Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Minor formatting revisions and edits in preparation to identify volunteers to finish. #2

Open
wants to merge 64 commits into
base: master
Choose a base branch
from
Open
Changes from 1 commit
Commits
Show all changes
64 commits
Select commit Hold shift + click to select a range
20c9760
Minor formatting revisions and edits in preparation to identify volun…
brantr Jan 6, 2016
652309d
Adding PDF and galaxies.tex. Will probably need to rework all the ta…
brantr Dec 21, 2016
8652da5
Moving to Legrange Orange Book format.
brantr-test Dec 21, 2016
e6b040d
Revising task list. Added AGN task list.
brantr-test Dec 21, 2016
eb86caa
Adding Photometric Redshift Task List.
brantr-test Dec 21, 2016
0a0f306
Adding High-Z and LSB task lists.
brantr-test Dec 21, 2016
e2244ac
Removing empty sections.
brantr-test Dec 21, 2016
5daff1d
Added Theory and Mock Catalogs.
brantr-test Dec 21, 2016
a41cedc
Updated GitHub readme.
brantr-test Dec 21, 2016
cac0bb5
Last update before sending to WG leads Dec 20.
brantr-test Dec 21, 2016
847bd4d
Adding CLSS and DDF sections.
brantr-test Dec 22, 2016
2468c29
Revisions for LSST Extragalactic Roadmap 2017.
brantr-test Mar 21, 2017
84d1519
Additional changes, reflects distributed copy on 03212017.
brantr-test Mar 21, 2017
42860b5
Adding Graham Smith to author list (sorry Graham)!
brantr-test Mar 21, 2017
3caf58a
Fixed NOAO affiliation.
brantr Mar 21, 2017
1b68cd2
Fixed NOAO in pdf as well.
brantr-test Mar 21, 2017
c1d290e
added pz section
Mar 21, 2017
dfe563b
add Newman2015 ref
sschmidt23 Mar 21, 2017
9f261e9
fix typos
sschmidt23 Mar 22, 2017
07fdb29
Forgot to add galaxies background (thanks @sschmidt23 !)
brantr-test Mar 22, 2017
84cf703
add pz text to correct file
sschmidt23 Mar 23, 2017
be0ae65
Working on final draft.
brantr-test Jul 19, 2017
d8e7b86
Changes to introduction.
brantr Jul 20, 2017
cf059f9
Edits to science background.
brantr Jul 20, 2017
e597392
Edits to AGN section.
brantr Jul 20, 2017
2977211
Page number suppression on title page.
brantr Jul 20, 2017
af10701
Edits to the CLSS section.
brantr Jul 20, 2017
67f2717
Edits to the DDF section.
brantr Jul 20, 2017
ea09d18
Edits to the Galaxy section.
brantr Jul 20, 2017
7f9410c
Edits to the high-z section.
brantr Jul 20, 2017
df6bac7
Edits to the LSB section.
brantr Jul 20, 2017
5f2f8fd
Edits to the Photo-z section.
brantr Jul 20, 2017
2e57f04
Edits to TMC.
brantr Jul 20, 2017
dec383e
Edits to auxillary data section.
brantr Jul 20, 2017
6745533
Formatting spacing near deliverable lists.
brantr Jul 20, 2017
44babd3
Additional formatting. Version sent to LSST Galaxies 07/20/2017.
brantr Jul 20, 2017
b9107e4
Issue resolution on July 24
brantr Jul 24, 2017
55245af
Added affiliation for Matt Jarvis.
brantr Aug 1, 2017
1033f3b
Removed sentence from acknowledgement.
brantr Aug 1, 2017
2f7934f
Improve science relevance of photo-z section.
brantr-test Aug 2, 2017
b63ec56
Add requested reference to AMICO.
brantr-test Aug 2, 2017
dafc267
Removed british spelling.
brantr-test Aug 2, 2017
8cef412
Revised BCG deliverable in AGN section.
brantr-test Aug 2, 2017
e76253a
Rewording on LSST pipeline requirements for clustering measurements.
brantr-test Aug 2, 2017
1ceeeb9
Minor text format issue in 3.6.4.
brantr-test Aug 2, 2017
d538164
Dara's updates to AGN section
brantr-test Aug 2, 2017
8d452b6
Edits suggested by Nicola.
brantr-test Aug 2, 2017
533f1af
Address MED comments.
brantr-test Aug 2, 2017
c335fbd
Cross-links added.
brantr-test Aug 2, 2017
9bfed8b
Updating LSB sky estimation section.
brantr Aug 2, 2017
d043e33
Previous LSB sky estimation changes did not propagate.
brantr Aug 2, 2017
7a5c7c3
Last requested change to LSB made.
brantr Aug 2, 2017
66862a2
Fixed PUC affiliation typo.
brantr Aug 2, 2017
80af8a7
Revised CLSS section with Graham Smith's input.
brantr Aug 2, 2017
05205e9
Fixed Lacerna's affiliation.
brantr Aug 2, 2017
a4052e5
Uniformity edits.
brantr Aug 2, 2017
01a4470
Uniformity and spell check.
brantr Aug 2, 2017
92fe283
Fixed Newman's affiliation.
brantr Aug 3, 2017
b28239f
Small language edits to improve layout.
brantr Aug 3, 2017
e9473de
Version date for arXiv posting updated.
brantr Aug 3, 2017
55941eb
Fixing references
brantr-test Aug 4, 2017
1ab13e8
Last minor revisions throughout.
brantr-test Aug 4, 2017
e9f7f52
Creating an arxiv version.
brantr-test Aug 4, 2017
c6e2d4c
Final formatting tweaks, submitted arxiv version.
brantr-test Aug 4, 2017
File filter

Filter by extension

Filter by extension

Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
Prev Previous commit
Next Next commit
fix typos
sschmidt23 committed Mar 22, 2017
commit 9f261e93269977c7d229b6bab861af3b3230fda3
4 changes: 2 additions & 2 deletions 2016/science_background/galaxies/galaxies.tex
Original file line number Diff line number Diff line change
@@ -388,9 +388,9 @@ \subsection{Probing the Extremes of Galaxy Formation}

\subsection{Photometric Redshifts}
\label{sec:sci:gal:bkgnd:photoz}
As a purely photometric survey, LSST provides an exquisite data set of two-dimensional images of the sky in six passbands. However, lacking a spectroscopic component, adding the third dimension of cosmic distance to each galaxy must come from calculating photometric redshifts (photo-z's). While spectroscopic distance estimates rely on expensive (in terms of telescope time and resources) identification of atomic or molecular transitions in high resolution spectra, photometric redshifts, instead, estimate the rough distance to an object based on broad-band photometric colors. This can be thought of as akin to a very low-resolution spectrum sensitive to the large-scale features of a galaxy spetral energy distribution (e.~g.~the 4000$\AA$ and Lyman breaks), with each broad-band filter being a single pixel in the spectrum. By relying on imaging data alone, we are able to measure photo-z's for billions of galaxies in the LSST survey, at the cost of added uncertainty in the redshift estimates, and potential redshift degeneracies.
As a purely photometric survey, LSST provides an exquisite data set of two-dimensional images of the sky in six passbands. However, lacking a spectroscopic component, adding the third dimension of cosmic distance to each galaxy must come from calculating photometric redshifts (photo-z's). While spectroscopic distance estimates rely on expensive (in terms of telescope time and resources) identification of atomic or molecular transitions in high resolution spectra, photometric redshifts, instead, estimate the rough distance to an object based on broad-band photometric colors. This can be thought of as akin to a very low-resolution spectrum sensitive to the large-scale features of a galaxy spectral energy distribution (e.~g.~the 4000\AA\ and Lyman breaks), with each broad-band filter being a single pixel in the spectrum. By relying on imaging data alone, we are able to measure photo-z's for billions of galaxies in the LSST survey, at the cost of added uncertainty in the redshift estimates, and potential redshift degeneracies.
As errors in the assigned redshift propagate directly to physical quantities of interest, understanding the uncertainties and systematic errors in photo-z's is of the utmost importance for LSST and other photometric surveys. For example, assigning an incorrect redshift to a galaxy also assigns it the incorrect luminosity via the distance modulus, and can bias estimates of the luminosity function; errors in redshift will also bias the inferred restframe colors of a galaxy, propagating to an error in the inferred spectral type, stellar mass, star formation rate, and other quantities. Estimating any physical quantities should be performed jointly with a redshift fit, and the expected uncertainties and degeneracies should be fully understood and propagated if we plan to make measurements in an unbiased way.
In order to understand the biases and uncertainties inherent to photo-z's for a particular survey, we need to train the photo-z algorithms using galaxies with known redshifts. For a full characterization, a fully representative sub-sample of the underlying galaxy population is necessary; however, in practice, this is very difficult to achieve, due to limitations in both spectroscopic instrumentation and telescope time. We can attempt to identify and remove any biases due to incomplete training data using several redshift calibration techniques, the most prominent one relying on spatially cross-correlating photo-z selected data sets with a sample of objects with secure redshifts. A detailed plan describing the spectroscopic needs, for training and calibration, is laid out in \citet[]{Newman2015}, which also details potential scenarios for obtaining the necessary spectroscopy using existing facilities and those expected to be functional in the near future. As a nearly representative set of galaxies designed to span all relevant galaxy properties, this data set could prove very useful not only for photo-z training, but also to those studying galaxy formation and evolution. In addition, any insights gained on galaxy formation and evolution during the course of the LSST survey can be used to improve photo-z algorithms. For example, improved spetral energy distribution evolution models would improve photo-z performance at high redshift. Or, observable quantities such as size and surface brightness may be incorporated as Bayesian priors on the photo-z's once their distributions are well understood. This mutual benefit between understanding galaxy evolution and improved photometric redshift performance should lead to improvements in both subjects as the survey progresses.
In order to understand the biases and uncertainties inherent to photo-z's for a particular survey, we need to train the photo-z algorithms using galaxies with known redshifts. For a full characterization, a fully representative sub-sample of the underlying galaxy population is necessary; however, in practice, this is very difficult to achieve, due to limitations in both spectroscopic instrumentation and telescope time. We can attempt to identify and remove any biases due to incomplete training data using several redshift calibration techniques, the most prominent one relying on spatially cross-correlating photo-z selected data sets with a sample of objects with secure redshifts. A detailed plan describing the spectroscopic needs, for training and calibration, is laid out in \citet[]{Newman2015}, which also details potential scenarios for obtaining the necessary spectroscopy using existing facilities and those expected to be functional in the near future. As a nearly representative set of galaxies designed to span all relevant galaxy properties, this data set could prove very useful not only for photo-z training, but also to those studying galaxy formation and evolution. In addition, any insights gained on galaxy formation and evolution during the course of the LSST survey can be used to improve photo-z algorithms. For example, improved spectral energy distribution evolution models would improve photo-z performance at high redshift. Or, observable quantities such as size and surface brightness may be incorporated as Bayesian priors on the photo-z's once their distributions are well understood. This mutual benefit between understanding galaxy evolution and improved photometric redshift performance should lead to improvements in both subjects as the survey progresses.

\subsection{Science Book}
\label{sec:sci:gal:bkgnd:scibook}