RAVEN: RAnking and Validation of ExoplaNets

Clicks: 10
ID: 332828
2026
Free PDF
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This article has not been analysed, so there is no overall score — reader engagement is measured and shown alongside.
AI Quality Assessment
Not analyzed
Readership in this journal
Emerging

Ranked #39 of 51 articles by views in RAS Techniques and Instruments

Most read Least read

Bar heights use a square-root scale.

Mint this article as an NFT
Not yet minted

Create a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.

5 SUSD one-off · no wallet required
Sign in to mint
Abstract
Abstract We present RAVEN, a vetting and validation pipeline for TESS exoplanet candidates. RAVEN employs Gradient Boosted Decision Trees and Gaussian Process classifiers in a Bayesian framework to derive the posterior probability of a candidate being a planet against a set of False Positive (FP) scenarios. The classifiers are trained on synthetic training sets of planets and 8 astrophysical FP scenarios injected into TESS lightcurves, allowing for large-scale candidate vetting and performance verification against individual FP scenarios. A training set consisting of real TESS candidates caused primarily by stellar variability and systematic noise is also included. The machine learning probabilities are combined with scenario specific priors to compute the final posterior probabilities. These probabilities can be used for vetting candidates up to a radius of 16R⊕ and ranking them for follow-up. Candidates with probabilities greater than 99% against each FP scenario and whose implied radius is less than 8R⊕ are considered to be statistically validated by the pipeline. In this first version, RAVEN has been developed for candidates with a lightcurve from the TESS Science Processing Operations Centre, orbital period between 0.5 and 16 days and transit depth greater than 300ppm. RAVEN obtained area-under-curve (AUC) scores >97% on all FP scenarios and >99% on all but one. Testing on an independent external sample of 1361 pre-classified TOIs, the pipeline achieved an overall accuracy of 91%, with a precision of 97% and a recall score of 66%. RAVEN is publicly released as a cloud-hosted app, allowing easy access to the community.
Reference Key
openalex_W4415255031 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Andreas Hadjigeorghiou, David J. Armstrong, Kaiming Cui, Marina Lafarga, Luis Agustín Nieto, R. F. Díaz, Lauren Doyle, Vedad Kunovac
Are you one of these authors? Claim your profile
Journal RAS Techniques and Instruments
Year 2026
DOI
10.1093/rasti/rzag080
URL
https://doi.org/10.1093/rasti/rzag080
Keywords Keywords not found

Citations

No citations found. To add a citation, contact the admin at info@scimatic.org

Comments

No comments yet. Be the first to comment on this article.