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The Spitzer Extragalactic Representative Volume Survey - measuring photometric redshifts for ∼4 million galaxies - challenges and ways forward

Published online by Cambridge University Press:  10 June 2020

Janine Pforr
Janine Pforr*
Affiliation:
Scientific Support Office, Directorate of Science and Robotic Exploration, European Space Research and Technology Centre, (ESA/ESTEC), Keplerlaan 1, 2201 AZ Noordwijk, The Netherlands email: [email protected], Research Fellow
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Abstract

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We highlight the challenges as well as lessons learnt in the derivation of the photometric redshifts for ∼4 million galaxies at 0 < z ≲ 6 contained in the Spitzer Extragalactic Representative Volume Survey (SERVS) and summarise the photometric redshift results recently published in Pforr et al. (2019). The inhomogeneous nature of the ancillary photometry for SERVS presents a similar situation to the one future, large, extragalactic surveys with e.g. LSST and JWST will face. We employ template SED-fitting to determine photometric redshifts. Our comparison of photometric redshifts to ∼75.000 public, spectroscopic redshifts results in an average σNMAD of ∼0.038 and outlier fraction of 3.7% for sources with the best photometric coverage. We find that photometric redshifts are determined most robustly when filter bands are numerous and cover a wide wavelength range. We highlight some possible improvements for the photometric redshifts in SERVS in the future.

Keywords

galaxies: generalgalaxies: distances and redshiftsgalaxies: fundamental parameterssurveys
Type
Contributed Papers
Information
Proceedings of the International Astronomical Union , Volume 15 , Symposium S341: Challenges in Panchromatic Modelling with Next Generation Facilities , November 2019 , pp. 157 - 161
Copyright
© International Astronomical Union 2020

References

Acquaviva, V., Gawiser, E., & Guaita, L., 2011, ApJ, 737, 4710.1088/0004-637X/737/2/47CrossRefGoogle Scholar
Bolzonella, M., Miralles, J.-M., & Pelló, R., 2000, A&A, 363, 476Google Scholar
Brammer, G. B., Whitaker, K. E., van Dokkum, P. G., Marchesini, D., Labbé, I., Franx, M., Kriek, M., Quadri, R. F., et al. 2009, ApJL, 706, 17310.1088/0004-637X/706/1/L173CrossRefGoogle Scholar
Calzetti, D., Armus, L., Bohlin, R. C., Kinney, A. L., Koornneef, J., & Storchi-Bergmann, T., 2000, ApJ, 533, 68210.1086/308692CrossRefGoogle Scholar
Dahlen, T., Mobasher, B., Faber, S. M., Ferguson, H. C., Barro, G., Finkelstein, S. L., Finlator, K., Fontana, A., et al. 2013, ApJ, 755, 9310.1088/0004-637X/775/2/93CrossRefGoogle Scholar
Duncan, K. J., Brown, M. J. I, Williams, W. L., Best, P. N., Buat, V., Burgarella, D., Jarvis, M. J., Małek, K., et al. 2018, MNRAS, 473, 265510.1093/mnras/stx2536CrossRefGoogle Scholar
Fazio, G. G., Hora, J. L., Allen, L. E., Ashby, M. L. N., Barmby, P., Deutsch, L. K., Huang, J.-S., Kleiner, S., et al. 2004, ApJS, 154, 1010.1086/422843CrossRefGoogle Scholar
Galametz, A., Grazian, A., Fontana, A., Ferguson, H. C., Ashby, M. L. N., Barro, G., Castellano, M., Dahlen, T., et al. 2013, ApJS, 206, 1010.1088/0067-0049/206/2/10CrossRefGoogle Scholar
Guo, Y., Ferguson, H. C., Giavalisco, M., Barro, G., Willner, S. P., Ashby, M. L. N., Dahlen, T., Donley, J. L., et al. 2013, ApJS, 207, 2410.1088/0067-0049/207/2/24CrossRefGoogle Scholar
Hoaglin, D. C., Mosteller, F., & Tukey, J. W. 1983, Wiley Series in Probability and Mathematical Statistics, New York: Wiley, 1983, edited by Hoaglin, David C.; Mosteller, Frederick; Tukey, John W.Google Scholar
Huber, P. J., 1981 Wiley Series in Probability and Mathematical Statistics, New York: Wiley, |c1981 10.1002/0471725250CrossRefGoogle Scholar
Lang, D., Hogg, D. W., & Schlegel, D. J., 2016, AJ, 151, 3610.3847/0004-6256/151/2/36CrossRefGoogle Scholar
Maraston, C. 2005, MNRAS, 362, 79910.1111/j.1365-2966.2005.09270.xCrossRefGoogle Scholar
Mauduit, J.-C., Lacy, M., Farrah, D., Surace, J. A., Jarvis, M., Oliver, S., Maraston, C., Vaccari, M., et al. 2012, PASP, 124, 71410.1086/666945CrossRefGoogle Scholar
Nyland, K., Lacy, M., Sajina, A., Pforr, J., Farrah, D., Wilson, G., Surace, J., Häußler, B., et al. 2017, ApJS, 230, 910.3847/1538-4365/aa6fedCrossRefGoogle Scholar
Pforr, J., Vaccari, M., Lacy, M., Maraston, C., Nyland, K., Marchetti, L., & Thomas, D., 2019, MNRAS, 483, 316810.1093/mnras/sty3075CrossRefGoogle Scholar
Pforr, J., Maraston, C., & Tonini, C., 2013, MNRAS, 435, 138910.1093/mnras/stt1382CrossRefGoogle Scholar
Salpeter, E. E. 1955, ApJ, 121, 16110.1086/145971CrossRefGoogle Scholar
Salvato, M., Hasinger, G., Ilbert, O., Zamorani, G., Brusa, M., Scoville, N. Z., Rau, A., Capak, P., et al. 2009, ApJ, 609, 125010.1088/0004-637X/690/2/1250CrossRefGoogle Scholar
Vaccari, M. 2015, “The Many Facets of Extragalactic Radio Surveys: Towards New Scientific Challenges”, The Spitzer Data Fusion: Contents, Construction and Applications to Galaxy Evolution Studies, p27Google Scholar