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DateDescription[XX.YY.rrr]sigma(r) Fishersigma(r) Fisher DCsigma(r) from sims (RF)bias(r) from sims (RF)bias from systematics (applies only to X=03) (RF)sigma(r) from sims (VB/JW/CB)bias(r) from sims (VB/JW/CB)All cases summary for just: A_L = 0.1, fsky=3%
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2016 FebFirst Optimization Results[01.00.000]6.8 x 10^(-4)
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2016 JulySecond Optimization (Science Book)[01.00a.000]9.1 x 10^(-4)Sky ModelDescription
(More info here)
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2017 JanData Challenge 1 --- Maps with Noise Levels that directly follow from the Science Book Optimization are created0
LCDM + Gaussian dust + Gaussian Sync
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2017 MarAnalysis of DC1.YY sims[01.00.000]6.8 x 10^(-4)
6.0 x 10^(-4)
6.6x10^(-4)-1.8x10^(-4)6.2x10^(-4)2.2x10^(-4)0a (just for analytic forecasts)
LCDM + Gaussian dust + Gaussian Sync + Dust/Sync Correlation + Dust Decorrelation
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[01.01.000]6.6x10^(-4)9.0x10^(-4)0b (just for analytic forecasts)
LCDM + Gaussian dust + Gaussian Sync + Dust/Sync Correlation + Dust Decorrelation + Sync Decorrelation
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[01.02.000]6.6x10^(-4)1
PySM run in a1d1f1s1 mode - i.e. with the default settings for AME, dust, free-free and synchrotron.
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2017 MarNew Band Definition2
PySM run in run in a2d4f1s3 mode - GitHub for some description of what this means.
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2017 AprThird Optimization[01.00b.000]8.4 x 10^(-4)3
Versus 02 this switches the dust model to a Hensley/Draine model and is dubbed a2d7f1s3
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[02a.00b.000]7.0 x 10^(-4)4
Tuhin Ghosh provided model which implements dust decorrelation
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2017 MayData Challenge 2 --- Maps with Noise Levels that directly follow the third optimization are created5
This is a toy model of dust which is highly decorrelated.
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2017 JulAnalysis of DC2.YY sims[02.00.000]5.4x10^(-4)2.8x10^(-5)6.70E-042.12E-056
RF model from MHD sims
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[02.01.000]8.6x10^(-4)1.1x10^(-4)7.80E-045.78E-04
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[02.02.000]8.0x10^(-4)-2.3x10^(-4)7.10E-043.14E-04
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[02.03.000]6.8x10^(-4)5.4x10^(-5)8.10E-041.79E-04
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[02.04.000]9.30E-04-3.35E-04
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[02.05.000]1.45E-03-2.49E-04
Experiment Definition
(More info here)
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[02.06.000]7.34E-041.07E-04
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Analysis of DC2.YY sims with r=.003[02.00.003]1.0x10^(-3)4.0x10^(-4)1.00E-033.16E-051fsky=3%
[30, 40, 85, 95, 145, 155, 220, 270]
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[02.01.003]1.0x10^(-3)2.0x10^(-5)9.01E-046.20E-042afsky=3%
[20, 30, 40, 85, 95, 145, 155, 220, 270]
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[02.02.003]1.0x10^(-3)-2.8x10^(-4)9.36E-043.50E-042afsky=3%
[20, 30, 40, 85, 95, 145, 155, 220, 270]
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[02.03.003]7.4x10^(-4)-1.4x10^(-4)1.02E-031.60E-042bfsky=1%
[20, 30, 40, 85, 95, 145, 155, 220, 270]
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[02.04.003]1.13E-03-1.80E-04
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[02.05.003]1.52E-033.00E-04
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[02.06.003]9.92E-041.10E-04
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2017 Jun/JulProposed Recipe for Additive Systematics2cfsky=10%
[20, 30, 40, 85, 95, 145, 155, 220, 270]
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2017 JulData Challenge 3 --- Maps with same Noise Levels as DC2.YY but with Additive Systematicsbias from systematics (from QE) (applies only to X=03) (VB/JW/CB/SP/BR)bias from systematics (from ML) (applies only to X=03) (VB/JW/CB/SP/BR)3afsky=3%
[20, 30, 40, 85, 95, 145, 155, 220, 270]
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2017, AugustAnalysis of DC3.YY sims (ILC)03.03.000.05.30.480.07.18E-040.00E+00-4.56e-083bfsky=1%
[20, 30, 40, 85, 95, 145, 155, 220, 270]
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2017, Septemberall numbers x10^(-4)03.03.000.16.01.00.847.98E-046.30E-053.34e-053cfsky=10%
[20, 30, 40, 85, 95, 145, 155, 220, 270]
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03.03.000.25.00.550.996.99E-048.47E-059.55e-05
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(analysis marked VB/JW/CB is actually03.03.000.36.31.61.27.34E-041.41E-048.49e-05
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from Steve P's estimator, please03.03.000.45.22.71.06.66E-049.65E-059.85e-05
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see this posting )03.03.000.55.61.80.897.53E-047.57E-056.70e-05
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03.03.000.65.52.40.986.91E-041.04E-048.31e-05
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03.03.000.75.61.91.17.92E-049.75E-058.22e-05
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03.03.003.07.0-1.80.09.14E-040.00E+00-1.65e-08
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03.03.003.16.7-0.401.09.71E-048.57E-055.49e-05
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03.03.003.28.31.60.878.74E-048.15E-058.53e-05
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03.03.003.36.51.51.69.29E-041.37E-041.13e-04
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03.03.003.47.21.40.829.44E-047.49E-059.32e-05
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03.03.003.57.0-0.290.919.21E-048.81E-056.23e-05
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03.03.003.66.7-1.11.37.96E-041.24E-049.34e-05
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03.03.003.76.7-1.11.31.04E-031.06E-041.01e-04
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2017, SeptemberDC4.YY
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2017, Sept.DC5.YY
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