Exploring Superfluid Angular Momentum Reservoir Effect on Pulsar Glitches and Forecasting Next Glitches of the Crab Pulsar

TL;DR

The paper challenges the view of pulsar glitches as purely stochastic by showing that Crab glitch activity exhibits long-term quasi-periodic modulation governed by a superfluid angular momentum reservoir. By clustering small, temporally proximate glitches and analyzing both consecutive and non-consecutive waiting times, the authors reveal a near 3.5-year fundamental period with 7- and 14-year harmonics, and a stronger pre-cluster waiting-time correlation with glitch size. They interpret these results as evidence of partial angular momentum release and history-dependent coupling, enabling predictions of future activity, including a major glitch anticipated before August 2026 with predicted amplitudes around $697.2\times 10^{-9}$ to $813.8\times 10^{-9}$. The findings imply a universal reservoir mechanism across pulsars and emphasize the Crab as a key laboratory for probing interior neutron star dynamics, with significant implications for timing campaigns and multiwavelength observations.

Abstract

Pulsar glitches are generally viewed as stochastic events driven by sudden angular momentum transfer from the neutron star's superfluid interior to its crust. Except two peculiar pulsars with quasi-periodic glitches, this stochastic view has prevailed. Here, by clustering temporally proximate small glitches of the Crab pulsar, we uncover clear evidence of an underlying quasi-periodic modulation, challenging the paradigm of purely random behavior. Furthermore, our correlation analyses reveal a strong positive relationship between glitch cluster size and waiting time since the preceding clusters. These findings demonstrate the effect of angular momentum reservoir operating over long-term scales and enable the predictions of next glitching window. Remarkably, two minor glitches detected in July and August 2025, which align with our initial prediction made in June, should be confirmed as the onset of this predicted activity. Inspired by the initial success, we forecast the occurrence of a major glitch from now until August 2026, with possible glitch size up to a relative change in rotational frequency of $697.2 \times 10^{-9}$. Physically, the observed long-term quasi-periodicity and cluster size-waiting time correlations imply that each glitch event releases only a fraction of the stored superfluid angular momentum. This partial-release mechanism provides a unified framework for both stochastic and quasi-periodic glitch behaviors across different pulsars, underscoring the universality of the superfluid angular momentum reservoir effect. As the most intensively monitored object, the Crab pulsar serves as a natural laboratory for studying angular momentum inside neutron stars.

Figures (4)

• Figure 1: (a): Glitch sizes in the Crab pulsar over time (first glitch at MJD 46663.69 set to zero). The three largest glitches (purple circles) follow a linear trend; dashed lines mark midpoints between them. (b): Evolution of inter-glitch waiting times. The 25th and 50th percentiles of the exponential waiting-time distribution are shown as hotpink and skyblue lines, respectively, and intervals below the 25th percentile are highlighted in hotpink.
• Figure 2: (a) Cumulative distribution function (CDF) of consecutive waiting times fitted with a normal distribution. (b) CDF of non-consecutive waiting times fitted with a normal distribution. Here, $\Delta t^{(1)}$ denotes the interval between consecutive glitch clusters, whereas $\Delta t^{(2)}$ represents the interval between non-consecutive glitches, that is, the time span separating every other glitch clusters.
• Figure 3: The correlation between cluster sizes and waiting times. In each glitch cluster, the largest glitch is taken of a cluster as the representative event, and the total glitch magnitude of a cluster as the cluster size. Panel (a) uses post-cluster waiting times and panel (b) uses pre-cluster waiting times show that the preceding waiting time ($\Delta t_{\mathrm{pre}}$) correlates more strongly with cluster size than the following waiting time ($\Delta t_{\mathrm{post}}$).The Pearson correlation coefficients and their corresponding p-values are given for each case. Due to the substantial uncertainty in the size of the last glitch cluster, this data point is excluded when calculating the correlation coefficient between cluster size and the preceding waiting time, and it is marked with a pentagram.
• Figure 4: Similar to Figure 3 but for two-step case. Panels (a), (b) and (c) corresponds to post-two-step, mid-two-step and pre-two-step situations, respectively.