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Extreme fluctuations of active Brownian motion

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Patrick Pietzonka, Kevin Kleinbeck, Udo Seifert

Statistical Mechanics · Submitted 9 Mar 2016

abstract compiled · 2026-09-23 · no language-model review

The authors' main claim We derive the distribution of its extreme fluctuations and identify its universal properties using large deviation theory.

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arXiv ID1603.03026 (version 2)
Pith IDMW26D4U3, Pith's permanent record of this paper and its review
CitationKevin Kleinbeck, Patrick Pietzonka, Udo Seifert. "Extreme fluctuations of active Brownian motion." arXiv:1603.03026 (2016). https://pith.science/paper/MW26D4U3
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Compiled by classifier-1.13.0 via TypeSafe API, question bank abstract-p1-20260923, 2026-09-23: 1 request, 4,199 input tokens.

What the abstract itself states, as probabilities: claims first 0.06 · states limitation 0.10 · human participants 0.01 · controlled comparison 0.03 · randomized assignment 0.01 · states quantitative result 0.19 · compares with prior results 0.19 · mentions code or data availability 0.02.

The abstract, sentence by sentence

arXiv listing abstract v2. Results and the main claim are underlined; .

In active Brownian motion, an internal propulsion mechanism interacts with translational and rotational thermal noise and other internal fluctuations to produce directed motion. We derive the distribution of its extreme fluctuations and identify its universal properties using large deviation theory. The limits of slow and fast internal dynamics give rise to a kink-like and parabolic behavior of the corresponding rate functions, respectively. For dipolar Janus particles in two and three dimensions interacting with a field, we predict a novel symmetry akin to, but different from, the one related to entropy production. Measurements of these extreme fluctuations could thus be used to infer properties of the underlying, often hidden, network of states.

background contribution method result interpretation main claim

What the classifier reports for each sentence. The number is the probability it gave the label.
#sentencerolethe authors' own workstates the main conclusionstrength
s0 In active Brownian motion, an internal propulsion mechanism interacts with translational and rotational thermal noise and other internal fluctuations to produce directed motion. background 1.00 0.05 0.03 asserted 1.00
s1 We derive the distribution of its extreme fluctuations and identify its universal properties using large deviation theory. contribution 0.89 0.96 0.29 asserted 1.00
s2 The limits of slow and fast internal dynamics give rise to a kink-like and parabolic behavior of the corresponding rate functions, respectively. result 1.00 0.85 0.23 asserted 1.00
s3 For dipolar Janus particles in two and three dimensions interacting with a field, we predict a novel symmetry akin to, but different from, the one related to entropy production. result 0.95 0.84 0.15 asserted 0.78
s4 Measurements of these extreme fluctuations could thus be used to infer properties of the underlying, often hidden, network of states. interpretation 0.94 0.18 0.23 hedged possibility 1.00

What kind of paper the classifier reports this is: theoretical result 1.00 · evidence from proof or derivation 1.00.

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Every quoted sentence on this record is the authors'. Every label is from Pith's closed vocabulary, with the probability shown beside it. Every number in the measurements table is the authors', checked by code. Record as JSON

The paper
Original abstract

In active Brownian motion, an internal propulsion mechanism interacts with translational and rotational thermal noise and other internal fluctuations to produce directed motion. We derive the distribution of its extreme fluctuations and identify its universal properties using large deviation theory. The limits of slow and fast internal dynamics give rise to a kink-like and parabolic behavior of the corresponding rate functions, respectively. For dipolar Janus particles in two and three dimensions interacting with a field, we predict a novel symmetry akin to, but different from, the one related to entropy production. Measurements of these extreme fluctuations could thus be used to infer properties of the underlying, often hidden, network of states.

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