Pith. sign in

REVIEW 1 cited by

Two Matrix Model, the Riemann Hypothesis and Master Matrix Obstruction

Not yet reviewed by Pith; the record is open.

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2305.14664 v3 pith:AYLVE4EZ submitted 2023-05-24 math.NT hep-thmath-phmath.MP

Two Matrix Model, the Riemann Hypothesis and Master Matrix Obstruction

classification math.NT hep-thmath-phmath.MP
keywords matrixmodelzeroscriticalinfinitylinemastergoes
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
0 comments
read the original abstract

We identify the Riemann Xi function as the Baker-Akhiezer function for a (p,1) two matrix model as p goes to infinity. We solve the two matrix model using biorthogonal polynomials and study the zeros of the polynomials in the double scaling limit as N goes to infinity. We find zeros off the critical line at finite N which possibly go to infinity as N goes to infinity. We study other Baker-Akhiezer functions whose zeros are known to be on a critical line using the two matrix model technique and find the zeros on the critical line in those cases. We study other L-functions using the two matrix model and compare the biorthogonal method with other approaches to the two matrix model such as the master matrix approach and saddle point method. In cases where there are zeros off the critical line the master matrix approach encounters an obstruction to the solution to a quenched master matrix.

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. Weighted Contrastive Learning for Anomaly-Aware Time-Series Forecasting

    cs.LG 2025-12 conditional novelty 4.0

    WECA, a contrastive loss that aligns normal and anomaly-augmented windows with severity-dependent weights, cuts anomaly-period SMAPE by 6.1 points on an ATM forecast benchmark with minimal normal-data loss.