Speaker
Description
Precise primary vertex reconstruction is critical for several measurements such as prompt particle identification and heavy-flavor measurements in the sPHENIX experiment. In this work, we present a detailed performance comparison of the standard sPHENIX primary vertex reconstruction algorithm with an alternative approach based on Kalman filtering. The standard method relies on DCA-based track selection, while the Kalman filter approach iteratively refines the vertex estimate using track covariance information. Both algorithms are evaluated using real and simulated sPHENIX events, with comparisons to truth-level vertices to assess vertex resolution, reconstruction efficiency, and computational performance. We further tune key primary vertexing parameters for each method to optimize reconstruction accuracy and stability. The impact of the original and optimized parameter sets is then studied in the context of prompt particle production, with a particular focus on the reconstruction of the ϕ→K^+ K^- decay channel. Finally, we examine how improvements in primary vertex determination propagate to downstream physics observables relevant for heavy-flavor analyses, including charmonium and bottomonium measurements. This study provides a quantitative assessment of DCA-based versus Kalman filter-based primary vertex reconstruction in sPHENIX, informing algorithm choices and parameter optimization for future precision heavy-ion and proton-proton analyses.
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