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3D Detection of ALMA Sources Through Deep Learning

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Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD 2022)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1752))

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Abstract

We present a Deep Learning pipeline for the detection of astronomical sources within radiointerferometric simulated data cubes. Our pipeline is constituted by two Deep Learning models: a Convolutional Autoencoder for the detection of sources within the spatial domain of the cube, and a RNN for the denoising and detection of emission peaks in the frequency domain. The combination of spatial and frequency information allows for higher completeness and helps to remove false positives. The pipeline has been tested on simulated ALMA observations achieving better performances and faster execution times with respect to traditional methods. The pipeline can detect \(92\%\) of sources up to a flux of 1.31 Jy/beam with no false positives thus providing a reliable source detection solution for future astronomical radio surveys.

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Authors and Affiliations

  1. INFN Section of Naples, via Cintia, 1, 80126, Naples, Italy

    Michele Delli Veneri

  2. Department of Electrical Engineering and Information Technology, University of Naples “Federico II”, Via Claudio, 21, 80125, Naples, NA, Italy

    Michele Delli Veneri

  3. ESO, Karl-Schwarzschild-Straße 2, 85748, Garching bei München, Germany

    Lukasz Tychoniec, Fabrizia Guglielmetti & Eric Villard

  4. Department of Physics “Ettore Pancini”, University of Naples “Federico II”, Via Cintia, 1, 80126, Naples, Italy

    Giuseppe Longo

Authors
  1. Michele Delli Veneri
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  2. Lukasz Tychoniec
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  3. Fabrizia Guglielmetti
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  4. Eric Villard
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  5. Giuseppe Longo
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Corresponding author

Correspondence to Michele Delli Veneri .

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Editors and Affiliations

  1. University of Sydney, Sydney, Australia

    Irena Koprinska

  2. University of Bari Aldo Moro, Bari, Italy

    Paolo Mignone

  3. University of Pisa, Pisa, Italy

    Riccardo Guidotti

  4. Warsaw University of Technology, Warsaw, Poland

    Szymon Jaroszewicz

  5. Heidelberg University, Heidelberg, Germany

    Holger Fröning

  6. UniCredit, Rome, Italy

    Francesco Gullo

  7. University of Lisbon, Lisbon, Portugal

    Pedro M. Ferreira

  8. Roche, Basel, Switzerland

    Damian Roqueiro

  9. Barcelona Supercomputing Center, Barcelona, Spain

    Gaia Ceddia

  10. Halmstad University, Halmstad, Sweden

    Slawomir Nowaczyk

  11. University of Porto, Porto, Portugal

    João Gama

  12. University of Porto, Porto, Portugal

    Rita Ribeiro

  13. UPC BarcelonaTech, Barcelona, Spain

    Ricard Gavaldà

  14. University of Naples Federico II, Naples, Italy

    Elio Masciari

  15. University of North Carolina, Charlotte, USA

    Zbigniew Ras

  16. ICAR-CNR, Rende, Italy

    Ettore Ritacco

  17. University of Pisa, Pisa, Italy

    Francesca Naretto

  18. Aalen University of Applied Sciences, Aalen, Germany

    Andreas Theissler

  19. Warsaw University of Technology, Warszaw, Poland

    Przemyslaw Biecek

  20. KU Leuven, Leuven, Belgium

    Wouter Verbeke

  21. University of Duisburg-Essen, Essen, Germany

    Gregor Schiele

  22. Graz University of Technology, Graz, Austria

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  23. AMD, Dublin, Ireland

    Michaela Blott

  24. UniCredit, Rome, Italy

    Ilaria Bordino

  25. UniCredit, Milan, Italy

    Ivan Luciano Danesi

  26. National Agency for New Technologies, Rome, Italy

    Giovanni Ponti

  27. Unicredit, Rome, Italy

    Lorenzo Severini

  28. University of Bari Aldo Moro, Bari, Italy

    Annalisa Appice

  29. University of Bari Aldo Moro, Bari, Italy

    Giuseppina Andresini

  30. University of Lisbon, Lisbon, Portugal

    Ibéria Medeiros

  31. University of Lisbon, Lisbon, Portugal

    Guilherme Graça

  32. Northwestern University, Chicago, USA

    Lee Cooper

  33. Roche, Basel, Switzerland

    Naghmeh Ghazaleh

  34. University of Lausanne, Lausanne, Switzerland

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  35. Novartis, Basel, Switzerland

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  36. Novartis, Basel, Switzerland

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  37. Fondazione IRCCS Ca’ Granda Ospedale Maggiore Policlinico, Milan, Italy

    Arif Canakoglu

  38. Politecnico di Milano, Milan, Italy

    Sara Pido

  39. Politecnico di Milano, Milan, Italy

    Pietro Pinoli

  40. University of Waikato, Hamilton, New Zealand

    Albert Bifet

  41. Halmstad University, Halmstad, Sweden

    Sepideh Pashami

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Veneri, M.D., Tychoniec, L., Guglielmetti, F., Villard, E., Longo, G. (2023). 3D Detection of ALMA Sources Through Deep Learning. In: Koprinska, I., et al. Machine Learning and Principles and Practice of Knowledge Discovery in Databases. ECML PKDD 2022. Communications in Computer and Information Science, vol 1752. Springer, Cham. https://doi.org/10.1007/978-3-031-23618-1_19

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  • DOI: https://doi.org/10.1007/978-3-031-23618-1_19

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-23617-4

  • Online ISBN: 978-3-031-23618-1

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