3,254 research outputs found
Photometric Redshift Training Survey Times
Table of telescope time required for photometric redshift training for the Large Synoptic Survey Telescope (LSST); this table was produced for an Astro2020 Decadal Survey white paper. Includes explanatory information
Photometric Redshifts for Next-Generation Surveys
Photometric redshifts are essential in studies of both galaxy evolution and
cosmology, as they enable analyses of objects too numerous or faint for
spectroscopy. The Rubin Observatory, Euclid, and Roman Space Telescope will
soon provide a new generation of imaging surveys with unprecedented area
coverage, wavelength range, and depth. To take full advantage of these
datasets, further progress in photometric redshift methods is needed. In this
review, we focus on the greatest common challenges and prospects for
improvement in applications of photo-'s to the next generation of surveys:
- Gains in -- i.e., the precision of redshift estimates for
individual galaxies -- could greatly enhance studies of galaxy evolution and
some probes of cosmology.
- Improvements in -- i.e., the accurate recovery of
redshift of galaxies in the presence of uncertainty on
individual redshifts -- are urgently needed for cosmological measurements with
next-generation surveys.
- To achieve both of these goals, improvements in the scope and treatment of
the samples of spectroscopic redshifts which make high-fidelity photo-'s
possible will also be needed.
For the full potential of the next generation of surveys to be reached, the
characterization of redshift distributions will need to improve by roughly an
order of magnitude compared to the current state of the art, requiring progress
on a wide variety of fronts. We conclude by presenting a speculative evaluation
of how photometric redshift methods and the collection of the necessary
spectroscopic samples may improve by the time near-future surveys are
completed.Comment: Posted with permission from the Annual Review of Astronomy and
Astrophysics, Volume 60, copyright 2022 Annual Reviews,
http://www.annualreviews.org
A Cosmic Variance Cookbook
Deep pencil beam surveys (<1 deg^2) are of fundamental importance for
studying the high-redshift universe. However, inferences about galaxy
population properties are in practice limited by 'cosmic variance'. This is the
uncertainty in observational estimates of the number density of galaxies
arising from the underlying large-scale density fluctuations. This source of
uncertainty can be significant, especially for surveys which cover only small
areas and for massive high-redshift galaxies. Cosmic variance for a given
galaxy population can be determined using predictions from cold dark matter
theory and the galaxy bias. In this paper we provide tools for experiment
design and interpretation. For a given survey geometry we present the cosmic
variance of dark matter as a function of mean redshift z and redshift bin size
Dz. Using a halo occupation model to predict galaxy clustering, we derive the
galaxy bias as a function of mean redshift for galaxy samples of a given
stellar mass range. In the linear regime, the cosmic variance of these galaxy
samples is the product of the galaxy bias and the dark matter cosmic variance.
We present a simple recipe using a fitting function to compute cosmic variance
as a function of the angular dimensions of the field, z, Dz and stellar mass
m*. We also provide tabulated values and a software tool. We find that for
GOODS at z=2 and with Dz=0.5 the relative cosmic variance of galaxies with
m*>10^11 Msun is ~38%, while it is ~27% for GEMS and ~12% for COSMOS. For
galaxies of m*~10^10 Msun the relative cosmic variance is ~19% for GOODS, ~13%
for GEMS and ~6% for COSMOS. This implies that cosmic variance is a significant
source of uncertainty at z=2 for small fields and massive galaxies, while for
larger fields and intermediate mass galaxies cosmic variance is less serious.Comment: 8 pages, 4 figures, 5 tables, submitted to Ap
What Genetics Offers Geobiology
For over 50 years, the Parker Brothers’ board game “Clue” has maintained its position as the classic family detective game. A murder has been committed in the mansion, but we don’t know where, by whom, or how. Was it Professor Plum in the study with a knife, or Miss Scarlett in the ballroom with a candlestick? Through rolls of the dice, fragments of information patiently accumulated piece-by-piece, and the application of logic, players construct a case to figure out “whodunit”. Because there are several potential solutions to the problem, the key challenge is to figure out what happened by understanding how it happened
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