Enhancing the detection of low-energy M dwarf flares: Wavelet-based denoising of CHEOPS data

TL;DR

This study demonstrates that wavelet-based denoising of CHEOPS data significantly improves the detection of low-energy flares on M dwarfs, expanding the observable energy range and providing new insights into flare statistics and formation mechanisms.

Contribution

It introduces a tailored wavelet-based denoising algorithm for CHEOPS data, enhancing low-energy flare detection and analysis on M dwarfs, which was limited in previous missions.

Findings

Recovered 291 flares with energies from 3.7×10^26 to 8.9×10^30 erg.

Denoising improved flare recovery rate by approximately 35%.

CHEOPS captures weaker flares than TESS or Kepler, expanding the energy detection range.

Abstract

Stellar flares are powerful bursts of electromagnetic radiation triggered by magnetic reconnection in the chromosphere of stars, occurring frequently and intensely on active M dwarfs. While missions like TESS and Kepler have studied regular and super-flares, their detection of flares with energies below $10^{30}$ erg remains incomplete. Extending flare studies to include these low-energy events could enhance flare formation models and provide insight into their impacts on exoplanetary atmospheres. This study investigates CHEOPS's capacity to detect low-energy flares in M dwarf light curves. Using its high photometric precision and observing cadence, along with a tailored wavelet-based denoising algorithm, we aim to improve detection completeness and refine flare statistics for low-energy events. We conducted a flare injection and recovery to optimise denoising parameters, applied it to CHEOPS light curves to maximise detection rates, and used a flare breakdown algorithm to analyse complex structures. We recovered 291 flares with energies ranging from $3.7\times10^{26}$ to $8.9\times10^{30}$ erg across 62 M dwarfs, with $\sim$42% exhibiting complex, multi-peaked structures. The denoising improved flare recovery by $\sim$35%, although it marginally extended the lower boundary of detectable energies. For the full sample, the power-law index $\alpha$ was $1.99\pm0.10$, but a log-normal distribution fitted better, suggesting multiple possible flare formation scenarios. While CHEOPS's observing mode is not ideal for large-scale surveys, it captures weaker flares than TESS or Kepler, expanding the observed energy range. Wavelet-based denoising enhances low-energy event recovery, enabling exploration of the micro-flaring regime. Expanding low-energy flare observations could refine flare generation models and improve the understanding of their role in star-planet interactions.