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

SGWB data analysis for Radler R. Buscicchio, G. Nardini, A. Petiteau 5 th Cosmology Working Group Workshop Helsinki - June 12, 2018 1

• Access Radler data • Understand the LDC pipeline • Build your own data • Perform some preliminary estimates A brief introduction to... • Resources available 2

• Understand the LDC pipeline • Build your own data • Perform some preliminary estimates A brief introduction to... • Resources available • Access Radler data 2

• Build your own data • Perform some preliminary estimates A brief introduction to... • Resources available • Access Radler data • Understand the LDC pipeline 2

• Perform some preliminary estimates A brief introduction to... • Resources available • Access Radler data • Understand the LDC pipeline • Build your own data 2

A brief introduction to... • Resources available • Access Radler data • Understand the LDC pipeline • Build your own data • Perform some preliminary estimates 2

• 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 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 3

• Data stored in .hdf5 files • Lisa Data Challenge Manual: https://lisa-ldc.lal.in2p3.fr/doc 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 3

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 3

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... 4

Radler Data Generic case: Mix type of sources in the same dataset with increasing complexity as example GB+MBHB, EMRI+GB, SGWB+MBHB... 5

Radler Data Stochastic: Choosing the sources is still performed, but no SNR estimate or catalogues lookup for SGWB. 6

============================== 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 ============================== 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 ============================== 7

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 7 ObservationDuration 7864320.0 ==============================

• 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 hours SIMUL Pipeline • ChooseSources.py --paramFile=Param.txt --filename=MySim_Param.hdf5 --seed=12345 8

• 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 hours SIMUL Pipeline • ChooseSources.py --paramFile=Param.txt --filename=MySim_Param.hdf5 --seed=12345 • Computehphc.py MySim_Param.hdf5 [Ignore if no superimposition] 8

• 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 hours SIMUL 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 8

• RunSimulLC2.py --debug=False --verbose=False --path2LisaCode= --NoNoise=False --NoGW=False --seed=12345 MySim_Param.hdf5 hours SIMUL 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: ( ) 1 + U ( − x, x ) PSD = PSD 0 100 8

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: ( ) 1 + U ( − x, x ) PSD = PSD 0 100 • RunSimulLC2.py --debug=False --verbose=False --path2LisaCode= --NoNoise=False --NoGW=False --seed=12345 MySim_Param.hdf5 T SIMUL ∼ 8 hours 8

TDI Power Spectral Density (Random Noises) 10 38 10 40 1 ) 10 42 PSD ( Hz 10 44 10 46 T A 10 48 E 10 5 10 4 10 3 10 2 10 1 Frequency ( Hz ) Overview of Radler Data TDIs: the line at high frequency is (partially) absorbed by the response function. TDI Power Spectral Density (NoNoise) X 10 43 A E 10 45 1 ) 10 47 PSD ( Hz 10 49 10 51 10 53 10 5 10 4 10 3 10 2 10 1 Frequency ( Hz ) TDI Power Spectral Density (Noises) 10 38 10 40 10 42 1 ) PSD ( Hz 10 44 10 46 X A 10 48 E 9 10 5 10 4 10 3 10 2 10 1 Frequency ( Hz )

Overview of Radler Data TDIs: the line at high frequency is (partially) absorbed by the response function. TDI Power Spectral Density (NoNoise) X 10 43 A E 10 45 1 ) 10 47 PSD ( Hz 10 49 10 51 10 53 10 5 10 4 10 3 10 2 10 1 Frequency ( Hz ) TDI Power Spectral Density (Noises) TDI Power Spectral Density (Random Noises) 10 38 10 38 10 40 10 40 10 42 1 ) 1 ) 10 42 PSD ( Hz PSD ( Hz 10 44 10 44 10 46 10 46 X T A A 10 48 10 48 E E 9 10 5 10 4 10 3 10 2 10 1 10 5 10 4 10 3 10 2 10 1 Frequency ( Hz ) Frequency ( Hz )

Overview of Radler Data TDIs: the line at high frequency is (partially) absorbed by the response function. TDI Power Spectral Density (NoNoise) X 10 43 A E 10 45 1 ) 10 47 PSD ( Hz 10 49 10 51 10 53 10 5 10 4 10 3 10 2 10 1 Frequency ( Hz ) TDI Power Spectral Density (Noises) TDI Power Spectral Density (Random Noises) 10 38 10 38 10 40 10 40 10 42 1 ) 1 ) 10 42 PSD ( Hz PSD ( Hz 10 44 10 44 10 46 10 46 X T A A 10 48 10 48 E E 9 10 5 10 4 10 3 10 2 10 1 10 5 10 4 10 3 10 2 10 1 Frequency ( Hz ) Frequency ( Hz )

Overview of Radler data By a quick and dirty check, fits agree with the input within 1 σ . Input Vs Data Vs Fit A m channel data vs analytic model 10 9 Data 10 38 Paramfile Fit 10 10 10 40 10 11 42 10 PSD (Hz -1 ) 44 10 10 12 10 46 10 13 Noise data 10 48 Model 10 14 10 5 10 4 10 3 10 2 10 1 10 5 10 4 10 3 10 2 10 1 Frequency (Hz) Frequency (Hz) 10

Build your own Data Fits over 2 weeks data = (0, 2/3, 2/3) 8 10 10 9 10 10 10 11 10 12 10 13 14 10 Params Fit 10 15 10 5 10 4 10 3 10 2 10 1 Frequency (Hz) 11

An “exercise” Energy density, = 2/3 6 10 Powerlaw sensitivity 10 8 10 10 10 12 10 14 10 16 10 18 10 4 10 3 10 2 10 1 Frequency (Hz) 12