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Astrophysics > Instrumentation and Methods for Astrophysics

arXiv:2204.13933 (astro-ph)
[Submitted on 29 Apr 2022]

Title:Latent Space Explorer: Unsupervised Data Pattern Discovery on the Cloud

Authors:T. Cecconello, C. Bordiu, F. Bufano, L. Puerari, S. Riggi, E. Schisano, E. Sciacca, Y. Maruccia, G. Vizzari
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Abstract: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.
Comments: 4 pages, 3 figures, proceedings of ADASS XXXI conference, to be published in ASP Conference Series
Subjects: Instrumentation and Methods for Astrophysics (astro-ph.IM)
Cite as: arXiv:2204.13933 [astro-ph.IM]
  (or arXiv:2204.13933v1 [astro-ph.IM] for this version)
  https://doi.org/10.48550/arXiv.2204.13933

Submission history

From: Cristobal Bordiu [view email]
[v1] Fri, 29 Apr 2022 08:14:47 UTC (2,203 KB)
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