Visitors: 1383

  MAZEDAN DIGITAL LIBRARY    

  JOURNAL MANAGEMENT SYSTEM

MDL
JMS  
MAZEDAN DIGITAL LIBRARY

Enhancing Exoplanet Transit Detection in Noisy Stellar Light Curves Using Statistical Filtering Technique

JOURNAL:MAZEDAN INTERNATIONAL JOURNAL OF APPLIED MATHEMATICS

Download pdf

# Authors First Online DOI Downloads Citations
1. Abhimaan Abhijit Ayare,* Abhipreet Abhijit Ayare, Vaishali Salunk, Harish Suryawanshi, Ansari Mizna Iram, Syed Md Humayun Akhter 01 Nov 2025 https://doi.org/10.5281/zenodo.17500040 4 0

Abstract

This study investigates methods to enhance exoplanet transit detection in noisy stellar light curves using simple yet robust statistical filtering techniques. Real photometric data from NASA’s Kepler Space Telescope—including observations of the enigmatic KIC 8462852 (Tabby’s Star)—were analyzed. Three filtering methods—moving average, median, and Savitzky–Golay—were implemented to mitigate noise while preserving true transit signatures. Among these, the median filter proved most effective in suppressing outliers and long-term stellar variability, while moving averages reduced high-frequency noise. Combined filtering significantly improved the signal-to-noise ratio (SNR), enabling clearer identification of subtle transit events. These findings suggest that lightweight preprocessing approaches can substantially enhance the efficiency of large-scale photometric surveys and improve the reliability of exoplanet detection pipelines


Keywords

Exoplanet detection; Light curves; Signal processing; Statistical filtering; Kepler data


References
  1. [1]    Foreman-Mackey, D., Luger, R., Agol, E., et al. (2021). The exoplanet population observation simulator (EPOS). The Astronomical Journal, 161(3), 123.
    [2]    Hippke, M., & Heller, R. (2019). Optimized transit detection algorithm to search for periodic transits of small planets. Astronomy & Astrophysics, 623, A39.
    [3]    Kostov, V. B., Orosz, J. A., Feinstein, A. D., et al. (2020). *TOI-1338: TESS' first transiting circumbinary planet*. The Astronomical Journal, 159(6), 253.
    [4]    Shallue, C. J., & Vanderburg, A. (2019). *Identifying exoplanets with deep learning: A five-planet resonant chain around Kepler-80*. The Astronomical Journal, 155(2), 94.
    [5]    Hattori, S., Foreman-Mackey, D., Hogg, D. W., & Montet, B. T. (2022). Notch filtering for exoplanet transit detection in stellar light curves. The Astronomical Journal, 163(6), 284.
    [6]    Vanderspek, R., Latham, D. W., Winn, J. N., et al. (2019). *TESS discovery of an ultra-short-period planet around the nearby M dwarf LHS 3844*. The Astrophysical Journal Letters, 871(2), L24.
    [7]    Aizawa, M., Uehara, S., Masuda, K., & Kawahara, H. (2023). Systematic noise removal from exoplanetary light curves using deep learning. The Astrophysical Journal, 945(2), 147.
    [8]    Günther, M. N., & Daylan, T. (2021). A comprehensive study of the Kepler, TESS, and PLATO mission data. Space Science Reviews, 217(2), 1-35.
    [9]    Barragán, O., Aigrain, S., Kubyshkina, D., & Lüftinger, T. (2022). PyLightcurve: A Python package for exoplanet light curve modelling. Monthly Notices of the Royal Astronomical Society, 509(1), 866-883.
    [10]    Montalto, M., Borsato, L., Granata, V., et al. (2020). A new algorithm for the recognition of planetary transit signals in stellar light curves. Astronomy & Astrophysics, 642, A149.