Investigating Light Curve Modulation via Kernel Smoothing. I. Application to 53 fundamental mode and first-overtone Cepheids in the LMC

TL;DR

This study introduces a kernel smoothing method to detect and analyze period and amplitude modulations in Cepheid variable stars, revealing widespread and complex variability patterns over a 12-year observational baseline.

Contribution

The paper presents a novel kernel regression approach for simultaneous detection of period and amplitude modulations in Cepheids, improving over traditional pre-whitening techniques.

Findings

Cepheids exhibit diverse and often non-linear modulations.

First overtone Cepheids are less stable than fundamental mode ones.

Period variations are more frequently detected than amplitude variations.

Abstract

Recent studies have revealed a hitherto unknown complexity of Cepheid pulsation. We implement local kernel regression to search for both period and amplitude modulations simultaneously in continuous time and to investigate their detectability, and test this new method on 53 classical Cepheids from the OGLE-III catalog. We determine confidence intervals using parametric and non-parametric bootstrap sampling to estimate significance and investigate multi-periodicity using a modified pre-whitening approach that relies on time-dependent light curve parameters. We find a wide variety of period and amplitude modulations and confirm that first overtone pulsators are less stable than fundamental mode Cepheids. Significant temporal variations in period are more frequently detected than those in amplitude. We find a range of modulation intensities, suggesting that both amplitude and period modulations are ubiquitous among Cepheids. Over the 12-year baseline offered by OGLE-III, we find that period changes are often non-linear, sometimes cyclic, suggesting physical origins beyond secular evolution. Our method more efficiently detects modulations (period and amplitude) than conventional methods reliant on pre-whitening with constant light curve parameters and more accurately pre-whitens time series, removing spurious secondary peaks effectively.