Title: Improving estimation of entropy production rate for run-and-tumble particle systems by high-order thermodynamic uncertainty relation

Abstract: Entropy production plays an important role in the regulation and stability of active matter systems, and its rate quantifies the nonequilibrium nature of these systems. However, entropy production is hard to be experimentally estimated even in some simple active systems like molecular motors or bacteria, which may be modeled by the run-and-tumble particle (RTP), a representative model in the study of active matters. Here we resolve this problem for an asymmetric RTP in one-dimension, firstly constructing a finite time thermodynamic uncertainty relation (TUR) for a RTP, which works well in the short observation time regime for entropy production estimation. Nevertheless, when the activity dominates,i.e., the RTP is far from equilibrium, the lower bound for entropy production from TUR turns to be trivial. We address this issue by introducing a recently proposed high-order thermodynamic uncertainty relation (HTUR), in which the cumulant generating function of current serve as a key ingredient. To exploit the HTUR, we adopt a novel method to analytically obtain the cumulant generating function of the current we study, with no need to explicitly know the time-dependent probability distribution. The HTUR is demonstrated to be able to estimate the steady state energy dissipation rate accurately because the cumulant generating function covers higher-order statistics of the current, including rare and large fluctuations besides its variance. Compared to the conventional TUR, the HTUR could give significantly improved estimation of energy dissipation, which can work well even in the far-from equilibrium regime. We also provide a strategy based on the improved bound to estimate the entropy production from moderate amount of trajectory data for experimental feasibility.