Photometric Redshifts Featured Project: Calibration White Paper

Impact of uncertainty on the mean redshift (Δ<z>) or the RMS of the redshift distribution (Δσz) on dark energy constraints. The y-axis shows the relative increase in the error in wa = dw/da , the derivative of the equation of state (pressure-to-energy ratio) of dark energy with respect to scale factor a, resulting from an LSST-like weak lensing analysis, as a function of the uncertainty in the RMS of each Δz = 0.1 wide bin used in the analysis. For Gaussian statistics, the error in the mean redshift, Δ<z>,
Caption: 

Impact of uncertainty on the mean redshift (Δ) or the RMS of the redshift distribution (Δσz) on dark energy constraints. The y-axis shows the relative increase in the error in wa = dw/da, the derivative of the equation of state (pressure-to-energy ratio) of dark energy with respect to scale factor a, resulting from an LSST-like weak lensing analysis, as a function of the uncertainty in the RMS of each Δz = 0.1 wide bin used in the analysis. For Gaussian statistics, the error in the mean redshift, Δ, will be larger than the plotted x coordinate value by a factor of sqrt(2) ; the impact of both of these errors in concert are included in this analysis (based on the work of Hearin et al. 2011). Uncertainties of a few hundredths in z in the mean redshift and RMS of galaxy samples, as has typically been achieved in the past, will not be sufficient for future surveys, as it leads to a factor of a few degradation in errors on this crucial dark energy parameter.

(Authors: Jeff Newman,  A. Abate, F. Abdalla, S. Allam, S. Allen, R. Ansari, S. Bailey, W. Barkhouse, T. Beers, M. Blanton, M. Brodwin, J. Brownstein, R. Brunner, M. Carrasco-Kind, J. Cervantes-Cota, E. Chisari, M. Colless, J. Comparat, J. Coupon, E. Cheu, C. Cunha,A. de la Macorra, I. Dell'Antonio, B. Frye, E. Gawiser, N. Gehrels, K. Grady, A. Hagen, P. Hall, A. Hearin, H. Hildebrandt, C. Hirata, S. Ho, K. Honscheid, D. Huterer, Z. Ivezic, J.-P. Kneib, J. Kruk, O. Lahav, R. Mandelbaum, J. Marshall, D. Matthews, B. Ménard, R. Miquel, M. Moniez, W. Moos, J. Moustakas, C. Papovich, J. Peacock, C. Park, J. Rhodes, J-S. Ricol, I. Sadeh, A. Slozar,S. Schmidt, D. Stern, T. Tyson, A. von der Linden, R. Wechsler, W. Wood-Vasey, A. Zentner.)

As part of the Snowmass process, we investigated the scope of spectroscopic datasets that could enhance the capabilities of LSST, posted to the arxiv at http://arxiv.org/abs/1309.5384 .  Specifically, large sets of objects with spectroscopic redshift measurements will be needed for LSST to achieve its full potential, serving two goals: training, i.e., the use of objects with known redshift to develop and optimize photometric redshift algorithms; and calibration, i.e., the characterization of moments of redshift (or photo-z error) distributions. Better training makes cosmological constraints from a given experiment stronger, while highly-accurate calibration is needed for photo-z systematics not to dominate errors.

In the resulting white paper, we investigate the required scope of spectroscopic datasets which can serve both these purposes for LSST and other dark energy experiments, as well as the time required to obtain such data with instruments available in the next decade. Large time allocations on kilo-object spectrographs will be necessary, ideally augmented by infrared spectroscopy from space. Alternatively, precision calibrations could be obtained by measuring cross-correlation statistics using samples of bright objects from a large baryon acoustic oscillation experiment such as DESI. We also summarize the additional work on photometric redshift methods needed to prepare for LSST.