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Neural networks reconstruct spectral functions in Minkowski QED

By PulseAugur Editorial · [1 sources] ·

Researchers have utilized neural networks to reconstruct spectral functions in Minkowski quantum electrodynamics (QED), drawing inspiration from the dispersive formulation. The study involved solving the quenched rainbow QED Dyson-Schwinger equation and comparing results from neural reconstructions with free outputs against those with positivity-constrained ansätze. While free output neural networks successfully reproduced the expected zero crossing above the critical region, positivity-constrained models failed in the supercritical regime, suggesting spectral positivity should be a diagnostic rather than a blind constraint. AI

IMPACT Demonstrates a new method for applying neural networks to complex physics problems, potentially advancing scientific discovery.

RANK_REASON The cluster contains an academic paper detailing a novel application of neural networks to a physics problem. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Neural networks reconstruct spectral functions in Minkowski QED

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The cluster contains an academic paper detailing a novel application of neural networks to a physics problem. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Rodrigo Carmo Terin ·

    Spectral functions in Minkowski quantum electrodynamics from neural reconstruction

    arXiv:2510.24728v2 Announce Type: replace-cross Abstract: We study neural reconstructions of quenched rainbow quantum electrodynamics (QED) Dyson--Schwinger benchmarks in Minkowski-related kinematics. Using the dispersive formulation as motivation, we separate the Euclidean Fukud…

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