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Dataset . 2023
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Data sources: ZENODO
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ZENODO
Dataset . 2023
License: CC BY
Data sources: Datacite
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ZENODO
Dataset . 2023
License: CC BY
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ZENODO
Dataset . 2023
License: CC BY
Data sources: Datacite
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Photometric Completeness Modelled With Neural Networks

Authors: Harris, William E; Speagle, Joshua S;

Photometric Completeness Modelled With Neural Networks

Abstract

Neural networks associated with the paper "Photometric Completeness Modelled With Neural Networks" (Harris & Speagle 2023). Neural networks (`nn_clf_[...].joblib`) are included for all possible parameter combinations and trained over various numbers of artificial star tests (`ngc[...].dat`). See the example notebook (`nn_example.ipynb`) for detailed explanations of the files, their contents, and some usage examples.

Related Organizations
Keywords

globular clusters, completeness function, neural networks

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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!
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OpenAIRE UsageCountsViews provided by UsageCounts
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