Wavelet transforms of microlensing data: Denoising, extracting intrinsic pulsations, and planetary signals

TL;DR

This paper demonstrates that wavelet transforms effectively denoise microlensing data, revealing intrinsic pulsations and planetary signals that are otherwise obscured by noise, with performance depending on observation cadence and wavelet choice.

Contribution

The study introduces the application of discrete and continuous wavelet transforms to microlensing data for denoising and signal extraction, highlighting their effectiveness and optimal wavelet types.

Findings

Wavelet denoising reduces data noise and improves model fit.

Performance depends on observation cadence, decreasing with longer intervals.

Wavelet families like Symlet and Biorthogonal are most effective.

Abstract

Wavelets are waveform functions that describe transient and unstable variations, such as noises. In this work, we study the advantages of discrete and continuous wavelet transforms (DWT and CWT) of microlensing data to denoise them and extract their planetary signals and intrinsic pulsations hidden by noises. We first generate synthetic microlensing data and apply wavelet denoising to them. For these simulated microlensing data with ideally Gaussian nosies based on the OGLE photometric accuracy, denoising with DWT reduces standard deviations of data from real models by $0.044$-$0.048$ mag. The efficiency to regenerate real models and planetary signals with denoised data strongly depends on the observing cadence and decreases from $37\%$ to $0.01\%$ by worsening cadence from $15$ min to $6$ hrs. We then apply denoising on $100$ microlensing events discovered by the OGLE group. On average, wavelet denoising for these data improves standard deviations and $\chi^{2}_{\rm n}$ of data with respect to the best-fitted models by $0.023$ mag, and $1.16$, respectively. The best-performing wavelets (based on either the highest signal-to-noise ratio's peak ($\rm{SNR}_{\rm{max}}$), or the highest Pearson's correlation, or the lowest Root Mean Squared Error (RMSE) for denoised data) are from 'Symlet', and 'Biorthogonal' wavelets families in simulated, and OGLE data, respectively. In some denoised data, intrinsic stellar pulsations or small planetary-like deviations appear which were covered with noises in raw data. However, through DWT denoising rather flattened and wide planetary signals could be reconstructed than sharp signals. CWT and 3D frequency-power-time maps could advise about the existence of sharp signals.