publication . Article . Preprint . Other literature type . 2018

DES Science Portal: Computing Photometric Redshifts

Gschwend, Julia; Carnero, Aurelio; Ogando, Ricardo L.C.; Neto, Angelo F.; Maia, Marcio A.G.; da Costa, Luiz A.N.; Lima, Marcos; Pellegrini, Paulo S.; Campisano, Riccardo; Singulani, Cristiano P.; ...
Open Access English
  • Published: 01 Oct 2018
  • Publisher: HAL CCSD
Abstract
A significant challenge facing photometric surveys for cosmological purposes is the need to produce reliable redshift estimates. The estimation of photometric redshifts (photo-zs) has been consolidated as the standard strategy to bypass the high production costs and incompleteness of spectroscopic redshift samples. Training-based photo-z methods require the preparation of a high-quality list of spectroscopic redshifts, which needs to be constantly updated. The photo-z training, validation, and estimation must be performed in a consistent and reproducible way in order to accomplish the scientific requirements. To meet this purpose, we developed an integrated web-...
Subjects
free text keywords: Galaxies: distances and redshifts, Methods: data analysis, surveys, Astronomical databases: catalogs, statistics, Astronomical data bases: surveys, photometry, DES, [PHYS.ASTR]Physics [physics]/Astrophysics [astro-ph], [PHYS.PHYS.PHYS-INS-DET]Physics [physics]/Physics [physics]/Instrumentation and Detectors [physics.ins-det], Astrophysics - Astrophysics of Galaxies, Astrophysics - Instrumentation and Methods for Astrophysics, Astronomy and Astrophysics, Computer Science Applications, 520, catalogs, surveys [Astronomical databases], distances and redshifts, statistics [Galaxies], data analysis [Methods], RCUK, STFC, /dk/atira/pure/subjectarea/asjc/3100/3103, /dk/atira/pure/subjectarea/asjc/1700/1706, /dk/atira/pure/subjectarea/asjc/1900/1912, Space and Planetary Science, ddc:520, Scientific analysis, Computer science, Photometry (optics), Vetting, Redshift, Data mining, computer.software_genre, computer, Data interface, Workflow
Funded by
NSF| Collaborative Research: The Dark Energy Survey Data Management Operations
Project
  • Funder: National Science Foundation (NSF)
  • Project Code: 1138766
  • Funding stream: Directorate for Mathematical & Physical Sciences | Division of Astronomical Sciences
62 references, page 1 of 5

3.5. Computing Photo-zs for large datasets Colless, M., Dalton, G., Maddox, S., et al., 2001. The 2dF Galaxy Redshift Survey: spectra and redshifts. MNRAS 328, 1039-1063. doi:10.1046/j. 1365-8711.2001.04902.x, arXiv:astro-ph/0106498.

Collister, A.A., Lahav, O., 2004. ANNz: Estimating Photometric Redshifts Using Artificial Neural Networks. PASP 116, 345-351. doi:10.1086/383254, arXiv:astro-ph/0311058.

Comparat, J., Delubac, T., Jouvel, S., et al., 2016. SDSS-IV eBOSS emissionline galaxy pilot survey. A&A 592, A121. doi:10.1051/0004-6361/ 201527377, arXiv:1509.05045. [OpenAIRE]

Cunha, C.E., Huterer, D., Lin, H., et al., 2014. Spectroscopic failures in photometric redshift calibration: cosmological biases and survey requirements. MNRAS 444, 129-146. doi:10.1093/mnras/stu1424, arXiv:1207.3347.

Cunha, C.E., Lima, M., Oyaizu, H., et al., 2009. Estimating the redshift distribution of photometric galaxy samples - II. Applications and tests of a new method. MNRAS 396, 2379-2398. doi:10.1111/j.1365-2966.2009. 14908.x, arXiv:0810.2991. [OpenAIRE]

Davis, M., Faber, S.M., Newman, J., et al., 2003. Science Objectives and Early Results of the DEEP2 Redshift Survey, in: Guhathakurta, P. (Ed.), Discoveries and Research Prospects from 6- to 10-Meter-Class Telescopes II, pp. 161-172. doi:10.1117/12.457897, arXiv:astro-ph/0209419.

Davis, M., Guhathakurta, P., Konidaris, N.P., et al., 2007. The All-Wavelength Extended Groth Strip International Survey (AEGIS) Data Sets. ApJ 660, L1-L6. doi:10.1086/517931, arXiv:astro-ph/0607355.

De Vicente, J., Sa´nchez, E., Sevilla-Noarbe, I., 2016. DNF - Galaxy photometric redshift by Directional Neighbourhood Fitting. MNRAS 459, 3078- 3088. doi:10.1093/mnras/stw857, arXiv:1511.07623.

DES, et al., 2016. The Dark Energy Survey: more than dark energy - an overview. MNRAS doi:10.1093/mnras/stw641, arXiv:1601.00329.

Desai, S., Armstrong, R., Mohr, J.J., et al., 2012. The Blanco Cosmology Survey: Data Acquisition, Processing, Calibration, Quality Diagnostics, and Data Release. ApJ 757, 83. doi:10.1088/0004-637X/757/1/83, arXiv:1204.1210.

Diehl, H.T., Abbott, T.M.C., Annis, J., et al., 2014. The Dark Energy Survey and operations: Year 1, in: Observatory Operations: Strategies, Processes, and Systems V, p. 91490V. doi:10.1117/12.2056982.

Driver, S.P., Hill, D.T., Kelvin, L.S., et al., 2011. Galaxy and Mass Assembly (GAMA): survey diagnostics and core data release. MNRAS 413, 971-995. doi:10.1111/j.1365-2966.2010.18188.x, arXiv:1009.0614.

Flaugher, B., 2005. The Dark Energy Survey. International Journal of Modern Physics A 20, 3121-3123. doi:10.1142/S0217751X05025917.

Flaugher, B., Diehl, H.T., Honscheid, K., et al., 2015. The Dark Energy Camera. AJ 150, 150. doi:10.1088/0004-6256/150/5/150, arXiv:1504.02900. [OpenAIRE]

Garilli, B., Guzzo, L., Scodeggio, M., et al., 2014. The VIMOS Public Extragalactic Survey (VIPERS). First Data Release of 57 204 spectroscopic measurements. A&A 562, A23. doi:10.1051/0004-6361/201322790, arXiv:1310.1008. [OpenAIRE]

62 references, page 1 of 5
Abstract
A significant challenge facing photometric surveys for cosmological purposes is the need to produce reliable redshift estimates. The estimation of photometric redshifts (photo-zs) has been consolidated as the standard strategy to bypass the high production costs and incompleteness of spectroscopic redshift samples. Training-based photo-z methods require the preparation of a high-quality list of spectroscopic redshifts, which needs to be constantly updated. The photo-z training, validation, and estimation must be performed in a consistent and reproducible way in order to accomplish the scientific requirements. To meet this purpose, we developed an integrated web-...
Subjects
free text keywords: Galaxies: distances and redshifts, Methods: data analysis, surveys, Astronomical databases: catalogs, statistics, Astronomical data bases: surveys, photometry, DES, [PHYS.ASTR]Physics [physics]/Astrophysics [astro-ph], [PHYS.PHYS.PHYS-INS-DET]Physics [physics]/Physics [physics]/Instrumentation and Detectors [physics.ins-det], Astrophysics - Astrophysics of Galaxies, Astrophysics - Instrumentation and Methods for Astrophysics, Astronomy and Astrophysics, Computer Science Applications, 520, catalogs, surveys [Astronomical databases], distances and redshifts, statistics [Galaxies], data analysis [Methods], RCUK, STFC, /dk/atira/pure/subjectarea/asjc/3100/3103, /dk/atira/pure/subjectarea/asjc/1700/1706, /dk/atira/pure/subjectarea/asjc/1900/1912, Space and Planetary Science, ddc:520, Scientific analysis, Computer science, Photometry (optics), Vetting, Redshift, Data mining, computer.software_genre, computer, Data interface, Workflow
Funded by
NSF| Collaborative Research: The Dark Energy Survey Data Management Operations
Project
  • Funder: National Science Foundation (NSF)
  • Project Code: 1138766
  • Funding stream: Directorate for Mathematical & Physical Sciences | Division of Astronomical Sciences
62 references, page 1 of 5

3.5. Computing Photo-zs for large datasets Colless, M., Dalton, G., Maddox, S., et al., 2001. The 2dF Galaxy Redshift Survey: spectra and redshifts. MNRAS 328, 1039-1063. doi:10.1046/j. 1365-8711.2001.04902.x, arXiv:astro-ph/0106498.

Collister, A.A., Lahav, O., 2004. ANNz: Estimating Photometric Redshifts Using Artificial Neural Networks. PASP 116, 345-351. doi:10.1086/383254, arXiv:astro-ph/0311058.

Comparat, J., Delubac, T., Jouvel, S., et al., 2016. SDSS-IV eBOSS emissionline galaxy pilot survey. A&A 592, A121. doi:10.1051/0004-6361/ 201527377, arXiv:1509.05045. [OpenAIRE]

Cunha, C.E., Huterer, D., Lin, H., et al., 2014. Spectroscopic failures in photometric redshift calibration: cosmological biases and survey requirements. MNRAS 444, 129-146. doi:10.1093/mnras/stu1424, arXiv:1207.3347.

Cunha, C.E., Lima, M., Oyaizu, H., et al., 2009. Estimating the redshift distribution of photometric galaxy samples - II. Applications and tests of a new method. MNRAS 396, 2379-2398. doi:10.1111/j.1365-2966.2009. 14908.x, arXiv:0810.2991. [OpenAIRE]

Davis, M., Faber, S.M., Newman, J., et al., 2003. Science Objectives and Early Results of the DEEP2 Redshift Survey, in: Guhathakurta, P. (Ed.), Discoveries and Research Prospects from 6- to 10-Meter-Class Telescopes II, pp. 161-172. doi:10.1117/12.457897, arXiv:astro-ph/0209419.

Davis, M., Guhathakurta, P., Konidaris, N.P., et al., 2007. The All-Wavelength Extended Groth Strip International Survey (AEGIS) Data Sets. ApJ 660, L1-L6. doi:10.1086/517931, arXiv:astro-ph/0607355.

De Vicente, J., Sa´nchez, E., Sevilla-Noarbe, I., 2016. DNF - Galaxy photometric redshift by Directional Neighbourhood Fitting. MNRAS 459, 3078- 3088. doi:10.1093/mnras/stw857, arXiv:1511.07623.

DES, et al., 2016. The Dark Energy Survey: more than dark energy - an overview. MNRAS doi:10.1093/mnras/stw641, arXiv:1601.00329.

Desai, S., Armstrong, R., Mohr, J.J., et al., 2012. The Blanco Cosmology Survey: Data Acquisition, Processing, Calibration, Quality Diagnostics, and Data Release. ApJ 757, 83. doi:10.1088/0004-637X/757/1/83, arXiv:1204.1210.

Diehl, H.T., Abbott, T.M.C., Annis, J., et al., 2014. The Dark Energy Survey and operations: Year 1, in: Observatory Operations: Strategies, Processes, and Systems V, p. 91490V. doi:10.1117/12.2056982.

Driver, S.P., Hill, D.T., Kelvin, L.S., et al., 2011. Galaxy and Mass Assembly (GAMA): survey diagnostics and core data release. MNRAS 413, 971-995. doi:10.1111/j.1365-2966.2010.18188.x, arXiv:1009.0614.

Flaugher, B., 2005. The Dark Energy Survey. International Journal of Modern Physics A 20, 3121-3123. doi:10.1142/S0217751X05025917.

Flaugher, B., Diehl, H.T., Honscheid, K., et al., 2015. The Dark Energy Camera. AJ 150, 150. doi:10.1088/0004-6256/150/5/150, arXiv:1504.02900. [OpenAIRE]

Garilli, B., Guzzo, L., Scodeggio, M., et al., 2014. The VIMOS Public Extragalactic Survey (VIPERS). First Data Release of 57 204 spectroscopic measurements. A&A 562, A23. doi:10.1051/0004-6361/201322790, arXiv:1310.1008. [OpenAIRE]

62 references, page 1 of 5
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