, the measured fluence distribution increased for lower ($τ\approx$ 2 ms) and decreased for higher ($τ\approx$ 5 ms) scatter broadening time $τ$ causing the GP rate to vary between 3000 and 100 per hour respectively. The timescale of variations (weeks) indicates refractive scintillation as the root cause. We also observe a strong positive correlation between the scatter broadening time and dispersion measure. Our modelling favours the screen of the size $\sim10^{-5}$ pc and mean electron density $\sim 400$e$^{-}$cm$^{-3}$ located within 100 pc from the pulsar. The frequency scaling of the scattering broadening time ($τ\propto ν^β$) with $β\approx -3.6\pm0.1$ is in agreement with earlier measurements. Our results agree with the current views that GPs from extra-galactic Crab-like pulsars can be responsible for very weak repeating FRBs, but cannot explain the entire FRB population. Finally, these results demonstrate an enormous scientific potential of individual SKA-Low stations.

Figures (17)

• Figure 1: Flowchart of the EDA2 FRB pipeline. From left to right: complex voltages from EDA2 real-time station beam at 1.08 usec time resolution are recorded and saved on the data acquisition server (eda2-server on the left), with data from 40 coarse channels saved to separate 40 dada files. These files are copied to the data processing computer and processed off-line with a custom developed CPU/GPU spectrometer, which fine-channelised each coarse channel into 64 fine channels, time averaged resulting spectra to 0.96768 ms time resolution and saved to 40 filterbank files. These files were merged into a single wide-band filterbank file, which was searched for single pulses with PRESTO 2011ascl.soft07017R and FREDDA 2019ascl.soft06003B software packages. The resulting single-pulse candidates were saved to text files for further processing and visual inspection of corresponding dynamic spectra.
• Figure 2: The brightest Crab giant pulse detected with EDA2 (SNR $\approx$350). The corresponding flux density is $\approx$16.6 kJy and fluence $\approx$76 Jy ms. Left: dynamic spectrum calibrated in Jy. Right: profile after de-dispersion and averaging over the entire frequency band with a fitted function (Gaussian pulse with an exponential tail as per equation \ref{['eq_pulse_scatter_broadening']} in \ref{['appendix_one']}). The fitted scatter broadening time $\tau =$1.918$\pm$0.006 ms, and the FWHM of the Gaussian is FWHM$= 1.4 \pm 0.01$ ms which is inline with the $\approx$1 ms time resolution of the data.
• Figure 3: DM (red circles) and scattering time (blue crosses) as a function of time during the time period 2024-12-14 to 2025-03-31. The black line connects the Jodrell Bank DM measurements (black stars) performed fortnightly. There is a good agreement between DM trends observed in this work and Jodrell Bank data. Furthermore, our data show strong correlation (Pearson correlation coefficient $\approx$0.7 and p-value = $10^{-8}$) between DM and scattering time ($\tau$). We note that the DM increase and correlation between DM and $\tau$ may be partially caused by changes of pulsar average profile due to scatter broadening. However, our additional verifications (see Section \ref{['subsec_scattering_and_dm']}) and Jodrell Bank measurements show that there is at least $\approx$0.015 pc cm$^{-3}$ increase in DM. Hence, the results of our analysis are valid to within factor of 2.
• Figure 4: Number of detected GPs as a function of time. The number of detected GPs is strongly correlated, in fact driven, by the scatter broadening (compare this figure with Figure \ref{['fit_dm_and_scat_vs_time']} above and see also Figure \ref{['fig_ngp_vs_tau']}). This comparison clearly shows that GP rate was very high (up to $\sim$3000 h$^{-1}$) during in low-scattering conditions ($\tau \sim$2 ms), and very low (down to $\sim$250 h$^{-1}$) during high-scattering periods ($\tau \sim$4 - 5 ms).
• Figure 5: Scatter broadening time ($\tau$) as a function of $\Delta$DM with a fitted linear function. The two quantities are highly correlated with correlation coefficient $\approx$0.7. However, part of this correlation may be caused by the impact of scatter broadening on DM measurement based on pulsar timing analysis. The DM measurement based on maximising SNR of GPs yielded lower correlation coefficient $\sim$0.5. More robust DM measurement procedure in the presence of strong scattering is required to confirm this correlation.