Latent Space Explorer: Unsupervised Data Pattern Discovery on the Cloud

Extracting information from raw data is probably one of the central activities of experimental scientific enterprises. This work is about a pipeline in which a specific model is trained to provide a compact, essential representation of the training data, useful as a starting point for visualization...

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Bibliographic Details
Main Authors Cecconello, T, Bordiu, C, Bufano, F, Puerari, L, Riggi, S, Schisano, E, Sciacca, E, Maruccia, Y, Vizzari, G
Format Journal Article
LanguageEnglish
Published 29.04.2022
Subjects
Physics - Instrumentation and Methods for Astrophysics
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Summary:Extracting information from raw data is probably one of the central activities of experimental scientific enterprises. This work is about a pipeline in which a specific model is trained to provide a compact, essential representation of the training data, useful as a starting point for visualization and analyses aimed at detecting patterns, regularities among data. To enable researchers exploiting this approach, a cloud-based system is being developed and tested in the NEANIAS project as one of the ML-tools of a thematic service to be offered to the EOSC. Here, we describe the architecture of the system and introduce two example use cases in the astronomical context.
DOI:10.48550/arxiv.2204.13933