YOLO-CL cluster detection in the Rubin/LSST DC2 simulation

LSST will provide galaxy cluster catalogs up to z$\sim$1 that can be used to constrain cosmological models once their selection function is well-understood. We have applied the deep convolutional network YOLO for CLuster detection (YOLO-CL) to LSST simulations from the Dark Energy Science Collaboration Data Challenge 2 (DC2), and characterized the LSST YOLO-CL cluster selection function. We have trained and validated the network on images from a hybrid sample of (1) clusters observed in the Sloan Digital Sky Survey and detected with the red-sequence Matched-filter Probabilistic Percolation, and (2) simulated DC2 dark matter haloes with masses $M_{200c} > 10^{14} M_{\odot}$. We quantify the completeness and purity of the YOLO-CL cluster catalog with respect to DC2 haloes with $M_{200c} > 10^{14} M_{\odot}$. The YOLO-CL cluster catalog is 100% and 94% complete for halo mass $M_{200c} > 10^{14.6} M_{\odot}$ at $0.2<z<0.8$, and $M_{200c} > 10^{14} M_{\odot}$ and redshift $z \lesssim 1$, respectively, with only 6% false positive detections. All the false positive detections are dark matter haloes with $10^{13.4} M_{\odot} \lesssim M_{200c} \lesssim 10^{14} M_{\odot}$. The YOLO-CL selection function is almost flat with respect to the halo mass at $0.2 \lesssim z \lesssim 0.9$. The overall performance of YOLO-CL is comparable or better than other cluster detection methods used for current and future optical and infrared surveys. YOLO-CL shows better completeness for low mass clusters when compared to current detections in surveys using the Sunyaev Zel'dovich effect, and detects clusters at higher redshifts than X-ray-based catalogs. The strong advantage of YOLO-CL over traditional galaxy cluster detection techniques is that it works directly on images and does not require photometric and photometric redshift catalogs, nor does it need to mask stellar sources and artifacts.