Micro_Interference4SLGW_identification

This repository is used to share the code and data for microlensing diffraction and SL identification

Step 0: Plot_result.py in Plot_code folder can plot all of the figures in this paper.

One can run this code to reproduce the result quickly.

Step 1: Run code in the SLGW_simulation folder to generate unlensed gravitational waves and only macro-lensed gravitational wave data.

1. Run GWDistribution.py: Generate the parameters for gravitational wave events.
2. Run Generate_GW_file_FD.py: Create the gravitational waveforms, save them to files, and then perform template matching to calculate the signal-to-noise ratio (SNR).
3. Run ReadAndEstimate.py: Perform parameter estimation on the saved gravitational wave files.
4. Run LensParameterGen.py: Generate the lens parameters. The sersic_profile.py file is called here to set the microlensing density based on the Sersic profile.
5. Run Generate_GW_file_Lensed_4_macro_4_SNR.py: Compute the SNR considering only macrolensing amplification, and save the waveform data into files.

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Step 2: Compute microlensing diffraction integrals in the Microlensing_Simulation folder.

1. Run Totalmicro.cpp: Calculate the microlensing diffraction integrals. (The microlensing data can be obtained from https://pan.bnu.edu.cn/l/X1QPKG.)

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Step 3: Return to the SLGW_simulation folder and generate the strongly lensed gravitational waveforms affected by microlensing.

1. Run Generate_GW_file_Lensed_4_micro.py: Generate the waveforms considering microlensing effects.
2. Run ReadAndEstimate_with_micro*.py: Perform parameter estimation for the microlensed images.

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Step 4: Run cWB-related code in the cWB_core folder and identify pairs.

1. Run Plot_SNR_VS_match: Generate the theoretical match for all lensing cases.
2. Run Gen_input_cat_file_unlens: Generate FRAMES data, input files, and DQ files for the unlensed cases, used as input for cWB.
3. Run Gen_input_cat_file_micro_Total: Generate FRAMES data, input files, and DQ files for all microlensed cases.
4. Run cal_match_unlens: Compute the match for unlensed cases.
5. Run cal_match_micro_Total and cal_match_micro_Total_final: Compute the match for microlensed cases.
6. Run OverlapAna.py: Analyze the Bayes factor for pairwise comparisons of unlensed cases.
7. Run Overlap_lens.py: Analyze the Bayes factor for pairwise comparisons of lensed cases.
8. Run Plot_Overlap_and_find_high_bayes_event.py: Plot the Bayes factors for unlensed and lensed cases, and identify events with high Bayes factors.

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Step 5: Identify the host galaxy in the Host_identification folder.

1. Run Lens_Galaxy_Sample.py: Generate galaxy-galaxy strong lensing systems based on the JWST star catalog.
2. Run Lens_light_population.pu: Calculate the effective radius (R_e) and magnitude of the lens galaxy using the fundamental plane.
3. Run Source_light_population_improve.py: Identify the magnitudes, effective radius (R_e), and Sersic index (R_sersic_n) of the host galaxies for the three selected SLGW events from the JWST star catalog, and save cases with star formation rates greater than 1.
4. Run Dec_Rec_Full_CSST/JWST_host_pop.py: Simulate the host galaxy-galaxy strong lensing system and reconstruct the lensing parameters.
5. Run TD_consistent_JWST_host_pop.py: Calculate the time delay and sample the four-image region based on the star formation rate.
6. Run Area_in_quadruple_region_host (unhost).py: Calculate the area of the quadruple region for all host (and unhost) galaxies.
7. Run Bayes_factor4TD.py: Calculate the Bayes factors for the time delay in unlensed and host lens galaxies.