SGWB data analysis for Radler R. Buscicchio, G. Nardini, A. Petiteau - - PowerPoint PPT Presentation

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SGWB data analysis for Radler

• R. Buscicchio, G. Nardini, A. Petiteau

5th Cosmology Working Group Workshop Helsinki - June 12, 2018

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A brief introduction to...

• Resources available
• Access Radler data
• Understand the LDC pipeline
• Build your own data
• Perform some preliminary estimates

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SLIDE 3

A brief introduction to...

• Resources available
• Access Radler data
• Understand the LDC pipeline
• Build your own data
• Perform some preliminary estimates

2

SLIDE 4

A brief introduction to...

• Resources available
• Access Radler data
• Understand the LDC pipeline
• Build your own data
• Perform some preliminary estimates

2

SLIDE 5

A brief introduction to...

• Resources available
• Access Radler data
• Understand the LDC pipeline
• Build your own data
• Perform some preliminary estimates

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SLIDE 6

A brief introduction to...

• Resources available
• Access Radler data
• Understand the LDC pipeline
• Build your own data
• Perform some preliminary estimates

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SLIDE 7

Resources

• Docker environments:
• Docker for user: gitlab-registry.in2p3.fr/stas/mldc:master
• Docker with jupyter support:

gitlab-registry.in2p3.fr/elisadpc/docker:ldc_jupyter

• LISA Data Challenge repository:
• User and Developer branch: https://gitlab.in2p3.fr/stas/MLDC
• How to mount/install the latter into the former:

https://gitlab.in2p3.fr/stas/MLDC/blob/master/README.md

• Data stored in .hdf5 files
• Lisa Data Challenge Manual: https://lisa-ldc.lal.in2p3.fr/doc

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Resources

• Docker environments:
• Docker for user: gitlab-registry.in2p3.fr/stas/mldc:master
• Docker with jupyter support:

gitlab-registry.in2p3.fr/elisadpc/docker:ldc_jupyter

• LISA Data Challenge repository:
• User and Developer branch: https://gitlab.in2p3.fr/stas/MLDC
• How to mount/install the latter into the former:

https://gitlab.in2p3.fr/stas/MLDC/blob/master/README.md

• Data stored in .hdf5 files
• Lisa Data Challenge Manual: https://lisa-ldc.lal.in2p3.fr/doc

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Resources

• Docker environments:
• Docker for user: gitlab-registry.in2p3.fr/stas/mldc:master
• Docker with jupyter support:

gitlab-registry.in2p3.fr/elisadpc/docker:ldc_jupyter

• LISA Data Challenge repository:
• User and Developer branch: https://gitlab.in2p3.fr/stas/MLDC
• How to mount/install the latter into the former:

https://gitlab.in2p3.fr/stas/MLDC/blob/master/README.md

• Data stored in .hdf5 files
• Lisa Data Challenge Manual: https://lisa-ldc.lal.in2p3.fr/doc

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Radler Data

Reference page: https://lisa-ldc.lal.in2p3.fr/home Stochastic signal: Sim_LISA_SGWB_12345_NoNoise.hdf5 Sim_LISA_SGWB_12345_Noises.hdf5 Sim_LISA_SGWB_12345_NoiseRand.hdf5 We’ll come back to these in a bit...

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Radler Data

Generic case: Mix type of sources in the same dataset with increasing complexity as example GB+MBHB, EMRI+GB, SGWB+MBHB...

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Radler Data

Stochastic: Choosing the sources is still performed, but no SNR estimate or catalogues lookup for SGWB.

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Input parameter file

SGWB basic input file & Superimposing sources

============================== SourceType SGWB NumberSources 1 Approximant LISACode2SGWB_4 Sky Isotropic FrequencyShape PowerLaw EnergySlope 0.666667 FrequencyRef 25 EnergyAmplitude 0.5e-9:4.5e-9 ============================== ============================== SourceType MBHB NumberSources 1 Catalogues "catalogues/MBHs/catalog_Q3_delay_real106.out" CoalescenceTime 0.1-0.25 MassRatio 1.0-10.0 Spin1 0.5-0.99 Spin2 0.5-0.99 Model IMRPhenomD RequestSNR 100.0-500.0 TimeStep 10.0 ObservationDuration 7864320.0 ============================== 7

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Input parameter file

SGWB basic input file & Superimposing sources

============================== SourceType SGWB NumberSources 1 Approximant LISACode2SGWB_4 Sky Isotropic FrequencyShape PowerLaw EnergySlope 0.666667 FrequencyRef 25 EnergyAmplitude 0.5e-9:4.5e-9 ============================== ============================== SourceType MBHB NumberSources 1 Catalogues "catalogues/MBHs/catalog_Q3_delay_real106.out" CoalescenceTime 0.1-0.25 MassRatio 1.0-10.0 Spin1 0.5-0.99 Spin2 0.5-0.99 Model IMRPhenomD RequestSNR 100.0-500.0 TimeStep 10.0 ObservationDuration 7864320.0 ============================== 7

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Pipeline

• ChooseSources.py --paramFile=Param.txt --filename=MySim_Param.hdf5
• -seed=12345
• Computehphc.py MySim_Param.hdf5 [Ignore if no superimposition]
• ConfigureInstrument.py --TDI=”X,Y,Z” --duration=125829105.0
• -timeStep=15.0 --orbits=LISACode_Orbits MySim_Param.hdf5
• ConfigureNoises.py --LevelRandom=30 MySim_Param.hdf5

Amount of noise randomisation:

PSD PSD

• RunSimulLC2.py --debug=False --verbose=False --path2LisaCode=
• -NoNoise=False --NoGW=False --seed=12345 MySim_Param.hdf5

SIMUL

hours

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Pipeline

• ChooseSources.py --paramFile=Param.txt --filename=MySim_Param.hdf5
• -seed=12345
• Computehphc.py MySim_Param.hdf5 [Ignore if no superimposition]
• ConfigureInstrument.py --TDI=”X,Y,Z” --duration=125829105.0
• -timeStep=15.0 --orbits=LISACode_Orbits MySim_Param.hdf5
• ConfigureNoises.py --LevelRandom=30 MySim_Param.hdf5

Amount of noise randomisation:

PSD PSD

• RunSimulLC2.py --debug=False --verbose=False --path2LisaCode=
• -NoNoise=False --NoGW=False --seed=12345 MySim_Param.hdf5

SIMUL

hours

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Pipeline

• ChooseSources.py --paramFile=Param.txt --filename=MySim_Param.hdf5
• -seed=12345
• Computehphc.py MySim_Param.hdf5 [Ignore if no superimposition]
• ConfigureInstrument.py --TDI=”X,Y,Z” --duration=125829105.0
• -timeStep=15.0 --orbits=LISACode_Orbits MySim_Param.hdf5
• ConfigureNoises.py --LevelRandom=30 MySim_Param.hdf5

Amount of noise randomisation:

PSD PSD

• RunSimulLC2.py --debug=False --verbose=False --path2LisaCode=
• -NoNoise=False --NoGW=False --seed=12345 MySim_Param.hdf5

SIMUL

hours

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Pipeline

• ChooseSources.py --paramFile=Param.txt --filename=MySim_Param.hdf5
• -seed=12345
• Computehphc.py MySim_Param.hdf5 [Ignore if no superimposition]
• ConfigureInstrument.py --TDI=”X,Y,Z” --duration=125829105.0
• -timeStep=15.0 --orbits=LISACode_Orbits MySim_Param.hdf5
• ConfigureNoises.py --LevelRandom=30 MySim_Param.hdf5

Amount of noise randomisation:

PSD = PSD0 ( 1 + U (−x, x) 100 )

• RunSimulLC2.py --debug=False --verbose=False --path2LisaCode=
• -NoNoise=False --NoGW=False --seed=12345 MySim_Param.hdf5

SIMUL

hours

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Pipeline

• ChooseSources.py --paramFile=Param.txt --filename=MySim_Param.hdf5
• -seed=12345
• Computehphc.py MySim_Param.hdf5 [Ignore if no superimposition]
• ConfigureInstrument.py --TDI=”X,Y,Z” --duration=125829105.0
• -timeStep=15.0 --orbits=LISACode_Orbits MySim_Param.hdf5
• ConfigureNoises.py --LevelRandom=30 MySim_Param.hdf5

Amount of noise randomisation:

PSD = PSD0 ( 1 + U (−x, x) 100 )

• RunSimulLC2.py --debug=False --verbose=False --path2LisaCode=
• -NoNoise=False --NoGW=False --seed=12345 MySim_Param.hdf5

TSIMUL ∼ 8hours

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Overview of Radler Data

TDIs: the line at high frequency is (partially) absorbed by the response function.

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PSD (Hz

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TDI Power Spectral Density (NoNoise)

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PSD (Hz

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TDI Power Spectral Density (Noises)

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PSD (Hz

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TDI Power Spectral Density (Random Noises)

T A E

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SLIDE 21

Overview of Radler Data

TDIs: the line at high frequency is (partially) absorbed by the response function.

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Frequency (Hz) 10

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PSD (Hz

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TDI Power Spectral Density (NoNoise)

X A E 10

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Frequency (Hz) 10

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PSD (Hz

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TDI Power Spectral Density (Noises)

X A E 10

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Frequency (Hz) 10

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PSD (Hz

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TDI Power Spectral Density (Random Noises)

T A E

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SLIDE 22

Overview of Radler Data

TDIs: the line at high frequency is (partially) absorbed by the response function.

10

5

10

4

10

3

10

2

10

1

Frequency (Hz) 10

53

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PSD (Hz

1)

TDI Power Spectral Density (NoNoise)

X A E 10

5

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10

3

10

2

10

1

Frequency (Hz) 10

48

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PSD (Hz

1)

TDI Power Spectral Density (Noises)

X A E 10

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Frequency (Hz) 10

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PSD (Hz

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TDI Power Spectral Density (Random Noises)

T A E

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Overview of Radler data

By a quick and dirty check, fits agree with the input within 1σ.

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Input Vs Data Vs Fit

Data Paramfile Fit 10

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PSD (Hz-1)

Am channel data vs analytic model

Noise data Model

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Build your own Data

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Fits over 2 weeks data = (0, 2/3, 2/3)

Params Fit

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An “exercise”

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Energy density, = 2/3

Powerlaw sensitivity

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An “exercise”

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Energy density, = 2/3

Powerlaw sensitivity Input

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An “exercise”

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Energy density, = 2/3

Powerlaw sensitivity Input

(Am)

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SLIDE 28

An “exercise”

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Energy density, = 2/3

Powerlaw sensitivity Input

(Am)

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SLIDE 29

What next?

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Omega equivalent spectra (w and w/o Noise)

signal only from Am signal + noises from Am

P a r a m e t e r E s t i m a t i

• n

THANK YOU!

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