Transit-APP: A Centroid-free Method of Identifying Background Transit False Positives

Abstract We present a centroid-free method to identify which star is the source of a known transit signal from pixel data. This method is based on observed and simulated difference images, and provides a relative probability of a star being the source of the transit compared to other known stars. This method is robust at low S/N compared to common centroid-based methods, and is well suited for space-based transit surveys such as Kepler and TESS.

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Publication Details

Journal
Research Notes of the AAS
Published
2025-04-14
DOI
https://doi.org/10.3847/2515-5172/adcb3a
Citations
1
Primary Topic
Bayesian Modeling and Causal Inference
Type
article
Field-Weighted Citation Impact
2.48
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article

Transit-APP: A Centroid-free Method of Identifying Background Transit False Positives

Michelle Kunimoto, Steve Bryson
1 citations
Research Notes of the AAS
Bayesian Modeling and Causal Inference
2.48
article

Transit-APP: A Centroid-free Method of Identifying Background Transit False Positives

Michelle Kunimoto, Steve Bryson
article en
1 citations

Abstract

Abstract We present a centroid-free method to identify which star is the source of a known transit signal from pixel data. This method is based on observed and simulated difference images, and provides a relative probability of a star being the source of the transit compared to other known stars. This method is robust at low S/N compared to common centroid-based methods, and is well suited for space-based transit surveys such as Kepler and TESS.

Research Notes of the AASVol. 9(4)
Ames Research Center (US), University of British Columbia (CA)
Openalex Percentile: Top 11%
Bayesian Modeling and Causal Inference
2.48
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Transit-APP: A Centroid-free Method of Identifying Background Transit False Positives — Michelle Kunimoto, Steve Bryson · Research Notes of the AAS (2025) | TGRS Research Map | TGRS