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Record W2398134290 · doi:10.48550/arxiv.1605.08625

Phase transition in a double branching annihilating random walk

2016· preprint· en· W2398134290 on OpenAlexaboutno aff
Attila Nagy

Bibliographic record

VenuearXiv (Cornell University) · 2016
Typepreprint
Languageen
FieldMathematics
TopicStochastic processes and statistical mechanics
Canadian institutionsnot available
Fundersnot available
KeywordsRandom walkBranching (polymer chemistry)Statistical physicsBranching random walkPhysicsPhase transitionMathematicsCondensed matter physicsStatisticsChemistry

Abstract

fetched live from OpenAlex

This paper investigates the long-time behavior of double branching annihilating random walkers with nearest-neighbor dependent rates. The system consists of even number of particles which can execute nearest-neighbor random walk and they can as well give birth in a parity conserving manner to two other particles with rates $1$ and $b$, respectively, until they meet. Upon meeting, each of the adjacent particles can branch with rate $p\cdot b$ while it can annihilate, i.e. hop on, the other particle with rate $p$ for some $0< p\leq 1$. This process first appeared in the article by D. ben Avraham, F. Leyvraz and S. Redner (Propagation and extinction in branching annihilating random walks, Phys. Rev. E 50(3), 1994) and can be considered as the extension of Sudbury's model (The branching annihilating process: an interacting particle system, Ann. Probab., 18(2), 1990). We prove that in some region of the parameters $(p,b)$, the process survives with positive probability. Combining with Sudbury's extinction result it shows a phase transition phenomenon for this model. In some sense our result also shows the sharpness of the assumptions of the article by M. Balázs and A. L. Nagy (Dependent double branching annihilating random walk, Electron. J. Probab., 20(84), 2015). We use similar arguments that was developed by M. Bramson and L. Gray in their article titled "The survival of branching annihilating random walk" (Z. Wahrsch. Verw. Gebiete, 68(4), 1985).

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.781
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.130
GPT teacher head0.264
Teacher spread0.134 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2016
Admission routes1
Has abstractyes

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