Real-Bogus Classification for ZTF Data Releases: Two Approaches

P. 211–219.

Semenikhin T., Kornilov M., Pruzhinskaya M., Lavrukhina A., Russeil E., Gangler E., Ishida E., Korolev V., Malanchev K., Volnova A., Sreejith S.

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.

Keywords: machine learning, neural network

In book

26th International Conference, DAMDID/RCDL 2024, Nizhny Novgorod, Russia, October 23–25, 2024, Revised Selected Papers. Data Analytics and Management in Data Intensive Domains. (CCIS, volume 2641)

Springer, 2026.