The low-surface-brightness Universe: a new frontier in the study of galaxy evolution. (arXiv:2001.01728v1 [astro-ph.GA])
<a href="http://arxiv.org/find/astro-ph/1/au:+Kaviraj_S/0/1/0/all/0/1">Sugata Kaviraj</a>
New and forthcoming deep-wide surveys, from instruments like the HSC, LSST
and EUCLID, are poised to revolutionize our understanding of galaxy evolution,
by revealing aspects of galaxies that are largely invisible in past wide-area
datasets. These surveys will open up the realm of low-surface-brightness (LSB)
and dwarf galaxies — which dominate the galaxy number density — for the first
time at cosmological distances. They will also reveal key, unexplored LSB
structures which strongly constrain our structure-formation paradigm, such as
merger-induced tidal features and intra-cluster light. However, exploitation of
these revolutionary new datasets will require us to address several
data-analysis challenges. Data-processing pipelines will have to preserve LSB
structures, which are susceptible to sky over-subtraction. Analysis of the
prodigious data volumes will require machine-learning (in particular
unsupervised techniques), to augment or even replace traditional methods.
Cosmological simulations, which are essential for a statistical understanding
of the physics of galaxy evolution, will require mass and spatial resolutions
that are high enough to resolve LSB/dwarf galaxies and LSB structures. And
finally, estimation of physical properties (e.g. stellar masses and star
formation rates) will require reliable redshift information. Since it is
unlikely that even next-generation spectrographs will provide complete spectral
coverage in the LSB/dwarf regime outside the nearby Universe, photometric
redshifts may drive the science from these datasets. It is necessary,
therefore, that the accuracy of these redshifts is good enough (e.g. < 10 per
cent) to enable statistical studies in the LSB/dwarf regime. I outline the
tremendous discovery potential of new/forthcoming deep-wide surveys and
describe techniques which will enable us to solve the data-analysis challenges
outlined above. (Abridged)
New and forthcoming deep-wide surveys, from instruments like the HSC, LSST
and EUCLID, are poised to revolutionize our understanding of galaxy evolution,
by revealing aspects of galaxies that are largely invisible in past wide-area
datasets. These surveys will open up the realm of low-surface-brightness (LSB)
and dwarf galaxies — which dominate the galaxy number density — for the first
time at cosmological distances. They will also reveal key, unexplored LSB
structures which strongly constrain our structure-formation paradigm, such as
merger-induced tidal features and intra-cluster light. However, exploitation of
these revolutionary new datasets will require us to address several
data-analysis challenges. Data-processing pipelines will have to preserve LSB
structures, which are susceptible to sky over-subtraction. Analysis of the
prodigious data volumes will require machine-learning (in particular
unsupervised techniques), to augment or even replace traditional methods.
Cosmological simulations, which are essential for a statistical understanding
of the physics of galaxy evolution, will require mass and spatial resolutions
that are high enough to resolve LSB/dwarf galaxies and LSB structures. And
finally, estimation of physical properties (e.g. stellar masses and star
formation rates) will require reliable redshift information. Since it is
unlikely that even next-generation spectrographs will provide complete spectral
coverage in the LSB/dwarf regime outside the nearby Universe, photometric
redshifts may drive the science from these datasets. It is necessary,
therefore, that the accuracy of these redshifts is good enough (e.g. < 10 per
cent) to enable statistical studies in the LSB/dwarf regime. I outline the
tremendous discovery potential of new/forthcoming deep-wide surveys and
describe techniques which will enable us to solve the data-analysis challenges
outlined above. (Abridged)
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