[Paper Review] Reconnection-driven State Transitions in Flat Spectrum Radio Quasars

Agniva Roychowdhury|arXiv (Cornell University)|Mar 15, 2026

Astrophysics and Cosmic Phenomena0 citations

TL;DR

The study extends skewness-based analysis of GeV flares from CTA 102 to 18 FSRQs, and uses a Smoluchowski-type plasmoid merger model to reproduce observed skewness, entropy changes, and PSD features in blazar GeV light curves.

ABSTRACT

We extend the work of Roychowdhury (2026) on skewness variations of the logarithmic flux, driven by large GeV flares in FSRQs, to a sample of 18 FSRQs. We find that they can be categorized into three groups, one where the skewness attains a persistent lower value after a large flare, one where it does not, and those where change in skewness is not significant. To provide a theoretical ground for these results, we use the statistical plasmoid model of Fermo et al. (2010) that self-consistently produces large plasmoids through merging which, when gain energy from the reconnection event and are Doppler aligned, produce large flares. We find that a downsampling of our simulation of 1500 runs to 18 statistically reproduces the observed distribution in p-values for change in skewness. We further compute the ensemble Shannon entropy of the system and the skewness, where the entropy is found to decrease at a $3σ$ level in both the groups where skewness either increases or decreases, as a direct evidence of increase in order in the system caused by a flare. We find that the power spectral densities of the simulated light curves are broken-power-laws, resembling a white noise+red noise broken by the typical cooling timescale in our system, in accordance with known blazar variability. We find that our results are robust to a $200-300\%$ change in several fiducial parameters of the simulation. Our stochastic simulation of plasmoids inside a blazar jet self-consistently reproduces key observable statistical properties of blazar GeV light curves.

Motivation & Objective

• Extend the skewness-based flare study from CTA 102 to a broader sample of 18 FSRQs.
• Test whether reconnection-driven plasmoid mergers can reproduce observed statistical properties of GeV blazar variability.
• Quantify how large GeV flares influence the skewness, entropy, and flare statistics across sources.
• Provide a self-consistent framework connecting plasmoid dynamics with observable blazar light-curve statistics.

Proposed method

• Analyze 18-year GeV light curves from the Fermi-LAT repository with weekly bins and TS ≥ 25.
• Compute rolling skewness around the largest flare and assess pre-/post-flare differences using the Mann-Whitney U test.
• Apply a Smoluchowski-like coagulation model for plasmoid evolution (injection, escape, merging) to simulate light curves.
• Use a Gillespie algorithm to stochastically solve the plasmoid coagulation equations and generate 1500 light-curve realizations.
• Link simulated luminosity to observations via a Doppler-boosted, cooling-informed energy framework and baseline jet emission.

Experimental results

Research questions

• RQ1 Do FSRQs exhibit flare-induced state transitions in skewness analogous to CTA 102?
• RQ2 Can a self-consistent plasmoid merger model reproduce the observed distribution of skewness changes (p-values) across flares?
• RQ3 How do plasmoid dynamics affect entropy and the order/disorder of blazar emission during and after large flares?
• RQ4 Do simulated light curves produce PSDs with broken-power-law shapes similar to those observed in GeV blazars?

Key findings

• 18 FSRQs show a range of skewness responses to the largest flare: some decrease, some increase, and some with inconclusive changes.
• Downsampled simulations (1500 runs to 18) statistically reproduce the observed distribution of p-values for skewness change.
• Ensemble Shannon entropy decreases at >3σ when skewness changes (both increases and decreases) due to flare-driven ordering.
• Simulated light curves exhibit broken-power-law PSDs with a break near 0.04 cycles per unit time, pre/post slopes ~0.4 and ~-1.8.
• The model remains robust to 200–300% changes in several fiducial parameters, indicating stability of the reconnection-driven scenario.
• CTA 102 is not an outlier; the broader sample supports reconnection-driven state transitions as a physical mechanism.