Chen-Xi Liang

In ultra-central heavy-ion collisions the measured ratio of elliptic to triangular flow approaches unity, whereas linear scaling from initial eccentricity predicts a clear hierarchy. UCC-DiffHIC, an AI-accelerated event-by-event hydrodynamic model, generates these extremely rare collisions and decomposes the flow ratio exactly into a linear-response term and the ratio of the Pearson response correlations for elliptic and triangular flow. For the standard TRENTo parameter set, the suppression from residual fluctuations offsets the linear-scaling enhancement, bringing the ratio near unity in the most central collisions in agreement with CMS; two Bayesian parameter sets compensate less completely.

In ultra-central heavy-ion collisions the measured ratio of elliptic to triangular flow approaches unity, whereas linear scaling from initial eccentricity predicts a clear hierarchy. UCC-DiffHIC, an AI-accelerated event-by-event hydrodynamic model, generates these extremely rare collisions and decomposes the flow ratio exactly into a linear-response term and the ratio of the Pearson response correlations for elliptic and triangular flow. For the standard TRENTo parameter set, the suppression from residual fluctuations offsets the linear-scaling enhancement, bringing the ratio near unity in the most central collisions in agreement with CMS; two Bayesian parameter sets compensate less completely.