Document detail
ID
oai:arXiv.org:2406.08571
Topic
Astrophysics - Instrumentation and...Author
Miller, Niall Lucas, Philip Sun, Yi Guo, Zhen Morris, Calum Cooper, WilliamCategory
sciences: astrophysics
Year
2024
listing date
6/19/2024
Keywords
methods curve light dataAbstract
The ability to automatically and robustly self-verify periodicity present in time-series astronomical data is becoming more important as data sets rapidly increase in size.
The age of large astronomical surveys has rendered manual inspection of time-series data less practical.
Previous efforts in generating a false alarm probability to verify the periodicity of stars have been aimed towards the analysis of a constructed periodogram.
However, these methods feature correlations with features that do not pertain to periodicity, such as light curve shape, slow trends and stochastic variability.
The common assumption that photometric errors are Gaussian and well determined is also a limitation of analytic methods.
We present a novel machine learning based technique which directly analyses the phase folded light curve for its false alarm probability.
We show that the results of this method are largely insensitive to the shape of the light curve, and we establish minimum values for the number of data points and the amplitude to noise ratio.
Miller, Niall,Lucas, Philip,Sun, Yi,Guo, Zhen,Morris, Calum,Cooper, William, 2024, The verification of periodicity with the use of recurrent neural networks
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