Spectroscopic needs for imaging dark energy experiments

Jeffrey A. Newman, Alexandra Abate, Filipe B. Abdalla, Sahar Allam, Steven W. Allen, Réza Ansari, Stephen Bailey, Wayne A. Barkhouse, Timothy C. Beers, Michael R. Blanton, Mark Brodwin, Joel R. Brownstein, Robert J. Brunner, Matias Carrasco Kind, Jorge L. Cervantes-Cota, Elliott Cheu, Nora Elisa Chisari, Matthew Colless, Johan Comparat, Jean CouponCarlos E. Cunha, Axel De La Macorra, Ian P. Dell'Antonio, Brenda L. Frye, Eric J. Gawiser, Neil Gehrels, Kevin Grady, Alex Hagen, Patrick B. Hall, Andew P. Hearin, Hendrik Hildebrandt, Christopher M. Hirata, Shirley Ho, Klaus Honscheid, Dragan Huterer, Željko Ivezić, Jean Paul Kneib, Jeffrey W. Kruk, Ofer Lahav, Rachel Mandelbaum, Jennifer L. Marshall, Daniel J. Matthews, Brice Ménard, Ramon Miquel, Marc Moniez, H. W. Moos, John Moustakas, Adam D. Myers, Casey Papovich, John A. Peacock, Changbom Park, Mubdi Rahman, Jason Rhodes, Jean Stephane Ricol, Iftach Sadeh, Anže Slozar, Samuel J. Schmidt, Daniel K. Stern, J. Anthony Tyson, Anja Von Der Linden, Risa H. Wechsler, W. M. Wood-Vasey, Andrew R. Zentner

    Research output: Contribution to journalArticle

    Abstract

    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.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.

    Original languageEnglish (US)
    Pages (from-to)81-100
    Number of pages20
    JournalAstroparticle Physics
    Volume63
    DOIs
    StatePublished - Mar 15 2015

    Fingerprint

    dark energy
    education
    cross correlation
    spectrographs
    telescopes
    galaxies
    moments
    faint objects
    spectroscopy
    natural satellites
    sky
    estimates
    color
    James Webb Space Telescope
    requirements
    random errors
    Southern Hemisphere
    completeness
    multiplexing
    figure of merit

    Keywords

    • Cosmology
    • Dark energy
    • Surveys

    ASJC Scopus subject areas

    • Astronomy and Astrophysics

    Cite this

    Newman, J. A., Abate, A., Abdalla, F. B., Allam, S., Allen, S. W., Ansari, R., ... Zentner, A. R. (2015). Spectroscopic needs for imaging dark energy experiments. Astroparticle Physics, 63, 81-100. https://doi.org/10.1016/j.astropartphys.2014.06.007

    Spectroscopic needs for imaging dark energy experiments. / Newman, Jeffrey A.; Abate, Alexandra; Abdalla, Filipe B.; Allam, Sahar; Allen, Steven W.; Ansari, Réza; Bailey, Stephen; Barkhouse, Wayne A.; Beers, Timothy C.; Blanton, Michael R.; Brodwin, Mark; Brownstein, Joel R.; Brunner, Robert J.; Carrasco Kind, Matias; Cervantes-Cota, Jorge L.; Cheu, Elliott; Chisari, Nora Elisa; Colless, Matthew; Comparat, Johan; Coupon, Jean; Cunha, Carlos E.; De La Macorra, Axel; Dell'Antonio, Ian P.; Frye, Brenda L.; Gawiser, Eric J.; Gehrels, Neil; Grady, Kevin; Hagen, Alex; Hall, Patrick B.; Hearin, Andew P.; Hildebrandt, Hendrik; Hirata, Christopher M.; Ho, Shirley; Honscheid, Klaus; Huterer, Dragan; Ivezić, Željko; Kneib, Jean Paul; Kruk, Jeffrey W.; Lahav, Ofer; Mandelbaum, Rachel; Marshall, Jennifer L.; Matthews, Daniel J.; Ménard, Brice; Miquel, Ramon; Moniez, Marc; Moos, H. W.; Moustakas, John; Myers, Adam D.; Papovich, Casey; Peacock, John A.; Park, Changbom; Rahman, Mubdi; Rhodes, Jason; Ricol, Jean Stephane; Sadeh, Iftach; Slozar, Anže; Schmidt, Samuel J.; Stern, Daniel K.; Anthony Tyson, J.; Von Der Linden, Anja; Wechsler, Risa H.; Wood-Vasey, W. M.; Zentner, Andrew R.

    In: Astroparticle Physics, Vol. 63, 15.03.2015, p. 81-100.

    Research output: Contribution to journalArticle

    Newman, JA, Abate, A, Abdalla, FB, Allam, S, Allen, SW, Ansari, R, Bailey, S, Barkhouse, WA, Beers, TC, Blanton, MR, Brodwin, M, Brownstein, JR, Brunner, RJ, Carrasco Kind, M, Cervantes-Cota, JL, Cheu, E, Chisari, NE, Colless, M, Comparat, J, Coupon, J, Cunha, CE, De La Macorra, A, Dell'Antonio, IP, Frye, BL, Gawiser, EJ, Gehrels, N, Grady, K, Hagen, A, Hall, PB, Hearin, AP, Hildebrandt, H, Hirata, CM, Ho, S, Honscheid, K, Huterer, D, Ivezić, Ž, Kneib, JP, Kruk, JW, Lahav, O, Mandelbaum, R, Marshall, JL, Matthews, DJ, Ménard, B, Miquel, R, Moniez, M, Moos, HW, Moustakas, J, Myers, AD, Papovich, C, Peacock, JA, Park, C, Rahman, M, Rhodes, J, Ricol, JS, Sadeh, I, Slozar, A, Schmidt, SJ, Stern, DK, Anthony Tyson, J, Von Der Linden, A, Wechsler, RH, Wood-Vasey, WM & Zentner, AR 2015, 'Spectroscopic needs for imaging dark energy experiments', Astroparticle Physics, vol. 63, pp. 81-100. https://doi.org/10.1016/j.astropartphys.2014.06.007
    Newman JA, Abate A, Abdalla FB, Allam S, Allen SW, Ansari R et al. Spectroscopic needs for imaging dark energy experiments. Astroparticle Physics. 2015 Mar 15;63:81-100. https://doi.org/10.1016/j.astropartphys.2014.06.007
    Newman, Jeffrey A. ; Abate, Alexandra ; Abdalla, Filipe B. ; Allam, Sahar ; Allen, Steven W. ; Ansari, Réza ; Bailey, Stephen ; Barkhouse, Wayne A. ; Beers, Timothy C. ; Blanton, Michael R. ; Brodwin, Mark ; Brownstein, Joel R. ; Brunner, Robert J. ; Carrasco Kind, Matias ; Cervantes-Cota, Jorge L. ; Cheu, Elliott ; Chisari, Nora Elisa ; Colless, Matthew ; Comparat, Johan ; Coupon, Jean ; Cunha, Carlos E. ; De La Macorra, Axel ; Dell'Antonio, Ian P. ; Frye, Brenda L. ; Gawiser, Eric J. ; Gehrels, Neil ; Grady, Kevin ; Hagen, Alex ; Hall, Patrick B. ; Hearin, Andew P. ; Hildebrandt, Hendrik ; Hirata, Christopher M. ; Ho, Shirley ; Honscheid, Klaus ; Huterer, Dragan ; Ivezić, Željko ; Kneib, Jean Paul ; Kruk, Jeffrey W. ; Lahav, Ofer ; Mandelbaum, Rachel ; Marshall, Jennifer L. ; Matthews, Daniel J. ; Ménard, Brice ; Miquel, Ramon ; Moniez, Marc ; Moos, H. W. ; Moustakas, John ; Myers, Adam D. ; Papovich, Casey ; Peacock, John A. ; Park, Changbom ; Rahman, Mubdi ; Rhodes, Jason ; Ricol, Jean Stephane ; Sadeh, Iftach ; Slozar, Anže ; Schmidt, Samuel J. ; Stern, Daniel K. ; Anthony Tyson, J. ; Von Der Linden, Anja ; Wechsler, Risa H. ; Wood-Vasey, W. M. ; Zentner, Andrew R. / Spectroscopic needs for imaging dark energy experiments. In: Astroparticle Physics. 2015 ; Vol. 63. pp. 81-100.
    @article{5f180f69abcc42859546d24dcfc18d03,
    title = "Spectroscopic needs for imaging dark energy experiments",
    abstract = "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.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.",
    keywords = "Cosmology, Dark energy, Surveys",
    author = "Newman, {Jeffrey A.} and Alexandra Abate and Abdalla, {Filipe B.} and Sahar Allam and Allen, {Steven W.} and R{\'e}za Ansari and Stephen Bailey and Barkhouse, {Wayne A.} and Beers, {Timothy C.} and Blanton, {Michael R.} and Mark Brodwin and Brownstein, {Joel R.} and Brunner, {Robert J.} and {Carrasco Kind}, Matias and Cervantes-Cota, {Jorge L.} and Elliott Cheu and Chisari, {Nora Elisa} and Matthew Colless and Johan Comparat and Jean Coupon and Cunha, {Carlos E.} and {De La Macorra}, Axel and Dell'Antonio, {Ian P.} and Frye, {Brenda L.} and Gawiser, {Eric J.} and Neil Gehrels and Kevin Grady and Alex Hagen and Hall, {Patrick B.} and Hearin, {Andew P.} and Hendrik Hildebrandt and Hirata, {Christopher M.} and Shirley Ho and Klaus Honscheid and Dragan Huterer and Željko Ivezić and Kneib, {Jean Paul} and Kruk, {Jeffrey W.} and Ofer Lahav and Rachel Mandelbaum and Marshall, {Jennifer L.} and Matthews, {Daniel J.} and Brice M{\'e}nard and Ramon Miquel and Marc Moniez and Moos, {H. W.} and John Moustakas and Myers, {Adam D.} and Casey Papovich and Peacock, {John A.} and Changbom Park and Mubdi Rahman and Jason Rhodes and Ricol, {Jean Stephane} and Iftach Sadeh and Anže Slozar and Schmidt, {Samuel J.} and Stern, {Daniel K.} and {Anthony Tyson}, J. and {Von Der Linden}, Anja and Wechsler, {Risa H.} and Wood-Vasey, {W. M.} and Zentner, {Andrew R.}",
    year = "2015",
    month = "3",
    day = "15",
    doi = "10.1016/j.astropartphys.2014.06.007",
    language = "English (US)",
    volume = "63",
    pages = "81--100",
    journal = "Astroparticle Physics",
    issn = "0927-6505",
    publisher = "Elsevier",

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    TY - JOUR

    T1 - Spectroscopic needs for imaging dark energy experiments

    AU - Newman, Jeffrey A.

    AU - Abate, Alexandra

    AU - Abdalla, Filipe B.

    AU - Allam, Sahar

    AU - Allen, Steven W.

    AU - Ansari, Réza

    AU - Bailey, Stephen

    AU - Barkhouse, Wayne A.

    AU - Beers, Timothy C.

    AU - Blanton, Michael R.

    AU - Brodwin, Mark

    AU - Brownstein, Joel R.

    AU - Brunner, Robert J.

    AU - Carrasco Kind, Matias

    AU - Cervantes-Cota, Jorge L.

    AU - Cheu, Elliott

    AU - Chisari, Nora Elisa

    AU - Colless, Matthew

    AU - Comparat, Johan

    AU - Coupon, Jean

    AU - Cunha, Carlos E.

    AU - De La Macorra, Axel

    AU - Dell'Antonio, Ian P.

    AU - Frye, Brenda L.

    AU - Gawiser, Eric J.

    AU - Gehrels, Neil

    AU - Grady, Kevin

    AU - Hagen, Alex

    AU - Hall, Patrick B.

    AU - Hearin, Andew P.

    AU - Hildebrandt, Hendrik

    AU - Hirata, Christopher M.

    AU - Ho, Shirley

    AU - Honscheid, Klaus

    AU - Huterer, Dragan

    AU - Ivezić, Željko

    AU - Kneib, Jean Paul

    AU - Kruk, Jeffrey W.

    AU - Lahav, Ofer

    AU - Mandelbaum, Rachel

    AU - Marshall, Jennifer L.

    AU - Matthews, Daniel J.

    AU - Ménard, Brice

    AU - Miquel, Ramon

    AU - Moniez, Marc

    AU - Moos, H. W.

    AU - Moustakas, John

    AU - Myers, Adam D.

    AU - Papovich, Casey

    AU - Peacock, John A.

    AU - Park, Changbom

    AU - Rahman, Mubdi

    AU - Rhodes, Jason

    AU - Ricol, Jean Stephane

    AU - Sadeh, Iftach

    AU - Slozar, Anže

    AU - Schmidt, Samuel J.

    AU - Stern, Daniel K.

    AU - Anthony Tyson, J.

    AU - Von Der Linden, Anja

    AU - Wechsler, Risa H.

    AU - Wood-Vasey, W. M.

    AU - Zentner, Andrew R.

    PY - 2015/3/15

    Y1 - 2015/3/15

    N2 - 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.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.

    AB - 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.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.

    KW - Cosmology

    KW - Dark energy

    KW - Surveys

    UR - http://www.scopus.com/inward/record.url?scp=84908202929&partnerID=8YFLogxK

    UR - http://www.scopus.com/inward/citedby.url?scp=84908202929&partnerID=8YFLogxK

    U2 - 10.1016/j.astropartphys.2014.06.007

    DO - 10.1016/j.astropartphys.2014.06.007

    M3 - Article

    VL - 63

    SP - 81

    EP - 100

    JO - Astroparticle Physics

    JF - Astroparticle Physics

    SN - 0927-6505

    ER -