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ZENODO
Dataset . 2025
License: CC BY
Data sources: ZENODO
ZENODO
Dataset . 2025
License: CC BY
Data sources: Datacite
ZENODO
Dataset . 2025
License: CC BY
Data sources: Datacite
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ExoMiner++: Enhanced Transit Classification and a New Vetting Catalog for 2-Minute TESS Data

Authors: Martinho, Miguel; Valizadegan, Hamed;

ExoMiner++: Enhanced Transit Classification and a New Vetting Catalog for 2-Minute TESS Data

Abstract

Summary This data repository is associated with the work presented in paper "ExoMiner++: Enhanced Transit Classification and a New Vetting Catalog for 2-Minute TESS Data" . Contents exominerplusplus_catalog_unk_tces_s1-s67_tess-spoc-2min_complete_1-16-2025_1014.csv: vetting catalog of unlabeled (UNK) TESS SPOC 2-min TCEs for sector runs S1-S67 (included multi-sector runs) produced using the ExoMiner++ models trained for the experiment "TESS+Kepler" (see Table 6 in paper). A shorter catalog is available online here (TCEs with score < 0.1 are excluded due to memory constraints). exominerplusplus_catalog_labeled_tces_s1-s67_tess-spoc-2min_complete_1-14-2025_1039.csv: catalog for the labeled TESS SPOC 2-min TCEs for sector runs S1-S67 (included multi-sector runs) dataset used to train the models in the experiment "TESS+Kepler" (see Table 6 in paper). exominer_plusplus_architecture.png: image of ExoMiner++ architecture. tfrecords_tess-spoc-2min_s1-s67_9-24-2024_1159.tar.xz: a compressed file of the TESS dataset used to train and evaluate the models in the paper (does not include the Kepler data). The examples are split across multiple files (aka shards) in TFRecord format. This dataset was used as source for the creation of the cross-validation dataset used in the paper. The features are unnormalized (at least the ones that require normalization using training set statistics). You would first split the dataset into, for example, a single train-val-test split OR into multiple cross-validation folds; then compute normalization statistics; and then normalize the features accordingly. This dataset includes both unlabeled examples (aka UNK) and the remaining labeled ones (all other labels) - see feature attribute 'label' after parsing an example from the TFRecord dataset. Includes an auxiliary table named "shards_tbl.csv" that describes the set of examples in the TFRecord dataset, with information about which file they are stored in (column 'shard'), and their order in the shard file (column 'example_i_tfrec'). Related Software These data are associated with version v1.0.0 (ExoMiner++) of ExoMiner found under NASA GitHub.

Related Organizations
Keywords

Machine Learning, tess, Exoplanetology, Artificial Intelligence, Astronomy, Machine Learning/classification, Photometry/classification, exoplanet, Supervised Machine Learning, kepler

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
1
Average
Average
Average