Statistical Signatures of Nanoflare Activity. I. Monte Carlo Simulations and Parameter-space Exploration

Abstract

Small-scale magnetic reconnection processes, in the form of nanoflares, have become increasingly hypothesized as important mechanisms for the heating of the solar atmosphere, for driving propagating disturbances along magnetic field lines in the Sun's corona, and for instigating rapid jet-like bursts in the chromosphere. Unfortunately, the relatively weak signatures associated with nanoflares places them below the sensitivities of current observational instrumentation. Here, we employ Monte Carlo techniques to synthesize realistic nanoflare intensity time series from a dense grid of power-law indices and decay timescales. Employing statistical techniques, which examine the modeled intensity fluctuations with more than 10 7 discrete measurements, we show how it is possible to extract and quantify nanoflare characteristics throughout the solar atmosphere, even in the presence of significant photon noise. A comparison between the statistical parameters (derived through examination of the associated intensity fluctuation histograms) extracted from the Monte Carlo simulations and SDO/AIA 171Å and 94Å observations of active region NOAA 11366 reveals evidence for a flaring power-law index within the range of 1.82 ≤ α ≤ 1.90, combined with e-folding timescales of 385 ± 26 s and 262 ± 17 s for the SDO/AIA 171Å and 94Å channels, respectively. These results suggest that nanoflare activity is not the dominant heating source for the active region under investigation. This opens the door for future dedicated observational campaigns to not only unequivocally search for the presence of small-scale reconnection in solar and stellar environments, but also quantify key characteristics related to such nanoflare activity.

NGTS Observations of Enhanced Nanoflare Activity in Fully Convective Stars Linked to Plasma Resistivity

“…Interestingly, the M4V stars display evidence for longer e-folding timescales and larger power-law indices when compared to their M2.5V and M3V counterparts. As discussed above, this is likely an explanation for the relatively constant ζ values throughout the spectral sub-type range, whereby the statistical effects of higher power-law indices are negated by the slower decay timescales associated with those stars, which is consistent with previous solar and stellar case studies 8,14 .…”

“…It must be remembered that the ζ value is a measure of the deviation away from a standard Gaussian distribution, which has a value of ζ = 1.73. Increased nanoflare decay timescales (i.e., larger τ values) result in broader tails of the intensity fluctuation distributions 14 , hence giving rise to ζ > 1.73. On the contrary, large power-law indices help reduce the widths of the tails in the intensity fluctuation distributions due to the superposition of positive intensity fluctuations (e.g., new nanoflares) superimposed on top of decaying (i.e., negative) intensity fluctuations, which result in ζ < 1.73.…”

“…These Monte-Carlo models were first developed for previous stellar nanoflare analysis 8 , and fully described therein. The nanoflare modelling techniques were originally adapted from 2-D Solar nanoflare modelling code, which have been documented extensively 14 .…”

“…Flares are found to exhibit two distinct statistical signatures 8,14,60,61 . First is a negative median offset, whereby the median value of the histogram is < 0 σ N , i.e., offset from the mean of the distribution that is equal to 0 σ N following the polynomial detrending.…”

“…While solar nanoflares have been a considerable topic of interest over the last 20 years, stellar nanoflares have been almost entirely unexplored -remaining stubbornly below the noise floor of observations, with their low energies precluding the identification of individual stellar nanoflare events. However, nanoflares exhibit statistical signatures that distinguish them from the quiescent background of a star, alongside characteristic quasi-periodic signatures 8,14 . The statistical signatures manifest themselves as distinct offsets from the normal distribution expected of shot noise patterns, which can be benchmarked by studying successive intensity fluctuations of the stellar time series.…”