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R Stompor - One of the best experts on this subject based on the ideXlab platform.

  • astrophysical foregrounds and primordial tensor to scalar ratio constraints from cosmic microwave background b mode polarization observations
    Physical Review D, 2012
    Co-Authors: J Errard, R Stompor
    Abstract:

    We study the effects of astrophysical foregrounds on the ability of CMB B-mode polarization experiments to constrain the primordial tensor-to-scalar ratio, r. To clean the foreground contributions we use parametric, maximum Likelihood Component separation technique, and consider experimental setups optimized to render a minimal level of the foreground residuals in the recovered CMB map. We consider nearly full-sky observations, include two diffuse foreground Components, dust and synchrotron, and study cases with and without calibration errors, spatial variability of the foreground properties, and partial or complete B-mode lensing signal removal. In all these cases we find that in the limit of very low noise level and in the absence of the intrumental or modeling systematic effects, the foreground residuals do not lead to a limit on the lowest detectable value of r. But the need to control the foreground residuals will play a major role in determining the minimal noise levels necessary to permit a robust detection of r < 0.1 and therefore in optimizing and forecasting the performance of the future missions. For current and proposed experiments noise levels, the foreground residuals are found non-negligible and potentially can affect our ability to set constraints on r. We also show how the constraints can be significantly improved on by restricting the post Component separation processing to a smaller sky area. This procedure applied to a case of a COrE-like satellite mission is shown to result potentially in over an order of magnitude improvement in the detectable value of r. With sufficient knowledge of the experimental bandpasses as well as foreground Component scaling laws, our conclusions are found to be independent on the assumed overall normalization of the foregrounds and only quantitatively depend on specific parametrizations assumed for the foreground Components.

  • maximum Likelihood parametric Component separation and cmb b mode detection in suborbital experiments
    Monthly Notices of the Royal Astronomical Society, 2010
    Co-Authors: F Stivoli, J Grain, S Leach, M Tristram, C Baccigalupi, R Stompor
    Abstract:

    We investigate the performance of the parametric maximum Likelihood Component separation method in the context of the cosmic microwave background (CMB) B-mode signal detection and its characterization by small-scale CMB suborbital experiments. We consider high-resolution (FWHM = 8′) balloon-borne and ground-based observatories mapping low dust-contrast sky areas of 400 and 1000 square degrees, in three frequency channels, 150, 250, 410 GHz, and 90, 150, 220 GHz, with sensitivity of order 1 to 10 μK per beam-size pixel. These are chosen to be representative of some of the proposed, next-generation, bolometric experiments. We study the residual foreground contributions left in the recovered CMB maps in the pixel and harmonic domain and discuss their impact on a determination of the tensor-to-scalar ratio, r. In particular, we find that the residuals derived from the simulated data of the considered balloon-borne observatories are sufficiently low not to be relevant for the B-mode science. However, the ground-based observatories are in need of some external information to permit satisfactory cleaning. We find that if such information is indeed available in the latter case, both the ground-based and balloon-borne experiments can detect the values of r as low as ∼0.04 at 95 per cent confidence level. The contribution of the foreground residuals to these limits is found to be then subdominant and these are driven by the statistical uncertainty due to CMB, including E-to-B leakage, and noise. We emphasize that reaching such levels will require a sufficient control of the level of systematic effects present in the data.

  • maximum Likelihood parametric Component separation and cmb b mode detection in suborbital experiments
    arXiv: Cosmology and Nongalactic Astrophysics, 2010
    Co-Authors: F Stivoli, J Grain, S Leach, M Tristram, C Baccigalupi, R Stompor
    Abstract:

    We investigate the performance of the parametric Maximum Likelihood Component separation method in the context of the CMB B-mode signal detection and its characterization by small-scale CMB suborbital experiments. We consider high-resolution (FWHM=8') balloon-borne and ground-based observatories mapping low dust-contrast sky areas of 400 and 1000 square degrees, in three frequency channels, 150, 250, 410 GHz, and 90, 150, 220 GHz, with sensitivity of order 1 to 10 micro-K per beam-size pixel. These are chosen to be representative of some of the proposed, next-generation, bolometric experiments. We study the residual foreground contributions left in the recovered CMB maps in the pixel and harmonic domain and discuss their impact on a determination of the tensor-to-scalar ratio, r. In particular, we find that the residuals derived from the simulated data of the considered balloon-borne observatories are sufficiently low not to be relevant for the B-mode science. However, the ground-based observatories are in need of some external information to permit satisfactory cleaning. We find that if such information is indeed available in the latter case, both the ground-based and balloon-borne experiments can detect the values of r as low as ~0.04 at 95% confidence level. The contribution of the foreground residuals to these limits is found to be then subdominant and these are driven by the statistical uncertainty due to CMB, including E-to-B leakage, and noise. We emphasize that reaching such levels will require a sufficient control of the level of systematic effects present in the data.

F Stivoli - One of the best experts on this subject based on the ideXlab platform.

  • maximum Likelihood parametric Component separation and cmb b mode detection in suborbital experiments
    Monthly Notices of the Royal Astronomical Society, 2010
    Co-Authors: F Stivoli, J Grain, S Leach, M Tristram, C Baccigalupi, R Stompor
    Abstract:

    We investigate the performance of the parametric maximum Likelihood Component separation method in the context of the cosmic microwave background (CMB) B-mode signal detection and its characterization by small-scale CMB suborbital experiments. We consider high-resolution (FWHM = 8′) balloon-borne and ground-based observatories mapping low dust-contrast sky areas of 400 and 1000 square degrees, in three frequency channels, 150, 250, 410 GHz, and 90, 150, 220 GHz, with sensitivity of order 1 to 10 μK per beam-size pixel. These are chosen to be representative of some of the proposed, next-generation, bolometric experiments. We study the residual foreground contributions left in the recovered CMB maps in the pixel and harmonic domain and discuss their impact on a determination of the tensor-to-scalar ratio, r. In particular, we find that the residuals derived from the simulated data of the considered balloon-borne observatories are sufficiently low not to be relevant for the B-mode science. However, the ground-based observatories are in need of some external information to permit satisfactory cleaning. We find that if such information is indeed available in the latter case, both the ground-based and balloon-borne experiments can detect the values of r as low as ∼0.04 at 95 per cent confidence level. The contribution of the foreground residuals to these limits is found to be then subdominant and these are driven by the statistical uncertainty due to CMB, including E-to-B leakage, and noise. We emphasize that reaching such levels will require a sufficient control of the level of systematic effects present in the data.

  • maximum Likelihood parametric Component separation and cmb b mode detection in suborbital experiments
    arXiv: Cosmology and Nongalactic Astrophysics, 2010
    Co-Authors: F Stivoli, J Grain, S Leach, M Tristram, C Baccigalupi, R Stompor
    Abstract:

    We investigate the performance of the parametric Maximum Likelihood Component separation method in the context of the CMB B-mode signal detection and its characterization by small-scale CMB suborbital experiments. We consider high-resolution (FWHM=8') balloon-borne and ground-based observatories mapping low dust-contrast sky areas of 400 and 1000 square degrees, in three frequency channels, 150, 250, 410 GHz, and 90, 150, 220 GHz, with sensitivity of order 1 to 10 micro-K per beam-size pixel. These are chosen to be representative of some of the proposed, next-generation, bolometric experiments. We study the residual foreground contributions left in the recovered CMB maps in the pixel and harmonic domain and discuss their impact on a determination of the tensor-to-scalar ratio, r. In particular, we find that the residuals derived from the simulated data of the considered balloon-borne observatories are sufficiently low not to be relevant for the B-mode science. However, the ground-based observatories are in need of some external information to permit satisfactory cleaning. We find that if such information is indeed available in the latter case, both the ground-based and balloon-borne experiments can detect the values of r as low as ~0.04 at 95% confidence level. The contribution of the foreground residuals to these limits is found to be then subdominant and these are driven by the statistical uncertainty due to CMB, including E-to-B leakage, and noise. We emphasize that reaching such levels will require a sufficient control of the level of systematic effects present in the data.

Mark N Maunder - One of the best experts on this subject based on the ideXlab platform.

  • can diagnostic tests help identify model misspecification in integrated stock assessments
    Fisheries Research, 2017
    Co-Authors: Felipe Carvalho, Andre E Punt, Yijay Chang, Mark N Maunder, Kevin R Piner
    Abstract:

    Abstract A variety of data types can be included in contemporary integrated stock assessments to simultaneously provide information on all estimated parameters. Conflicts between data, which are often a symptom of model misspecification and evident as model misfit, can affect the estimates of important parameters and derived quantities. Unfortunately, there are few standard diagnostic tools available for integrated stock assessment models that can provide the analyst with all the information needed to determine if there is substantial model misspecification. In this study, we use simulation methods to evaluate the ability of commonly-used and recently-proposed diagnostic tests to detect model misspecification in the observation model process (i.e., the incorrect form for survey selectivity), systems dynamics (i.e., incorrect assumed values for steepness of the stock-recruitment relationship and natural mortality), and incorrect data weighting. The diagnostic tests evaluated here were: i) residuals analysis (SDNR and runs test); ii) retrospective analysis; iii) the R 0 Likelihood Component profile; iv) the age-structured production model (ASPM); and v) catch-curve analysis (CCA). The efficacy of the diagnostic tests depended on whether the misspecification was in the observation or systems dynamics model. Residual analyses were easily the best detector of misspecification of the observation model while the ASPM test was the only good diagnostic for detecting misspecification of system dynamics model. Retrospective analysis and the R 0 Likelihood Component profile infrequently detected misspecified models, and CCA had a high probability of rejecting correctly-specified models. Finally, applying multiple carefully selected diagnostics can increase the power to detect misspecification without substantially increasing the probability of falsely concluding there is misspecification when the model is correctly specified.

  • evaluation of virgin recruitment profiling as a diagnostic for selectivity curve structure in integrated stock assessment models
    Fisheries Research, 2014
    Co-Authors: Mark N Maunder, Kevin R Piner, Shengping Wang, Alexandre Airesdasilva
    Abstract:

    Abstract Virgin recruitment (R0), the equilibrium recruitment in the absence of fishing, is an often used parameter in fisheries stock assessment for scaling population size. We describe and evaluate the use of the R0 Likelihood Component profile to diagnose selectivity misspecification, using simulation analysis for bigeye tuna in the eastern Pacific Ocean. The profile is evaluated under two types of selectivity misspecification: (1) misspecified shape and (2) misspecified temporal variation. The results indicate that length-composition data can provide substantial information on R0 estimation when the model is correctly specified, but can substantially bias estimates of absolute abundance when selectivity is misspecified. Although contradictory profiles for length-composition and abundance index data result from selectivity misspecification, they may not be useful in determining which survey or fishery selectivity is misspecified. The R0 profile selectivity diagnostic is based on the influence of composition data on absolute abundance. However, perhaps a more problematic and difficult to detect issue is the impact of length-composition data on biomass trends. The age-structured production model diagnostic could be applied to identify bias in both absolute biomass and biomass trend caused by age- or length-composition data in the presence of model misspecification.

  • Likelihood Component profiling as a data exploratory tool for north atlantic albacore
    2014
    Co-Authors: Laurence T Kell, Mark N Maunder, Kevin R Piner, Paul De Bruyn, I G Taylor
    Abstract:

    Biomass dynamic models are widely used in ICCAT for stock assessment and advice, parameters are estimated by fitting to time series of total catch and standardised catch per unit effort (CPUE) from fisheries. The later are assumed to track stock abundance. However it is not uncommon for such indices to contain sufficient information to estimate both parameters. Also indices may be conflicting and fitting therefore may involve weighted averages of contradictory CPUE data. This generally produces parameter estimates intermediate than would be obtained from the data sets individually (Schnute and Hilborn, 1993), who point out that the most likely parameter values are not intermediary to conflicting values; instead, they occur at one of the apparent extremes. We therefore use the ASPIC biomass model to explore uncertainty due to contradictory trends in time series of catch per unit effort (CPUE). We do this by calculating Likelihood profiles for the parameter K (carrying capacity or unfished biomass B0) and MSY (maximum sustainable yield).

Kevin R Piner - One of the best experts on this subject based on the ideXlab platform.

  • can diagnostic tests help identify model misspecification in integrated stock assessments
    Fisheries Research, 2017
    Co-Authors: Felipe Carvalho, Andre E Punt, Yijay Chang, Mark N Maunder, Kevin R Piner
    Abstract:

    Abstract A variety of data types can be included in contemporary integrated stock assessments to simultaneously provide information on all estimated parameters. Conflicts between data, which are often a symptom of model misspecification and evident as model misfit, can affect the estimates of important parameters and derived quantities. Unfortunately, there are few standard diagnostic tools available for integrated stock assessment models that can provide the analyst with all the information needed to determine if there is substantial model misspecification. In this study, we use simulation methods to evaluate the ability of commonly-used and recently-proposed diagnostic tests to detect model misspecification in the observation model process (i.e., the incorrect form for survey selectivity), systems dynamics (i.e., incorrect assumed values for steepness of the stock-recruitment relationship and natural mortality), and incorrect data weighting. The diagnostic tests evaluated here were: i) residuals analysis (SDNR and runs test); ii) retrospective analysis; iii) the R 0 Likelihood Component profile; iv) the age-structured production model (ASPM); and v) catch-curve analysis (CCA). The efficacy of the diagnostic tests depended on whether the misspecification was in the observation or systems dynamics model. Residual analyses were easily the best detector of misspecification of the observation model while the ASPM test was the only good diagnostic for detecting misspecification of system dynamics model. Retrospective analysis and the R 0 Likelihood Component profile infrequently detected misspecified models, and CCA had a high probability of rejecting correctly-specified models. Finally, applying multiple carefully selected diagnostics can increase the power to detect misspecification without substantially increasing the probability of falsely concluding there is misspecification when the model is correctly specified.

  • evaluation of virgin recruitment profiling as a diagnostic for selectivity curve structure in integrated stock assessment models
    Fisheries Research, 2014
    Co-Authors: Mark N Maunder, Kevin R Piner, Shengping Wang, Alexandre Airesdasilva
    Abstract:

    Abstract Virgin recruitment (R0), the equilibrium recruitment in the absence of fishing, is an often used parameter in fisheries stock assessment for scaling population size. We describe and evaluate the use of the R0 Likelihood Component profile to diagnose selectivity misspecification, using simulation analysis for bigeye tuna in the eastern Pacific Ocean. The profile is evaluated under two types of selectivity misspecification: (1) misspecified shape and (2) misspecified temporal variation. The results indicate that length-composition data can provide substantial information on R0 estimation when the model is correctly specified, but can substantially bias estimates of absolute abundance when selectivity is misspecified. Although contradictory profiles for length-composition and abundance index data result from selectivity misspecification, they may not be useful in determining which survey or fishery selectivity is misspecified. The R0 profile selectivity diagnostic is based on the influence of composition data on absolute abundance. However, perhaps a more problematic and difficult to detect issue is the impact of length-composition data on biomass trends. The age-structured production model diagnostic could be applied to identify bias in both absolute biomass and biomass trend caused by age- or length-composition data in the presence of model misspecification.

  • Likelihood Component profiling as a data exploratory tool for north atlantic albacore
    2014
    Co-Authors: Laurence T Kell, Mark N Maunder, Kevin R Piner, Paul De Bruyn, I G Taylor
    Abstract:

    Biomass dynamic models are widely used in ICCAT for stock assessment and advice, parameters are estimated by fitting to time series of total catch and standardised catch per unit effort (CPUE) from fisheries. The later are assumed to track stock abundance. However it is not uncommon for such indices to contain sufficient information to estimate both parameters. Also indices may be conflicting and fitting therefore may involve weighted averages of contradictory CPUE data. This generally produces parameter estimates intermediate than would be obtained from the data sets individually (Schnute and Hilborn, 1993), who point out that the most likely parameter values are not intermediary to conflicting values; instead, they occur at one of the apparent extremes. We therefore use the ASPIC biomass model to explore uncertainty due to contradictory trends in time series of catch per unit effort (CPUE). We do this by calculating Likelihood profiles for the parameter K (carrying capacity or unfished biomass B0) and MSY (maximum sustainable yield).

M Tristram - One of the best experts on this subject based on the ideXlab platform.

  • maximum Likelihood parametric Component separation and cmb b mode detection in suborbital experiments
    Monthly Notices of the Royal Astronomical Society, 2010
    Co-Authors: F Stivoli, J Grain, S Leach, M Tristram, C Baccigalupi, R Stompor
    Abstract:

    We investigate the performance of the parametric maximum Likelihood Component separation method in the context of the cosmic microwave background (CMB) B-mode signal detection and its characterization by small-scale CMB suborbital experiments. We consider high-resolution (FWHM = 8′) balloon-borne and ground-based observatories mapping low dust-contrast sky areas of 400 and 1000 square degrees, in three frequency channels, 150, 250, 410 GHz, and 90, 150, 220 GHz, with sensitivity of order 1 to 10 μK per beam-size pixel. These are chosen to be representative of some of the proposed, next-generation, bolometric experiments. We study the residual foreground contributions left in the recovered CMB maps in the pixel and harmonic domain and discuss their impact on a determination of the tensor-to-scalar ratio, r. In particular, we find that the residuals derived from the simulated data of the considered balloon-borne observatories are sufficiently low not to be relevant for the B-mode science. However, the ground-based observatories are in need of some external information to permit satisfactory cleaning. We find that if such information is indeed available in the latter case, both the ground-based and balloon-borne experiments can detect the values of r as low as ∼0.04 at 95 per cent confidence level. The contribution of the foreground residuals to these limits is found to be then subdominant and these are driven by the statistical uncertainty due to CMB, including E-to-B leakage, and noise. We emphasize that reaching such levels will require a sufficient control of the level of systematic effects present in the data.

  • maximum Likelihood parametric Component separation and cmb b mode detection in suborbital experiments
    arXiv: Cosmology and Nongalactic Astrophysics, 2010
    Co-Authors: F Stivoli, J Grain, S Leach, M Tristram, C Baccigalupi, R Stompor
    Abstract:

    We investigate the performance of the parametric Maximum Likelihood Component separation method in the context of the CMB B-mode signal detection and its characterization by small-scale CMB suborbital experiments. We consider high-resolution (FWHM=8') balloon-borne and ground-based observatories mapping low dust-contrast sky areas of 400 and 1000 square degrees, in three frequency channels, 150, 250, 410 GHz, and 90, 150, 220 GHz, with sensitivity of order 1 to 10 micro-K per beam-size pixel. These are chosen to be representative of some of the proposed, next-generation, bolometric experiments. We study the residual foreground contributions left in the recovered CMB maps in the pixel and harmonic domain and discuss their impact on a determination of the tensor-to-scalar ratio, r. In particular, we find that the residuals derived from the simulated data of the considered balloon-borne observatories are sufficiently low not to be relevant for the B-mode science. However, the ground-based observatories are in need of some external information to permit satisfactory cleaning. We find that if such information is indeed available in the latter case, both the ground-based and balloon-borne experiments can detect the values of r as low as ~0.04 at 95% confidence level. The contribution of the foreground residuals to these limits is found to be then subdominant and these are driven by the statistical uncertainty due to CMB, including E-to-B leakage, and noise. We emphasize that reaching such levels will require a sufficient control of the level of systematic effects present in the data.