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Record W7117851991

On the Brown measure of $x + i y$, with $x,y$ selfadjoint and $y$ free Poisson

2025· article· W7117851991 on OpenAlexfundno aff
Franz Lehner, Alexandru Nica, Kamil Szpojankowski, Ping Zhong

Bibliographic record

VenueArXiv.org · 2025
Typearticle
Language
FieldMathematics
TopicRandom Matrices and Applications
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of CanadaNarodowym Centrum NaukiAustrian Science FundNational Science Foundation
KeywordsMeasure (data warehouse)Poisson distributionAbsolute continuityInverseDistribution (mathematics)Function (biology)Limit (mathematics)Probability density functionDistribution function
DOInot available

Abstract

fetched live from OpenAlex

Let $x,y$ be freely independent selfadjoint elements in a $W^{*}$-probability space, where $y$ has free Poisson distribution of parameter $p$. We pursue a methodology for computing the Brown measure of $x + i y$, which relies on the matrix-valued subordination function $Ω$ of the hermitization of $x + i y$, and on the fact that $Ω$ has an explicitly described left inverse $H$. Our main point is that the Brown measure of $x + i y$ becomes more approachable when it is reparametrized via a certain change of variable $h : \mathcal{D} \to \mathcal{M}$, with $\mathcal{D}, \mathcal{M}$ open subsets of $\mathbb{C}$, where $\\mathcal{D}$ and $h$ are defined in terms of the aforementioned left inverse $H$, and $\mathrm{cl} \,(\mathcal{M})$ contains the support of the absolutely continuous part of Brown measure. More precisely, we find (with some conditions on the distribution of $x$) the following formula: \[ f(s + i \, t) =\frac{1}{2 π}\left[\frac{1}{t}\left(\frac{\partial α}{\partial s} +\frac{\partial β}{\partial t}\right)-\frac{1}{t}-\fracβ{t^2}\right], \ \ s + i \, t \in \mathcal{M}, \] where $f$ is the density of the absolutely continuous part of the Brown measure and the functions $α, β: \mathcal{M} \to \mathbb{R}$ are the real and respectively the imaginary part of $h^{-1}$. We show that if $x$ has an atom $α$ with $μ_x(α)>p$, then the Brown measure of $x+iy$ has an atom of mass $μ_x(α)-p$ at the same point $α$. Moreover we prove that if $α_1,\ldots,α_k$ is the list of atoms of $x$ with mass bigger than $p$, then the Brown measure of $x+iy$ is supported on $\mathrm{cl}(\mathcal{M})\cup\{α_1,\ldots,α_k\}$.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.007
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.001
Science and technology studies0.0020.008
Scholarly communication0.0030.005
Open science0.0020.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0060.001

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 head0.033
GPT teacher head0.261
Teacher spread0.228 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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
Published2025
Admission routes1
Has abstractyes

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Same venueArXiv.orgSame topicRandom Matrices and ApplicationsFrench-language works237,207