{"paper":{"title":"Optimising transient discovery with Swift-XRT","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["astro-ph.IM"],"primary_cat":"astro-ph.HE","authors_text":"M. R. Goad, P. A. Evans, R. A. J. Eyles-Ferris, S. Srivastava","submitted_at":"2026-06-29T11:20:14Z","abstract_excerpt":"The Living Swift-XRT Point Source Catalogue (LSXPS) enables near real-time searches for X-ray transients. Many detected candidates are faint, often near the XRT detection limit, and are classed as \"low significance,\" as it is often unclear whether their apparent brightening reflects a genuine transient or a statistical fluctuation. Some of these sources are affected by Eddington bias, a statistical effect that inflates measured fluxes near the detection threshold. We present a simulation-based Bayesian framework that corrects for this bias and provides more accurate probabilities for each sour"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.30141","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/2606.30141/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"}