FINKER: Frequency Identification through Nonparametric KErnel Regression in astronomical time series

¹ Department of Astrophysics/IMAPP, Radboud University, PO Box 9010, 6500 GL Nijmegen, The Netherlands
e-mail: f.stoppa@astro.ru.nl
² Department of Mathematics/IMAPP, Radboud University, PO Box 9010, 6500 GL Nijmegen, The Netherlands
³ Max-Planck-Institut für Astrophysik, Karl-Schwarzschild-Straße 1, 85741 Garching bei München, Germany
⁴ Institute of Astronomy, KU Leuven, Celestijnenlaan 200D, 3001 Leuven, Belgium
⁵ SRON, Netherlands Institute for Space Research, Sorbonnelaan 2, 3584 CA Utrecht, The Netherlands
⁶ Department of Astronomy and Inter-University Institute for Data Intensive Astronomy, University of Cape Town, Private Bag X3, Rondebosch, 7701, South Africa
⁷ South African Astronomical Observatory, PO Box 9, Observatory 7935, South Africa

Received: 5 December 2023
Accepted: 18 March 2024

Abstract

Context. Optimal frequency identification in astronomical datasets is crucial for variable star studies, exoplanet detection, and astero-seismology. Traditional period-finding methods often rely on specific parametric assumptions, employ binning procedures, or overlook the regression nature of the problem, limiting their applicability and precision.

Aims. We introduce a universal- nonparametric kernel regression method for optimal frequency determination that is generalizable, efficient, and robust across various astronomical data types.

Methods. FINKER uses nonparametric kernel regression on folded datasets at different frequencies, selecting the optimal frequency by minimising squared residuals. This technique inherently incorporates a weighting system that accounts for measurement uncertainties and facilitates multi-band data analysis. We evaluated our method’s performance across a range of frequencies pertinent to diverse data types and compared it with an established period-finding algorithm, conditional entropy.

Results. The method demonstrates superior performance in accuracy and robustness compared to existing algorithms, requiring fewer observations to reliably identify significant frequencies. It exhibits resilience against noise and adapts well to datasets with varying complexity.