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  • NAM2021
    • Contacts
  • Science
    • Science Programme
    • Plenary Talks
    • Parallel Sessions
    • Special Lunches/Discussion Sessions
    • Poster Session
    • NAM Community Session
  • Social
    • Presidential Address
    • Herschel Concert
    • RAS Awards Ceremony
    • Virtual Stonehenge Tour
  • Media
  • Public Engagement
    • Public engagement opportunities
    • Public talk
    • Writing Skyscapes
  • Venue
    • Code of Conduct
    • Accessing the conference
    • Gather.town
    • NAM2021 Slack
    • About Bath

Poster

id
Maximum information retrieval from DESI spectra
AstroML
Matthew
Scourfield
Date Submitted
2021-04-14 13:14:00
University College London
M. Scourfield (University College London), K. Ganga (University College London), A. Saintonge (University College London), S. Viti (Leiden University)
The DESI survey will collect data on a large number of galaxies. We shall present our work on techniques to maximise the physical data retrieval for the purpose of galaxy evolution analysis. We focus on the use of machine learning algorithms as a means of intelligent noise reduction, primarily auto encoder methods. In addition, we make use of the latent space representations produced by such methods to automate the selection of similar spectra for stacking.

In anticipation of the DESI SV data we first train our models using SDSS spectra with high signal to noise, adding artificial noise to create a training set. Once the SV data are available we shall look into both training the model on the data, and also at transfer learning between the two datasets.

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