{"paper":{"title":"Are PTA measurements sensitive to gravitational wave non-Gaussianities?","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"PTA data after decorrelation cannot distinguish Gaussian from non-Gaussian gravitational wave backgrounds without strong assumptions.","cross_cats":["astro-ph.HE","gr-qc","hep-ph"],"primary_cat":"astro-ph.CO","authors_text":"Chiara Cecchini, Gabriele Franciolini, Jonas El Gammal, Mauro Pieroni","submitted_at":"2026-05-06T17:28:29Z","abstract_excerpt":"Observing non-Gaussianities in the timing residuals of Pulsar Timing Arrays (PTAs) has recently attracted attention as a potential discriminator between astrophysical and cosmological origins of the observed Gravitational Wave (GW) signal. In this work, we show that even in an idealized signal-dominated setup, after decorrelating the data to avoid spurious detections, statistical tests applied to PTA data cannot distinguish between a Gaussian and a non-Gaussian amplitude distribution of the GWB in a model-agnostic way. In particular, without making strong assumptions on the GW spectrum or the "},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"even in an idealized signal-dominated setup, after decorrelating data to avoid spurious detections, statistical tests applied to PTA data cannot distinguish between Gaussian and non-Gaussian GWBs in a model-agnostic way. In particular, without making strong assumptions on the GW spectrum or the properties of the population, the sensitivity to any distinctive non-Gaussian feature is washed out.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"The conclusion depends on an idealized signal-dominated setup together with the assumption that decorrelation fully removes spurious correlations without erasing genuine non-Gaussian information; the result is stated to hold only in the absence of strong prior assumptions on spectrum or population.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"PTA statistical tests lose sensitivity to non-Gaussian GW features after decorrelation and cannot distinguish them model-agnostically.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"PTA data after decorrelation cannot distinguish Gaussian from non-Gaussian gravitational wave backgrounds without strong assumptions.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"9be919cf6688fedb40af657c726a850872cd1e77b902147bff6e5bc69c59557c"},"source":{"id":"2605.05157","kind":"arxiv","version":2},"verdict":{"id":"e0b94320-e6a1-4dde-9029-a0488325be4b","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-08T16:33:13.425463Z","strongest_claim":"even in an idealized signal-dominated setup, after decorrelating data to avoid spurious detections, statistical tests applied to PTA data cannot distinguish between Gaussian and non-Gaussian GWBs in a model-agnostic way. In particular, without making strong assumptions on the GW spectrum or the properties of the population, the sensitivity to any distinctive non-Gaussian feature is washed out.","one_line_summary":"PTA statistical tests lose sensitivity to non-Gaussian GW features after decorrelation and cannot distinguish them model-agnostically.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"The conclusion depends on an idealized signal-dominated setup together with the assumption that decorrelation fully removes spurious correlations without erasing genuine non-Gaussian information; the result is stated to hold only in the absence of strong prior assumptions on spectrum or population.","pith_extraction_headline":"PTA data after decorrelation cannot distinguish Gaussian from non-Gaussian gravitational wave backgrounds without strong assumptions."},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2605.05157/integrity.json","findings":[],"available":true,"detectors_run":[{"name":"ai_meta_artifact","ran_at":"2026-05-20T10:36:01.440686Z","status":"completed","version":"1.0.0","findings_count":0},{"name":"doi_title_agreement","ran_at":"2026-05-19T21:01:19.808856Z","status":"completed","version":"1.0.0","findings_count":0},{"name":"doi_compliance","ran_at":"2026-05-19T13:48:04.414689Z","status":"completed","version":"1.0.0","findings_count":0}],"snapshot_sha256":"aa5290365c30f255eb6cfe6fe97aa49c80b08efb4d727fdd5a29f0f366ce3e8e"},"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"}