BibTeX

@article{2302.04324v1,
Author        = {Ofer Lahav},
Title         = {Deep Machine Learning in Cosmology: Evolution or Revolution?},
Eprint        = {2302.04324v1},
ArchivePrefix = {arXiv},
PrimaryClass  = {astro-ph.CO},
Abstract      = {Could Machine Learning (ML) make fundamental discoveries and tackle unsolved
problems in Cosmology? Detailed observations of the present contents of the
universe are consistent with the Cosmological Constant Lambda and Cold Dark
Matter model, subject to some unresolved inconsistencies ('tensions') among
observations of the Hubble Constant and the clumpiness factor. To understand
these issues further, large surveys of billions of galaxies and other probes
require new statistical approaches. In recent years the power of ML, and in
particular 'Deep Learning', has been demonstrated for object classification,
photometric redshifts, anomaly detection, enhanced simulations, and inference
of cosmological parameters. It is argued that the more traditional 'shallow
learning' (i.e. with pre-processing feature extraction) is actually quite deep,
as it brings in human knowledge, while 'deep learning' might be perceived as a
black box, unless supplemented by explainability tools. The 'killer
applications' of ML for Cosmology are still to come. New ways to train the next
generation of scientists for the Data Intensive Science challenges ahead are
also discussed. Finally, the chatbot ChatGPT is challenged to address the
question posed in this article's title.},
Year          = {2023},
Month         = {Feb},
Url           = {http://arxiv.org/abs/2302.04324v1},
File          = {2302.04324v1.pdf}
}