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19972026
most citedLSST: from Science Drivers to Reference Design and Anticipated Data Products

3.5k citations · 38.1k across the 408 of their papers we have counts for

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Showing 2022Show all

15 papers · 1 filter

astro-ph.CO2022★ 33 cited

A Measurement of the Cosmic Optical Background and Diffuse Galactic Light Scaling from the R < 50 AU New Horizons-LORRI Data

Teresa Symons, Michael Zemcov, Asantha Cooray +2

Direct photometric measurements of the cosmic optical background (COB) provide an important point of comparison to both other measurement methodologies and models of cosmic structu…

astro-ph.CO2022

Euclid preparation. XXVII. Covariance model validation for the 2-point correlation function of galaxy clusters

Euclid Collaboration, A. Fumagalli, A. Saro +191

Aims. We validate a semi-analytical model for the covariance of real-space 2-point correlation function of galaxy clusters. Methods. Using 1000 PINOCCHIO light cones mimicking the…

astro-ph.GA2022★ 24 cited

Missing Giants: Predictions on Dust-Obscured Galaxy Stellar Mass Assembly Throughout Cosmic Time

Arianna S. Long, Caitlin M. Casey, Claudia del P. Lagos +5

Due to their extremely dust-obscured nature, much uncertainty still exists surrounding the stellar mass growth and content in dusty, star-forming galaxies (DSFGs) at . In this…

astro-ph.GA2022★ 134 cited

CEERS Key Paper V: A triality on the nature of HST-dark galaxies

Pablo G. Pérez-González, Guillermo Barro, Marianna Annunziatella +52

The new capabilities that JWST offers in the near- and mid-infrared (IR) are used to investigate in unprecedented detail the nature of optical/near-IR faint, mid-IR bright sources,…

astro-ph.GA2022★ 26 cited

Euclid preparation. XXV. The Euclid Morphology Challenge -- Towards model-fitting photometry for billions of galaxies

Euclid Collaboration, E. Merlin, M. Castellano +218

The ESA Euclid mission will provide high-quality imaging for about 1.5 billion galaxies. A software pipeline to automatically process and analyse such a huge amount of data in real…

astro-ph.GA2022★ 14 cited

A Machine Learning Approach to Predict Missing Flux Densities in Multi-band Galaxy Surveys

Nima Chartab, Bahram Mobasher, Asantha Cooray +21

We present a new method based on information theory to find the optimal number of bands required to measure the physical properties of galaxies with a desired accuracy. As a proof…