Cosmological Prediction of the CSST Ultra Deep Field Type Ia Supernova Photometric Survey

Type Ia supernova (SN Ia) as a standard candle is an ideal tool to measure cosmic distance and expansion history of the Universe. Here we investigate the SN Ia photometric measurement in the China Space Station Telescope Ultra Deep Field (CSST-UDF) survey, and study the constraint power on the cosmological parameters, such as the equation of state of dark energy. The CSST-UDF survey is expected to cover a 9 deg^2 sky area in two years with 60 exposures. The magnitude limit can reach i≃26 AB mag for 5σ point source detection. We generate light curve mock data for SNe Ia and different types of core-collapse supernovae (CCSNe), based on the relevant SN SED templates, natural generation rates, luminosity functions, CSST instrumental design and survey strategy. After selecting high-quality data and fitting the light curves, we derive the light curve parameters and identify CCSNe as contamination, resulting in ∼2200 SNe with a ∼7% CCSN contamination rate. We adopt a calibration method similar to Chauvenet's criterion, and apply it to the distance modulus data to further reduce the contamination. We find that this method is effective and can suppress the contamination fraction to ∼3.5% with 2012 SNe Ia and 73 CCSNe. About 16% of SNe Ia are at z>1 in the final CSST-UDF SN sample. By checking the cosmological constraints, the result derived from this calibrated SN sample is in good agreement with that using the pure SN Ia sample. The constraint accuracies on Ω_ M, Ω_Λ and w are about 10%∼20%, which is about two times better than the current SN surveys, and it could be further improved by a factor of ∼1.4 if including the baryon acoustic oscillation (BAO) data from the CSST spectroscopic survey. This indicates that CSST can provide accurate measurements for the cosmic expansion history and the nature of dark energy.