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Galaxy formation and evolution science in the era of the Large Synoptic Survey Telescope

Abstract

The field of galaxy formation and evolution synthesizes the physics of baryons and dark matter to describe the origin of systems such as the Milky Way and the enormous diversity of the galaxy population. The broad variation in possible formation histories and the wide range of cosmic environments make large statistical samples of galaxies essential for identifying the important physical mechanisms that govern their formation. Starting in the early 2020s, the Large Synoptic Survey Telescope (LSST) will provide an unmatched dataset for galaxy evolution studies by observing the entire southern sky in ultraviolet, optical and near-infrared wavelengths, producing multi-epoch digital images over a 10-year nominal mission that when summed will provide the deepest, wide-angle view of our Universe ever assembled. Here, we discuss the importance of LSST for deepening our understanding of galaxy formation and evolution over cosmic time. We present some outstanding problems in the field that LSST will address, and we present a roadmap of some preparatory research efforts required to make effective use of the LSST dataset for galaxy formation science.

Key points

  • The Large Synoptic Survey Telescope (LSST) will provide a new window into galaxy formation and evolution by imaging the entire southern sky with unprecedented sensitivity.

  • By probing the rarest cosmic environments, LSST can reveal the extreme conditions under which the most luminous galaxies and the most massive supermassive black holes are formed.

  • The gravitational lensing signals measured by LSST will enable astronomers to understand the mapping between observed galaxy properties and the dark matter halos that serve as the sites of galaxy formation.

  • LSST will unveil the low-surface-brightness features around galaxies that encode the hierarchical nature of cosmological structure formation.

  • A broad effort to address technical challenges for LSST, including deblending and machine learning, must commence now and requires heightened investment in the wide community of scientists who motivated the development of LSST.

  • Coordinating existing ancillary data and new observational programmes in support of LSST will enable astronomers to make full use of the power of LSST, but these efforts need sufficient advance planning and community funding.

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Fig. 1: Connection between dark matter structures and galaxies.
Fig. 2: Illustration of blending from atmospheric seeing.
Fig. 3: Example of deep learning classification of astronomical image pixels.

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Acknowledgements

B.E.R. acknowledges a Maureen and John Hendricks Visiting Professorship at the Institute for Advanced Study, NASA contract NNG16PJ25C, and NSF award 1828315. S.K. acknowledges support from STFC through grant ST/N002512/1 and a Senior Research Fellowship at Worcester College Oxford. S.J.S. acknowledges support from DOE grant DE-SC0009999 and NSF/AURA grant N56981C.

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B.E.R. wrote the text and created the figures. M.B., S.B., R.L.D., H.C.F., R.H., S.K., J.A.N., S.J.S., J.A.T. and R.H.W. contributed to the text. All authors reviewed the manuscript.

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Correspondence to Brant E. Robertson.

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Glossary

Deblending

The analysis procedure by which the light from overlapping stars and galaxies in crowded astronomical images is assigned to distinct objects.

Airmass

A measure of the path length through the Earth’s atmosphere, increasing from zenith to the horizon.

Dithering strategies

The distribution on the sky of individual images taken by a camera, where shifts of the camera position are taken to cover more area, to improve the image quality by better sampling the light distribution of astronomical objects, and to account for the layout and possible defects of detectors in the camera.

Pointings

Single locations on the sky where a telescope has been aimed.

ugrizy photometric bands

The camera filters used by LSST to capture certain wavelengths of light, from the ultraviolet (u), to the optical (g, r and i) to the near-infrared (z and y).

Ancillary dataset

A collection of observations that augment or support an astronomical experiment, but were not taken by the same observatory (for instance, radio or X-ray observations provide ancillary datasets for optical observations).

Peak rarity

A measure of the local overdensity of matter, equivalent to the number of standard deviations above the mean density a region would lie in the initial, nearly Gaussian matter density field generated by the end of inflation.

Galactic cirrus background

A source of noise in the form of sky brightness from low density gas and dust in the Milky Way, spread over large angular scales on the sky.

Slitless near-infrared spectroscopy

A method for measuring the spectra of light from astronomical objects in a telescope’s field of view by dispersing incoming light to a camera without using a mask with small slits.

R + I + Z visual filter

The camera filters used by Euclid to capture certain wavelengths of optical light.

H4RG infrared detectors

Special pixelated sensors used in astronomical cameras that have high quantum efficiency for light redder than wavelengths of 1 μm.

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Robertson, B.E., Banerji, M., Brough, S. et al. Galaxy formation and evolution science in the era of the Large Synoptic Survey Telescope. Nat Rev Phys 1, 450–462 (2019). https://doi.org/10.1038/s42254-019-0067-x

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