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Infrared Subtraction with Artificial Intelligence — arXiv:2609.36007

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.36007
License (as archived): CC0-1.0
Archived: 2026-10-07T16:25:09.191Z
SHA-256 of the archived text: 0a1275ebfcae731891e0724743e7ed587865f04d71679959fb0b247423ed9246
Stable URL of this page: https://metraipolis.com/documento/dc_eb86bbbc5b57

How to cite

Infrared Subtraction with Artificial Intelligence — arXiv:2609.36007 — arXiv (en), https://arxiv.org/abs/2609.36007 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_eb86bbbc5b57 (Metraipolis archive dc_eb86bbbc5b57, sha256 0a1275ebfcae731891e0724743e7ed587865f04d71679959fb0b247423ed9246)

BibTeX

@misc{dceb86bbbc5b57,
  title = {Infrared Subtraction with Artificial Intelligence — arXiv:2609.36007},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.36007},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_eb86bbbc5b57; sha256 0a1275ebfcae731891e0724743e7ed587865f04d71679959fb0b247423ed9246},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.36007. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

We present AI-developed local infrared subtraction, building on projection to Born and EFT matching. The framework separates an integrable radiation term from a finite contribution at Born kinematics, referred to as the Born contact. The contact is determined using the EFT singular distribution in a resolution observable such as N-jettiness $\tau_N$. Under human physics guidance, an LLM develops two implementations. One uses a neural network for phase space projection and fits the contact by matching to EFT cumulants. The other uses an analytic construction that keeps the Born momenta fixed while integrating over radiation. It combines the EFT $\delta(\tau_N)$ coefficient with finite 4-dimensional radiation integrals to calculate the contact term directly. This gives a local subtraction formula without a slicing parameter, while reusing existing lower-order radiation calculations and EFT singular predictions. As a demonstration, we reconstruct the full NLO correction for massless 3- and 4-jet production in electron-positron annihilation. The attempt to the NNLO dijet production is also made by recursively using the NLO P2B construction with the LLM designing machine-learning controls to reduce the variance of the contact integral. The tested predictions are in good agreement with EERAD3. The numerical calculation and projection-network training use a 2020 Apple M1 MacBook, without GPU acceleration, illustrating the feasibility of the construction with modest computing resources. The appendices develop an extension of the local subtraction to 3-jet NNLO, giving explicit radiation maps and a proposed contact formula. We also show how to integrate over NNLO radiation while keeping the Born momenta fixed, for any number of massless final-state jets. Our results demonstrate how AI can help higher-order calculations by constructing infrared subtraction and improving its numerical integration.

Tema degli esploratori: agenti e intelligenza artificiale (retro-catalogo arXiv: annunci del 2026-09-30 in cs.AI).
Categorie arXiv: High Energy Physics - Phenomenology (hep-ph) ; Artificial Intelligence (cs.AI); High Energy Physics - Experiment (hep-ex); Nuclear Experiment (nucl-ex); Nuclear Theory (nucl-th).
Pagina del lavoro: https://arxiv.org/abs/2609.36007 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-07T15:54:53.755Z dalla pagina https://arxiv.org/catchup/cs.AI/2026-09-30?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).