{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/trap-a-temporal-systematics-model-for","title":"TRAP: A temporal systematics model for improved direct detection of exoplanets at small angular separations","arxiv_id":"2011.12311","date":"2020-11-24","proceeding":null,"authors":["M. Samland","J. Bouwman","D. W. Hogg","W. Brandner","T. Henning","M. Janson"],"abstract":"High-contrast imaging surveys for exoplanet detection have shown giant planets at large separations to be rare. It is important to push towards detections at smaller separations, the part of the parameter space containing most planets. The performance of traditional methods for post-processing of pupil-stabilized observations decreases at smaller separations, due to the larger field-rotation required to displace a source on the detector in addition to the intrinsic difficulty of higher stellar contamination. We developed a method of extracting exoplanet signals that improves performance at small angular separations. A data-driven model of the temporal behavior of the systematics for each pixel can be created using reference pixels at a different position, assuming the underlying causes of the systematics are shared across multiple pixels. This is mostly true for the speckle pattern in high-contrast imaging. In our causal regression model, we simultaneously fit the model of a planet signal \"transiting\" over detector pixels and non-local reference lightcurves describing a basis of shared temporal trends of the speckle pattern to find the best fitting temporal model describing the signal. With our implementation of a spatially non-local, temporal systematics model, called TRAP, we show that it is possible to gain up to a factor of 6 in contrast at close separations ($<3\\lambda/D$) compared to a model based on spatial correlations between images displaced in time. We show that better temporal sampling resulting in significantly better contrasts. At short integration times for $\\beta$ Pic data, we increase the SNR of the planet by a factor of 4 compared to the spatial systematics model. Finally, we show that the temporal model can be used on unaligned data which has only been dark and flat corrected, without the need for further pre-processing.","url_abs":"https://arxiv.org/abs/2011.12311v3","url_pdf":"https://arxiv.org/pdf/2011.12311v3.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"links_only","authors_date_abstract":"arXiv metadata, CC0 1.0 (https://info.arxiv.org/help/license), from the Kaggle arXiv metadata snapshot of 2026-09-12"},"code_links":[{"paper_slug":"trap-a-temporal-systematics-model-for","repo_url":"https://github.com/m-samland/trap","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2011.12311","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2011.12311"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. Samples come from repositories linked to the paper, official or community; repo_kind says which.","repos":[{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/m-samland/trap","reach":null}],"summary":{"ran_fixture":1},"by_repo_kind":{"official":{"samples":1,"ran":1,"repositories":1}},"repo_kind_vocabulary":{"official":"The archive marks this repository official for the paper","named_in_paper":"The archive records that the paper mentions this repository; it is not marked official","listed":"In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper","found_in_text":"Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted","community":"Not in the archive's code links for this paper; a community repository Syntology harvested"},"n_pointer_only_for_licence":0,"samples":[{"code_sha256_prefix":"fc8bb153d445a5b9","entry":"ratio_contrast_tables","repo":"m-samland/trap","repo_kind":"official","path":"src/trap/detection.py","file_url":"https://github.com/m-samland/trap/blob/HEAD/src/trap/detection.py","link_basis":"first_harvest_node","language":"python","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"well_formed","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"fc8bb153d445a5b9"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}