{"paper":{"title":"Detrending algorithms in large time-series: Application to TFRM-PSES data","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["astro-ph.EP"],"primary_cat":"astro-ph.IM","authors_text":"A. Rosich, D. del Ser, H. Voss, J. N\\'u\\~nez, O. Fors, V. Kouprianov","submitted_at":"2014-11-19T19:15:33Z","abstract_excerpt":"Certain instrumental effects and data reduction anomalies introduce systematic errors in photometric time-series. Detrending algorithms such as the Trend Filtering Algorithm (TFA) (Kov\\'{a}cs et al. 2004) have played a key role in minimizing the effects caused by these systematics. Here we present the results obtained after applying the TFA, Savitszky-Golay (Savitzky & Golay 1964) detrending algorithms and the Box Least Square phase folding algorithm (Kov\\'{a}cs et al. 2002) to the TFRM-PSES data (Fors et al. 2013). Tests performed on this data show that by applying these two filtering methods"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1411.5320","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":""},"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"}