Dresden 2026 – scientific programme

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DY: Fachverband Dynamik und Statistische Physik

DY 21: Stochastic Thermodynamics

DY 21.6: Talk

Tuesday, March 10, 2026, 10:45–11:00, ZEU/0114

Compensating random transition-detection blackouts in Markov networks — •Alexander Maier, Benjamin Häsler, and Udo Seifert — II. Institut für Theoretische Physik, Universität Stuttgart, 70550 Stuttgart, Germany

In Markov networks, measurement blackouts with unknown frequency compromise observations such that thermodynamic quantities can no longer be inferred reliably. In particular, the observed currents neither discern equilibrium from non-equilibrium nor can they be used in extant estimators of entropy production. Our strategy to eliminate these effects is based on formally attributing the blackouts to a second channel connecting states. The unknown frequency of blackouts and the true underlying transition rates can be determined from the short-time limit of observed waiting-time distributions. A post-modification of observed trajectory data yields a virtual effective dynamics from which the lower bound on entropy production based on thermodynamic uncertainty relations can be recovered fully. Moreover, the post-processed data can be used in waiting-time based estimators. Crucially, our strategy does neither require the blackouts to occur homogeneously nor symmetrically under time-reversal. Reference: Alexander M. Maier, Benjamin Häsler and Udo Seifert, arXiv:2511.14679 (2025)

Keywords: thermodynamic inference; measurement blackouts; waiting-time distribution; entropy production rate