  DESI is a groundbreaking international project to observe more than 40
million quasars and galaxies over a 5-year period to create a 3D map of the
sky. This map will enable us to probe multiple aspects of cosmology, from dark
energy to neutrino mass. We are focusing here on one type of object observed by
DESI, the Lyman Break Galaxies (LBGs). The aim is to use their spectra to
determine whether they are indeed LBGs, and if so, to determine their distance
from the Earth using a phenomenon called redshift. This will enable us to place
these galaxies on the DESI 3D map.
  The aim is therefore to develop a convolutional neural network (CNN) inspired
by QuasarNET (See arXiv:1808.09955), performing simultaneously a classification
(LBG type or not) and a regression task (determine the redshift of the LBGs).
Initially, data augmentation techniques such as shifting the spectra in
wavelengths, adding noise to the spectra, or adding synthetic spectra were used
to increase the model training dataset from 3,019 data to over 66,000. In a
second phase, modifications to the QuasarNET architecture, notably through
transfer learning and hyperparameter tuning with Bayesian optimization, boosted
model performance.
  Gains of up to 26% were achieved on the Purity/Efficiency curve, which is
used to evaluate model performance, particularly in areas with interesting
redshifts, at low (around 2) and high (around 4) redshifts. The best model
obtained an average score of 94%, compared with 75% for the initial model.
