Supernova Classification using the Recurrent Neural Network in the CSST Ultra-Deep Field Survey

We study supernova (SN) classification using the machine learning method of the Recurrent Neural Network (RNN) in the Chinese Space Station Survey Telescope Ultra-Deep Field (CSST-UDF) photometric survey, and explore the improvement of the cosmological constraint. We generate the mock light curve data of Type Ia supernova (SN Ia) and core collapse supernova (CCSN) using SNCosmo with SALT3 SN Ia model and CCSN templates, and apply the SuperNNova (SNN) program for classifying SNe. Our study indicates that the SNN combined with the Joint Light-curve Analysis like (JLA-like) cuts can enhance the purity of the CSST-UDF SN Ia sample up to over 99.5% with 2,193 SNe Ia and 4 CCSNe, which can significantly increase the reliability of the cosmological constraint results. The method based on the Bayesian Estimation Applied to Multiple Species (BEAMS) with Bias Corrections (BBC) framework is used to correct the SN Ia magnitude bias caused by the selection effect and CCSN contamination, and the Markov Chain Monte Carlo (MCMC) method is employed for cosmological constraints. We find that the accuracy of the constraints on the matter density $\Omega_{\rm M}$ and the equation of state of dark energy $w$ can achieve 14% and 18%, respectively, assuming the flat $w$CDM model. This result is comparable to that from the current surveys that relied on spectroscopic confirmation. It indicates that our data analysis method is effective, and the CSST-UDF SN photometric survey is powerful in exploring the expansion history of the Universe.