{"paper":{"title":"Hypernuclei with Neural Network Quantum States","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"nucl-th","authors_text":"Alessandro Lovato, Andrea Di Donna, Francesco Pederiva, Lorenzo Contessi","submitted_at":"2025-07-22T20:05:55Z","abstract_excerpt":"Leveraging complementary machine-learning-based approaches, we compute properties of $s$- and $p$-shell $\\Lambda$ hypernuclei - including binding energies, single-particle densities, and radii - starting from the individual interactions among their constituents. These interactions are modeled using an improved leading-order pionless effective field theory expansion, with coefficients determined via a Gaussian Process framework anchored on virtually exact few-body techniques. We solve the many-body Schr\\\"odinger equation using a variational Monte Carlo method based on neural network quantum sta"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2507.16994","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2507.16994/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"}