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Computing the center of a fusion category

Representation Theory · Submitted 19 Jun 2024

abstract compiled · 2026-10-16 · no language-model review

The authors' main claim We present an algorithm for explicitly computing the categorical (Drinfeld) center of a pivotal fusion category.

how it is stated · asserted 1.00 read the record →

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arXiv ID2406.13438 (version 3)
Pith IDZHBFQ2PA, Pith's permanent record of this paper and its review
CitationFabian M\"aurer, Ulrich Thiel. "Computing the center of a fusion category." arXiv:2406.13438 (2024). https://pith.science/paper/ZHBFQ2PA
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What the abstract itself states, as probabilities: claims first 0.05 · states limitation 0.25 · human participants 0.01 · controlled comparison 0.01 · randomized assignment 0.01 · states quantitative result 0.97 · compares with prior results 0.13 · mentions code or data availability 0.75.

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arXiv listing abstract v3. Results and the main claim are underlined; .

We present an algorithm for explicitly computing the categorical (Drinfeld) center of a pivotal fusion category. Our approach is based on decomposing the images of simple objects under the induction functor from the category to its center. We have implemented this algorithm in a general-purpose software framework TensorCategories for tensor categories that we develop within the open-source computer algebra system OSCAR. We compute explicit models for the centers in form of the tuples $(X,\gamma)$ where $X$ is an object and $\gamma$ is a half-braiding. From these models we can compute the $F$-symbols and $R$-symbols. Using the data from the AnyonWiki, we were able to compute the center together with its $F$-symbols and $R$-symbols for all the 279 multiplicity-free fusion categories up to rank 5, and furthermore some chosen examples of rank 6, including the Haagerup subfactor (presented in a separate paper). Some of these centers yield new high-rank and not multiplicity-free examples of modular categories, e.g. rank 21 and multiplicity 2.

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What the classifier reports for each sentence. The number is the probability it gave the label.
#sentencerolethe authors' own workstates the main conclusionstrength
s0 We present an algorithm for explicitly computing the categorical (Drinfeld) center of a pivotal fusion category. contribution 1.00 0.95 0.57 asserted 1.00
s1 Our approach is based on decomposing the images of simple objects under the induction functor from the category to its center. method 1.00 0.81 0.06 asserted 1.00
s2 We have implemented this algorithm in a general-purpose software framework TensorCategories for tensor categories that we develop within the open-source computer algebra system OSCAR. method 0.58 0.95 0.05 asserted 1.00
s3 We compute explicit models for the centers in form of the tuples $(X,\gamma)$ where $X$ is an object and $\gamma$ is a half-braiding. method 0.74 0.93 0.13 asserted 1.00
s4 From these models we can compute the $F$-symbols and $R$-symbols. method 0.72 0.86 0.06 asserted 0.78
s5 Using the data from the AnyonWiki, we were able to compute the center together with its $F$-symbols and $R$-symbols for all the 279 multiplicity-free fusion categories up to rank 5, and furthermore some chosen examples of rank 6, including the Haagerup subfactor (presented in a separate paper). result 0.94 0.95 0.31 asserted 1.00
s6 Some of these centers yield new high-rank and not multiplicity-free examples of modular categories, e.g. rank 21 and multiplicity 2. result 0.97 0.91 0.18 asserted 0.97

What kind of paper the classifier reports this is: method or tool 1.00 · evidence from simulation or computation 0.98.

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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

We present an algorithm for explicitly computing the categorical (Drinfeld) center of a pivotal fusion category. Our approach is based on decomposing the images of simple objects under the induction functor from the category to its center. We have implemented this algorithm in a general-purpose software framework TensorCategories for tensor categories that we develop within the open-source computer algebra system OSCAR. We compute explicit models for the centers in form of the tuples $(X,\gamma)$ where $X$ is an object and $\gamma$ is a half-braiding. From these models we can compute the $F$-symbols and $R$-symbols. Using the data from the AnyonWiki, we were able to compute the center together with its $F$-symbols and $R$-symbols for all the 279 multiplicity-free fusion categories up to rank 5, and furthermore some chosen examples of rank 6, including the Haagerup subfactor (presented in a separate paper). Some of these centers yield new high-rank and not multiplicity-free examples of modular categories, e.g. rank 21 and multiplicity 2.

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