Galaxy clustering with photometric surveys using PDF redshift information
Abstract
Here, photometric surveys produce large-area maps of the galaxy distribution, but with less accurate redshift information than is obtained from spectroscopic methods. Modern photometric redshift (photo-z) algorithms use galaxy magnitudes, or colors, that are obtained through multi-band imaging to produce a probability density function (PDF) for each galaxy in the map. We used simulated data to study the effect of using different photo-z estimators to assign galaxies to redshift bins in order to compare their effects on angular clustering and galaxy bias measurements. We found that if we use the entire PDF, rather than a single-point (mean or mode) estimate, the deviations are less biased, especially when using narrow redshift bins. When the redshift bin widths are $$\Delta z=0.1$$, the use of the entire PDF reduces the typical measurement bias from 5%, when using single point estimates, to 3%.
- Authors:
-
- Univ. of Illinois, Urbana, IL (United States)
- Univ. of Illinois, Urbana, IL (United States); National Center for Supercomputing Applications, Urbana, IL (United States)
- Univ. of Illinois, Urbana, IL (United States); Centro de Investigaciones Energeticas, Medioambientales y Technologicas (CIEMAT), Madrid (Spain)
- Publication Date:
- Research Org.:
- Fermi National Accelerator Lab. (FNAL), Batavia, IL (United States)
- Sponsoring Org.:
- USDOE Office of Science (SC), High Energy Physics (HEP)
- OSTI Identifier:
- 1254141
- Report Number(s):
- arXiv:1601.00357; FERMILAB-PUB-16-183-AE
Journal ID: ISSN 0035-8711; 1412017
- Grant/Contract Number:
- AC02-07CH11359
- Resource Type:
- Accepted Manuscript
- Journal Name:
- Monthly Notices of the Royal Astronomical Society
- Additional Journal Information:
- Journal Volume: 459; Journal Issue: 2; Journal ID: ISSN 0035-8711
- Publisher:
- Royal Astronomical Society
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 79 ASTRONOMY AND ASTROPHYSICS; methods: statistical; galaxies: distances and redshifts; cosmology: large-scale structure of Universe
Citation Formats
Asorey, J., Carrasco Kind, M., Sevilla-Noarbe, I., Brunner, R. J., and Thaler, J. Galaxy clustering with photometric surveys using PDF redshift information. United States: N. p., 2016.
Web. doi:10.1093/mnras/stw721.
Asorey, J., Carrasco Kind, M., Sevilla-Noarbe, I., Brunner, R. J., & Thaler, J. Galaxy clustering with photometric surveys using PDF redshift information. United States. https://doi.org/10.1093/mnras/stw721
Asorey, J., Carrasco Kind, M., Sevilla-Noarbe, I., Brunner, R. J., and Thaler, J. Mon .
"Galaxy clustering with photometric surveys using PDF redshift information". United States. https://doi.org/10.1093/mnras/stw721. https://www.osti.gov/servlets/purl/1254141.
@article{osti_1254141,
title = {Galaxy clustering with photometric surveys using PDF redshift information},
author = {Asorey, J. and Carrasco Kind, M. and Sevilla-Noarbe, I. and Brunner, R. J. and Thaler, J.},
abstractNote = {Here, photometric surveys produce large-area maps of the galaxy distribution, but with less accurate redshift information than is obtained from spectroscopic methods. Modern photometric redshift (photo-z) algorithms use galaxy magnitudes, or colors, that are obtained through multi-band imaging to produce a probability density function (PDF) for each galaxy in the map. We used simulated data to study the effect of using different photo-z estimators to assign galaxies to redshift bins in order to compare their effects on angular clustering and galaxy bias measurements. We found that if we use the entire PDF, rather than a single-point (mean or mode) estimate, the deviations are less biased, especially when using narrow redshift bins. When the redshift bin widths are $\Delta z=0.1$, the use of the entire PDF reduces the typical measurement bias from 5%, when using single point estimates, to 3%.},
doi = {10.1093/mnras/stw721},
journal = {Monthly Notices of the Royal Astronomical Society},
number = 2,
volume = 459,
place = {United States},
year = {Mon Mar 28 00:00:00 EDT 2016},
month = {Mon Mar 28 00:00:00 EDT 2016}
}
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