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| Auteurs principaux: | , , , |
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| Format: | Preprint |
| Publié: |
2026
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| Accès en ligne: | https://arxiv.org/abs/2604.18673 |
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| _version_ | 1866917423628681216 |
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| author | Ghosh, Kausik Kumar, Sidhaarth Niarchos, Vasilis Stergiou, Andreas |
| author_facet | Ghosh, Kausik Kumar, Sidhaarth Niarchos, Vasilis Stergiou, Andreas |
| contents | We propose that simple neural networks (NNs) trained on crossing symmetry can reconstruct conformal correlators restricted to a line to remarkable accuracy. The input is minimal: an external scaling dimension, a spectral gap, and the value of the correlator at a single point. We present evidence across a wide range of conformal theories and dimensions, for both four-point and thermal two-point functions. We attribute these observations to the spectral bias of gradient-based NN training, which appears to align with an intrinsic smoothness property of conformal field theory. This suggests a novel variational principle for conformal correlators and opens a path towards a powerful new computational framework for non-perturbative quantum field theory. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_18673 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Neural Networks Reveal a Universal Bias in Conformal Correlators Ghosh, Kausik Kumar, Sidhaarth Niarchos, Vasilis Stergiou, Andreas High Energy Physics - Theory We propose that simple neural networks (NNs) trained on crossing symmetry can reconstruct conformal correlators restricted to a line to remarkable accuracy. The input is minimal: an external scaling dimension, a spectral gap, and the value of the correlator at a single point. We present evidence across a wide range of conformal theories and dimensions, for both four-point and thermal two-point functions. We attribute these observations to the spectral bias of gradient-based NN training, which appears to align with an intrinsic smoothness property of conformal field theory. This suggests a novel variational principle for conformal correlators and opens a path towards a powerful new computational framework for non-perturbative quantum field theory. |
| title | Neural Networks Reveal a Universal Bias in Conformal Correlators |
| topic | High Energy Physics - Theory |
| url | https://arxiv.org/abs/2604.18673 |