Welcome to ler’s documentation!

ler

ler (/ˈɛlɚ/): Lensed (or un-lensed) gravitational waves Event (compact-binaries) Rate calculator and simulator.

ler is a statistical based python package whose core function is to calculate detectable rates of gravitational waves events. Description available in the Summary section.

The code is available at github.ler.

For reaching out to the developer, please raise issue in github.ler.issue.

ler main developer: Hemanta Ph.

ler developer and analyst: Harsh Narola, Anupreeta More, Ng Chung Yin (Leo), Justin Janquart, Chris Van Den Broeck, Otto Akseli HANNUKSELA

Contents:

• Installation
  • ler for development
  • Installation of numba with conda
• Summary
  • Sampling Gravitational Wave Source Properties:
  • Lensing Related:
  • Calculation of Detectable Merger Rates Per Year:
  • Distribution binary black hole (BBH) in terms of redshift.
• Gravitational wave event rates
  • Compact Binaries Merger Rate Density
  • Annual Rate of Detectable Gravitational Wave Events
  • Compact binaries mass distribution
  • Sampling Methods for Compact Binary Sources
  • Statistical Form of Detectable Merger Rate (Monte Carlo Integration)
• Gravitational wave events sampling (Analytical formulation)
  • Initial setup
  • Rate equation (statistical formulation)
  • The order of sampling and rate calculation steps in LeR are listed below.
• Lensed event rates
  • Lensed merger rate distribution of compact binary coalescences
  • Annual Rate of Detectable Lensed Gravitational Wave Events
  • SNR Operator for Lensed Gravitational Wave Events
  • Sampling Lens Properties
  • Sampling of Source Position
  • Calculation of Images properties
  • Statistical Form of Detectable Merger Rate of lensed events (Monte Carlo Integration)
• Lensed event sampling (Analytical formulation)
  • Initial setup
  • Optical depth
  • Rate equation (statistical formulation)
  • The order of sampling and rate calculation steps in LeR are listed below.

API:

• ler.rates
• ler.gw_source_population
• ler.lens_galaxy_population
• ler.image_properties
• ler.utils

Examples:

• LeR complete examples
  • Short lensed/unlensed BBH example with three detectors
  • Custom functions
  • Using internal model functions
  • Selecting particular number of detectable events
  • Using custom detection criteria
• GWRATES complete exmaples
  • Short BBH (Binary Black Hole) example with three detectors
  • Custom functions
  • Using internal model functions
  • Selecting particular number of detectable events
  • Utilizing the ANN feature of the gwsnr
  • BNS (Binary Neutron Star) example
  • Using custom detection criteria
• Calculation of detectable Gamma ray burst (GRB) event rate associated with Binary Neutron Star (BNS) mergers.
  • Equations (GRB)
  • Probability of detection of GRBs
  • Rate calculation of GRB with LeR
  • Rate calculation of GW with LeR
  • Final results
• LeR complete examples of BNS events (O4 design sensitivity)
  • Models Considered
  • Un-lensed events
  • Lensed events
  • Time delay distribution
  • Magnification distribution
• Event Rates with 3G detectors
  • With ANN snr (IMRphenomXPHM)
• Merger rate density evolution with redshift
  • Plotting differential comoving volume
  • Merger rate evolution with redshift (detector frame)
  • BBH
  • BNS
  • Redshift distribution (BBH)
• Sampling parameters for a compact binary
  • Introduction
  • Sampling of parameters
  • Available priors and their comparisions
  • BBHs mass distribution with different origins (popI/II vs popIII vs primordial black holes)
• Statistical study of changing Mass model paramter(s)
  • Binary Black Hole (BBH) Mass Model
  • Binary Neutron Star (BNS) Mass Model
  • Calculation of rates (per year)
  • Un-lensed
• Statistical study of cosmological friction and its effect on the redshift vs inferred chirp-mass relation of gravitational waves
  • Function to convert from luminosity distance to redshift
  • Functional form of change in the luminosity distance due to cosmological friction
  • BBH events (O4 sensitivity)
  • Inferred source chirp-mass vs redshift
  • BBH (3G sensitivities)
  • Rates of detectable events per year
• Lens parameter distributions
  • Model 1
  • Model 2
• Optical depth SIE model validation
  • Let’s look at the available functions and priors
• Validation of SIS model (Numerical vs Analytical)
• Study of Time delays and magnification of lensed gravitational waves.
  • data generation
  • 2-image case
  • KDE
  • corner plots
  • magnification plot
  • Contour plot
  • 4-image case

Indices and tables

• Index

• Module Index

• Search Page