Pattern recognition in multi wavelength astronomical data in the context of the SKA and its pathfinders

Tuesday, 19th of January 2021

I. Participants:

  • Benoit Semelin
  • Philippe Zarka
  • Françoise Combes
  • Martin Hardcastle
  • Kevin Schmidt
  • Francesco De Gasperin
  • Rafaël Mostert
  • Beatriz Mingo
  • Lara Alegre
  • Judith Croston
  • Philippe Salomé
  • Philip Best
  • Kushatha Ntwaetsile
  • Joshua Albert
  • Erik Osinga
  • Cyril Tasse
  • David Cornu
  • Baptiste Cecconi
  • Laurent Lamy
  • Jurjen de Jong
  • Bonny Barkus
  • Alan Loh
  • Ulrich A. Mbou Sob
  • Huub Rottgering
  • Stéphane Corbel

II. Program:

(CET times)14:00-14:10:
Welcome / Round table
14:10-15:30 Talks
15:40-17:00 Discussion

III. Talks: [7 minutes talk, 3 minutes questions]

  • David Cornu: 3D extinction mapping of the Milky Way using CNNs
  • Philippe Salomé : Yafits – A remote radio-data viewer – Demo (if people interested)
  • Bonny Barkus: automated host identification and classification of extended radio sources – Part I: Intro and Ridgelines
  • Beatriz Mingo: automated host identification and classification of extended radio sources – Part II: Morphological Classification + Conclusions
  • Lara Alegre: Classification of LOFAR radio sources with machine learning. 
  • Rafaël Mostert: Radio component association using r-CNNs and optical cross-identification using CNNs.
  • Kevin Schmidt: Deep Learning Approaches to Improve Imaging
  • Kushatha Ntwaetsile: Rapid sorting of radio galaxy morphology using Haralick Features
  • Sydil Kupa : Automating Image segmentation using CNN for MeerKAT Radio Galaxy Images

IV. Discussion

1. Machine Learning approaches:

1.1 Radio source morphology

  • In the context of morphological classification: Are there rare types of objects and what is the dilution factor with frequent types of objects in our datasets ? Possible discussion on how to properly evaluate the effect of such objects in terms of prediction purity (proportion of false positives).
  • How to handle morphological degeneracy (when there is some) ?

1.2 Multiwavelength morphology

  • What would be the most efficient ML approaches for hyperspectral surveys ?
  • Would it be possible to extract characteristic morphological features of a given class of object using generative models ? (e.g GAN)

1.3 Machine learning with Lofar Galaxy Zoo

  • Given the data that we have from the first year of Lofar Galaxy Zoo, can we better select which sources should not go into the Zoo? 
  • Related: Can we use the LGZ data to train a ML algorithm to do the task, and then in cases where it is uncertain we put those subjects into the project. 

2. Applications:

2.1 LoTSS:

  • Where do we stand? Where do we go?

2.2 SKA challenges :

  • CNN architectures for object detection
  • Data pre-processing & augmentation
  • Transfer learning from other radioastronomical surveys

3. Interpretation of the results?

  • How to avoid falling into the usual traps ( e.g. resolution/sensitivity limits in different surveys yielding different results for the same source, overinterpretation of the results, interpretation within schemes that are now obsolete…)?
  • How can we make sure that we don’t cause more problems than we fix?
  • Using joint approaches for different surveys (e.g. SPARCS)?

4. Research program / Potential collaborations?

  • Sharing astro dedicated ML methods tuning (e.g. dedicated ANN architectures modification sharing)
  • Exploration of transfer learning between applications and surveys (Existing githubs?, test datasets?)