On the Brown measure of $x + i y$, with $x,y$ selfadjoint and $y$ free Poisson
Bibliographic record
Abstract
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 $\mathrm{cl}(\mathcal{M})\cup\{α_1,\ldots,α_k\}$.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".