A comprehensive search for Long and Short Periodic Features from an Extremely Active Cycle of FRB 20240114A

TL;DR

This work conducts a comprehensive multi-timescale search for periodic and quasi-periodic features in FRB 20240114A using FAST data. It combines short-timescale TOA analyses with a Time-Differencing Algorithm and $Z_n^2$ refinement, and a long-timescale Lomb-Scargle approach to reveal a significant $P = 143.40 \\pm 7.19$ days activity cycle, plus a harmonic near $73.60$ days, suggesting periodic repeater behavior. Burst time-series analyses uncover transient quasi-periodic oscillations at $ ext{f} \\sim$ hundreds of Hz and ms-scale periodic burst trains, evidenced by ACF and FFT-based Bayesian methods, with EMD supporting QPO components. Overall, the results demonstrate a self-similar timing structure across scales, constrain potential rotation or binary scenarios, and emphasize magnetar- and environment-driven mechanisms; however, a definitive spin period remains unconfirmed.

Abstract

Possible periodic features in fast radio bursts (FRBs) may provide insights into their astrophysical origins. Using extensive observations from the Five-hundred-meter Aperture Spherical radio Telescope (FAST), we conduct a multi-timescale periodicity search for the exceptionally active repeater FRB~20240114A. Our analysis is based on different datasets for different timescales: for short-timescale periodicity in Time of Arrivals (TOAs), we use 57 observations from January to August 2024; for long-timescale periodicity, we employ an extended TOA dataset comprising 111 observations spanning from January 2024 to October 2025; and for burst time series analysis, we utilize individual burst data from the 57 FAST observations. We identify three candidate short-timescale periodic signals (0.673~s, 0.635~s, and 0.536~s) with significances of $3.2σ$--$6σ$, each detected in two independent observations. On longer timescales, we detect a significant $143.40\pm7.19$-day periodicity with $5.2σ$ significance, establishing FRB~20240114A as a periodic repeater. In burst time series, we find quasi-periodic oscillations in the few hundred Hz range ($3.4σ$ and $3.7σ$) and periodic burst trains with periods of several to tens of milliseconds ($3σ$--$3.9σ$), though these periodic features appear transient and short-lived. The detection of periodic signals at these different time scales indicates that FRB 20240114A exhibits intriguing periodic self-similar characteristics. Despite the comprehensive dataset, no definitive periodicity linked to the source's rotation is confirmed, placing stringent constraints on the intrinsic source properties and the modulation mechanisms. All data are available via the Science Data Bank.

Figures (9)

• Figure 1: The 57 FAST observations of FRB 20240114A from January 28 to August 29, 2024 (UT), show the distribution of waiting times between bursts (top panel), burst counts (bars in the middle panel), burst rates (red dots in the middle panel), cumulative burst counts (dashed line in the middle panel), and exposure times (bars in the bottom panel). In the top panel, vertical lines of different colors represent the period values of three candidates identified in the TDA short-timescale search. The bar charts with the same colors in the middle panel indicate that there are candidate periodic signals with similar parameters in the corresponding observations (see Section \ref{['sec:period_search']}).
• Figure 2: Folding results of the short-timescale periodicity candidates. Each row corresponds to one candidate group, with the left and right panels showing similar periods observed on different observation dates (see Table \ref{['tab:short_TDA']}). Top of each panel indicates the candidate period $P$ in seconds. For each candidate, the top panel displays the pulse profile folded with the corresponding period after correcting for the period derivative, and the bottom panel shows the distribution of individual TOAs in phase.
• Figure 3: Long-timescale periodicity search results for FRB 20240114A. The analysis uses all TOAs from observations between 2024-01-28 and 2025-10-01 ($T_{\rm span} = 612.33$ days), searching periods from 1.5 days to $T_{\rm span}/2 \approx 306.16$ days. Candidate periods were selected at $>3\sigma$ confidence (FAP $< 0.0027$) and verified through epoch folding with Gaussian profile modeling. A: Burst rate evolution with time, showing the count rate (counts/hour) versus MJD. The green-shaded regions mark the predicted enhanced active phases. These are derived from a phase-folding analysis using the 143.40-day period, with a Gaussian fit (panel C1) applied to identify the timing and duration of the expected enhanced activity windows. B: Lomb-Scargle periodogram showing normalized power versus period. The black solid line represents the burst rate-weighted analysis, while the orange dashed line shows the observing window function. Two significant periods are identified: Period 1 = $143.40 \pm 7.19$ days (red dashed line, $5.2\sigma$ significance) and Period 2 = $73.60 \pm 2.45$ days (green dashed line, $3.3\sigma$ significance). C1-C3: Phase-folded analysis for Period 1. C1: Intensity profile showing exposure-corrected (green) and uncorrected (blue) rates with Gaussian fit (red dashed), from which the enhanced active window timing and duration are determined. C2: Relative exposure time distribution across phase bins. C3: TOA versus phase, with point sizes scaled by burst rate weights. D1-D3: Similar to C1-C3 but for Period 2. Based on the analysis results, Period 2 is identified as a harmonic of Period 1.
• Figure 4: Four quasi-periodic candidates identified from the burst time series using the ACF method, each with a significance exceeding $3\sigma$. Each candidate consists of four subplots: the top-left subplot shows the burst time series, the bottom-left subplot shows the corresponding dynamic spectrum, the top-right subplot shows the ACF of the burst time series, and the bottom-right subplot shows the simulated statistic used to calculate the probability of generating this ACF pattern. The red line in the burst time series represents the periodic intervals determined by the ACF analysis, while the red line in the simulated statistic corresponds to the observed value.
• Figure 5: Analysis of an FRB burst containing a QPO component using the FFT-based Bayesian inference method. The six panels illustrate key steps in data processing and model evaluation. (a) Frequency–time dynamic spectrum of the burst, dedispersed using a dispersion measure of 528.5 pc cm$^{-3}$, with RFI-contaminated channels masked; (b) Time series obtained by frequency integration of the dynamic spectrum, used as input for the periodicity analysis; (c) Periodogram of the time series. The blue dashed line represents the background power spectrum modeled under the null hypothesis $H_0$ (no QPO component), while the red dashed line corresponds to the alternative hypothesis $H_1$ (with a QPO component). A prominent excess of power is seen at a specific frequency. The best-fit parameters for the $H_1$ model are listed in Table \ref{['tab:QPO_pars']}, entry No. 1. (d) Residuals of the periodogram under both $H_0$ and $H_1$, used to quantify deviations from the respective model fits; (e) Posterior distribution of model parameters obtained via MCMC sampling under $H_0$, used to generate simulated periodograms; (f) Distribution of likelihood ratio test statistics computed from the simulated periodograms. The red line marks the observed test statistic $T_\mathrm{LRT}^{\mathrm{obs}}$, which quantifies the significance of the QPO component. The observed significance of this QPO, as inferred from panel (f), is $3.4\sigma$.