Spectroscopic Needs for Imaging Dark Energy Experiments

Journal Article · Sun Mar 15 00:00:00 EDT 2015 · Astroparticle Physics

DOI:https://doi.org/10.1016/j.astropartphys.2014.06.007· OSTI ID:1172078

Newman, Jeffrey A. ^[1]; Slosar, Anze ^[2]; Abate, Alexandra ^[3]; Abdalla, Filipe B. ^[4]; Allam, Sahar ^[5]; Allen, Steven W. ^[6]; Ansari, Reza ^[7]; Bailey, Stephen ^[8]; Barkhouse, Wayne A. ^[9]; Beers, Timothy C. ^[10]; Blanton, Michael R. ^[11]; Brodwin, Mark ^[12]; Brownstein, Joel R. ^[13]; Brunner, Robert J. ^[14]; Carrasco-Kind, Matias ^[14]; Cervantes-Cota, Jorge ^[15]; Chisari, Nora Elisa ^[16]; Colless, Matthew ^[17]; Comparat, Johan ^[18]; Coupon, Jean ^[19] ; Cheu, Elliott ^[3]; Cunha, Carlos E. ^[20]; de la Macorra, Alex ^[21]; Dell’Antonio, Ian P. ^[22]; Frye, Brenda L. ^[3]; Gawiser, Eric J. ^[23]; Gehrels, Neil ^[24]; Grady, Kevin ^[24]; Hagen, Alex ^[25]; Hall, Patrick B. ^[26]; Hearin, Andrew P. ^[27]; Hildebrandt, Hendrik ^[28]; Hirata, Christopher M. ^[29]; Ho, Shirley ^[30]; Honscheid, Klaus ^[29]; Huterer, Dragan ^[31]; Ivezic, Zeljko ^[32]; Kneib, Jean -Paul ^[33]; de Marseille, Laboratoire d'Astrophysique ^[34]; Kruk, Jeffrey W. ^[24]; Lahav, Ofer ^[35]; Mandelbaum, Rachel ^[30]; Marshall, Jennifer L. ^[36]; Matthews, Daniel J. ^[1]; Menard, Brice ^[37]; Miquel, Ramon ^[38]; Moniez, Marc ^[39]; Moos, H. W. ^[37]; Moustakas, John ^[40]; Papovich, Casey ^[36]; Peacock, John A. ^[41]; Park, Changbom ^[42]; Rhodes, Jason ^[43]; Ricol, Jean-Stepane ^[44]; Sadeh, Iftach ^[4]; Schmidt, Samuel J. ^[45]; Stern, Daniel K. ^[46]; Tyson, J. Anthony ^[45]; von der Linden, Anja ^[47]; Wechsler, Risa H. ^[6]; Wood-Vasey, W. M. ^[1]; Zentner, A. ^[1]

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1. Univ. of Pittsburgh and PITT PACC, PA (United States). Dept of Physics and Astronomy
2. Brookhaven National Laboratory (BNL), Upton, NY (United States)
3. Univ. of Arizona, Tucson, AZ (United States)
4. Univ. College London (United Kingdom)
5. Fermi National Accelerator Laboratory (FNAL), Batavia, IL (United States)
6. SLAC National Accelerator Laboratory (SLAC), Menlo Park, CA (United States)
7. LAL Univ. Paris-Sud, Orsay (France)
8. Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States)
9. Univ. of North Dakota, Grand Forks, ND (United States)
10. National Optical Astronomy Observations, Tucson, AZ (United States)
11. New York Univ., NY (United States)
12. Univ. of Missouri at Kansas City, Kansas City, MO (United States)
13. Univ. of Utah, Salt Lake City, UT (United States)
14. Illinois Univ., Urbana, IL (United States)
15. Inst. Nacional de Investigaciones Nucleares (ININ), Escandon (Mexico)
16. Princeton Univ., Princeton, NJ (United States)
17. Australian National Univ., Canberra (Australia). Research School of Astronomy and Astrophysics
18. Campus of International Excellence UAM and CSIC, Madrid (Spain)
19. Univ. of Geneva (Switzerland). Astronomical Observatory
20. Stanford Univ., Stanford, CA (United States). Kavli Inst. for Particle Astrophysics and Cosmology
21. UNAM, Mexico City (Mexico). Dept. de Fisica Teorica and Inst. Avanzado de Cosmologia
22. Brown Univ., Providence, RI (United States)
23. State Univ. of New Jersey, Piscataway, NJ (United States)
24. NASA Goddard Space Flight Center (GSFC), Greenbelt, MD (United States)
25. Penn State Univ., University Park, PA (United States)
26. York Univ., Toronto, ON (Canada)
27. Yale Univ., New Haven, CT (United States)
28. Argelander-Inst. fuer Astronomie, Bonn (Germany)
29. Ohio State Univ., Columbus, OH (United States)
30. Carnegie Mellon Univ., Pittsburgh, PA (United States). McWilliams Center for Cosmology
31. Univ. of Michigan, Ann Arbor, MI (United States)
32. Univ. of Washington, Seattle, WA (United States)
33. Laboratoire d'Astrophysique, Ecole Polytechnique Federale de Lausanne (EPFL) (Swizerland)
34. France
35. Univ. College London, Bloomsbury (United Kingdom)
36. Texas A and M Univ., College Station, TX (United States)
37. Johns Hopkins Univ., Baltimore, MD (United States)
38. Univ. Autonoma de Barcelona (Spain). Inst. de Fisica d'Altes Energies (IFAE)
39. Univ. Paris-Sud, Orsay (France)
40. Siena College, Loudonville, NY (United States)
41. Univ. of Edinburgh (United Kingdom). Inst. for Astronomy, Royal Observatory
42. Korea Inst. for Advanced Study, Seoul (Korea, Republic of)
43. Jet Propulsion Lab./Caltech, Pasadena, CA (United States)
44. Laboratoire de Physique Subatomique et de Cosmologie Grenoble (France)
45. Univ. of California, Davis, CA (United States)
46. California Institute of Technology (Caltech), Pasadena, CA (United States)
47. Stanford Univ., Stanford, CA (United States)

Ongoing and near-future imaging-based dark energy experiments are critically dependent upon photometric redshifts (a.k.a. photo-z’s): i.e., estimates of the redshifts of objects based only on flux information obtained through broad filters. Higher-quality, lower-scatter photo-z’s will result in smaller random errors on cosmological parameters; while systematic errors in photometric redshift estimates, if not constrained, may dominate all other uncertainties from these experiments. The desired optimization and calibration is dependent upon spectroscopic measurements for secure redshift information; this is the key application of galaxy spectroscopy for imaging-based dark energy experiments. Hence, to achieve their full potential, imaging-based experiments will require large sets of objects with spectroscopically-determined redshifts, for two purposes: Training: Objects with known redshift are needed to map out the relationship between object color and z (or, equivalently, to determine empirically-calibrated templates describing the rest-frame spectra of the full range of galaxies, which may be used to predict the color-z relation). The ultimate goal of training is to minimize each moment of the distribution of differences between photometric redshift estimates and the true redshifts of objects, making the relationship between them as tight as possible. The larger and more complete our “training set” of spectroscopic redshifts is, the smaller the RMS photo-z errors should be, increasing the constraining power of imaging experiments; Requirements: Spectroscopic redshift measurements for ~30,000 objects over >~15 widely-separated regions, each at least ~20 arcmin in diameter, and reaching the faintest objects used in a given experiment, will likely be necessary if photometric redshifts are to be trained and calibrated with conventional techniques. Larger, more complete samples (i.e., with longer exposure times) can improve photo-z algorithms and reduce scatter further, enhancing the science return from planned experiments greatly (increasing the Dark Energy Task Force figure of merit by up to ~50%); Options: This spectroscopy will most efficiently be done by covering as much of the optical and near-infrared spectrum as possible at modestly high spectral resolution (λ/Δλ > ~3000), while maximizing the telescope collecting area, field of view on the sky, and multiplexing of simultaneous spectra. The most efficient instrument for this would likely be either the proposed GMACS/MANIFEST spectrograph for the Giant Magellan Telescope or the OPTIMOS spectrograph for the European Extremely Large Telescope, depending on actual properties when built. The PFS spectrograph at Subaru would be next best and available considerably earlier, c. 2018; the proposed ngCFHT and SSST telescopes would have similar capabilities but start later. Other key options, in order of increasing total time required, are the WFOS spectrograph at TMT, MOONS at the VLT, and DESI at the Mayall 4 m telescope (or the similar 4MOST and WEAVE projects); of these, only DESI, MOONS, and PFS are expected to be available before 2020. Table 2-3 of this white paper summarizes the observation time required at each facility for strawman training samples. To attain secure redshift measurements for a high fraction of targeted objects and cover the full redshift span of future experiments, additional near-infrared spectroscopy will also be required; this is best done from space, particularly with WFIRST-2.4 and JWST; Calibration: The first several moments of redshift distributions (the mean, RMS redshift dispersion, etc.), must be known to high accuracy for cosmological constraints not to be systematics-dominated (equivalently, the moments of the distribution of differences between photometric and true redshifts could be determined instead). The ultimate goal of calibration is to characterize these moments for every subsample used in analyses - i.e., to minimize the uncertainty in their mean redshift, RMS dispersion, etc. – rather than to make the moments themselves small. Calibration may be done with the same spectroscopic dataset used for training if that dataset is extremely high in redshift completeness (i.e., no populations of galaxies to be used in analyses are systematically missed). Accurate photo-z calibration is necessary for all imaging experiments; Requirements: If extremely low levels of systematic incompleteness (<~0.1%) are attained in training samples, the same datasets described above should be sufficient for calibration. However, existing deep spectroscopic surveys have failed to yield secure redshifts for 30–60% of targets, so that would require very large improvements over past experience. This incompleteness would be a limiting factor for training, but catastrophic for calibration. If <~0.1% incompleteness is not attainable, the best known option for calibration of photometric redshifts is to utilize cross-correlation statistics in some form. The most direct method for this uses cross-correlations between positions on the sky of bright objects of known spectroscopic redshift with the sample of objects that we wish to calibrate the redshift distribution for, measured as a function of spectroscopic z. For such a calibration, redshifts of ~100,000 objects over at least several hundred square degrees, spanning the full redshift range of the samples used for dark energy, would be necessary; and Options: The proposed BAO experiment eBOSS would provide sufficient spectroscopy for basic calibrations, particularly for ongoing and near-future imaging experiments. The planned DESI experiment would provide excellent calibration with redundant cross-checks, but will start after the conclusion of some imaging projects. An extension of DESI to the Southern hemisphere would provide the best possible calibration from cross-correlation methods for DES and LSST. We thus anticipate that our two primary needs for spectroscopy – training and calibration of photometric redshifts – will require two separate solutions. For ongoing and future projects to reach their full potential, new spectroscopic samples of faint objects will be needed for training; those new samples may be suitable for calibration, but the latter possibility is uncertain. In contrast, wide-area samples of bright objects are poorly suited for training, but can provide high-precision calibrations via cross-correlation techniques. Additional training/calibration redshifts and/or host galaxy spectroscopy would enhance the use of supernovae and galaxy clusters for cosmology. We also summarize additional work on photometric redshift techniques that will be needed to prepare for data from ongoing and future dark energy experiments.

• Research Organization: Brookhaven National Laboratory (BNL), Upton, NY (United States)
• Sponsoring Organization: USDOE Office of Science (SC), High Energy Physics (HEP)
• Grant/Contract Number: DE-SC00112704
• OSTI ID: 1172078
• Alternate ID(s): OSTI ID: 1250277
• Report Number(s): BNL-107334-2015-JA; KA2301020; TRN: US1500481
• Journal Information: Astroparticle Physics, Vol. 63; ISSN 0927-6505
• Publisher: Elsevier
• Country of Publication: United States
• Language: English

Related Subjects

79 ASTRONOMY AND ASTROPHYSICS
Cosmology
Dark energy