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We considered two fundamentally different approaches to real-bogus classification within the Zwicky Transient Facility survey data. The first approach is based on neural networks that take sequences of object images as input. The second approach uses features extracted from light curves and classical machine learning methods. Several models for both approaches were tested. Quality metrics were evaluated using k-fold cross-validation. We found that models based on classical machine learning algorithms outperform the neural network approach in both computational performance and quality. The code written during the study is available on https://github.com .

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Real-Bogus Classification for ZTF Data Releases: Two Approaches

  • 会议论文

  • 发表日期：

    2026年

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  • Timofey Semenikhin,
  • Matwey Kornilov,
  • Maria Pruzhinskaya,
  • Anastasia Lavrukhina,
  • Etienne Russeil,
  • Emmanuel Gangler,
  • Emille Ishida,
  • Vladimir Korolev,
  • Konstantin Malanchev,
  • Alina Volnova,
  • Sreevarsha Sreejith

摘要

We considered two fundamentally different approaches to real-bogus classification within the Zwicky Transient Facility survey data. The first approach is based on neural networks that take sequences of object images as input. The second approach uses features extracted from light curves and classical machine learning methods. Several models for both approaches were tested. Quality metrics were evaluated using k-fold cross-validation. We found that models based on classical machine learning algorithms outperform the neural network approach in both computational performance and quality. The code written during the study is available on https://github.com .