Minutes

FZJ-2024-06463

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png Galaxy Morphology Classification with Counterfactual Explanation

Cao, Z.FZJ* ; Krieger, L.FZJ* ; Scharr, H.FZJ* ; Assent, I.FZJ*

2024

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Please use a persistent id in citations: doi:10.34734/FZJ-2024-06463

Abstract: Galaxy morphologies play an essential role in the study of the evolution of galaxies. The determination of morphologies is laborious for a large amount of data giving rise to machine learning-based approaches. Unfortunately, most of these approaches offer no insight into how the model works and make the results difficult to understand and explain. We here propose to extend a classical encoder-decoder architecture with invertible flow, allowing us to not only obtain a good predictive performance but also provide additional information about the decision process with counterfactual explanations.

Keyword(s): Others (2nd)

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Contributing Institute(s):

1. Datenanalyse und Maschinenlernen (IAS-8)

Research Program(s):

1. 5112 - Cross-Domain Algorithms, Tools, Methods Labs (ATMLs) and Research Groups (POF4-511) (POF4-511)

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Database coverage:

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