TY  - JOUR
PB  - SciPost Foundation
DO  - 10.21468/SciPostPhys.14.5.131
TI  - Extreme value statistics of edge currents in Markov jump processes and their use for entropy production estimation
PY  - 2023/05/25
UR  - https://www.scipost.org/SciPostPhys.14.5.131
JF  - SciPost Physics
JA  - SciPost Phys.
VL  - 14
IS  - 5
SP  - 131
A1  - Neri, Izaak
AU  - Polettini, Matteo
AB  - The infimum of an integrated current is its extreme value against the direction of its average flow. Using martingale theory, we show that the infima of integrated edge currents in time-homogeneous Markov jump processes are geometrically distributed, with a mean value determined by the effective affinity measured by a marginal observer that only sees the integrated edge current. In addition, we show that a marginal observer can estimate a fraction of the average entropy production rate in the underlying nonequilibrium process from the extreme value statistics in the integrated edge current. The estimated average rate of dissipation obtained in this way equals the above mentioned effective affinity times the average edge current. Moreover, we show that estimates of dissipation based on extreme value statistics can be significantly more accurate than those based on thermodynamic uncertainty ratios, as well as those based on a naive estimator obtained by neglecting nonMarkovian correlations in the Kullback-Leibler divergence of the trajectories of the integrated edge current.
ER  -

@Article{10.21468/SciPostPhys.14.5.131,
	title={{Extreme value statistics of edge currents in Markov jump processes and their use for entropy production estimation}},
	author={Izaak Neri and Matteo Polettini},
	journal={SciPost Phys.},
	volume={14},
	pages={131},
	year={2023},
	publisher={SciPost},
	doi={10.21468/SciPostPhys.14.5.131},
	url={https://scipost.org/10.21468/SciPostPhys.14.5.131},
}