Architecture

The codebase has two distinct pipelines: the EMRI Simulation Pipeline and the Bayesian Inference Pipeline.

EMRI Simulation Pipeline

main.py:data_simulation() drives a loop over simulation_steps:

1. Model1CrossCheck (cosmological model) samples EMRI events from a distribution.

2. GalaxyCatalogueHandler resolves each event to a host galaxy from the GLADE catalog.

3. ParameterSpace.randomize_parameters() + set_host_galaxy_parameters() set up the 14-parameter EMRI.

4. ParameterEstimation.compute_signal_to_noise_ratio() computes SNR using a LISA waveform.

5. If SNR ≥ threshold: compute_Cramer_Rao_bounds() computes the Fisher matrix and saves to CSV.

Bayesian Inference Pipeline

main.py:evaluate() → BayesianStatistics.evaluate() (in bayesian_inference/bayesian_statistics.py):

• Loads saved Cramér-Rao bounds from CSV into per-event Detection objects.

• Constructs a simulation-based SimulationDetectionProbability built from the real GLADE galaxy catalog.

• Builds multivariate-normal GW likelihoods from the full Fisher-matrix covariance and evaluates per-detection posteriors over an H₀ grid using a multiprocessing pool.

• Output is written to simulations/posteriors/ as JSON; per-event posteriors are combined via posterior_combination.py.

Key Data Flow

ParameterSpace (14 params)
    │
    ▼
WaveformGenerator (few / ResponseWrapper)
    │
    ▼
ParameterEstimation
    ├── compute_signal_to_noise_ratio()   →  SNR
    └── compute_Cramer_Rao_bounds()        →  Fisher matrix  →  CSV
                                                                   │
                                                  ┌────────────────┘
                                                  ▼
                                          Detection (from CSV)
                                                  │
                                                  ▼
                                          BayesianStatistics
                                                  │
                                                  ▼
                                          posterior p(H₀ | {dᵢ})

Module Responsibilities

Module	Responsibility
parameter_estimation/parameter_estimation.py	Waveform generation via few, Fisher matrix (5-point stencil), SNR and Cramér-Rao bounds. The scalar_product_of_functions inner product is the computational bottleneck (PSD loop).
LISA_configuration.py	LISA antenna patterns (F+, F×), PSD, SSB↔detector frame transformations.
datamodels/parameter_space.py	14-parameter EMRI space with randomization and bounds.
bayesian_inference/bayesian_statistics.py	BayesianStatistics class: orchestrates the full H₀ posterior evaluation (per-event MV-normal likelihood, simulation-based detection probability, multiprocessing pool, JSON output).
bayesian_inference/simulation_detection_probability.py	SimulationDetectionProbability: KDE-based selection function built from injection campaign results.
bayesian_inference/posterior_combination.py	Combines per-event posteriors into the population-level H₀ posterior.
datamodels/galaxy.py	Galaxy and GalaxyCatalog dataclasses; comoving volume, redshift/mass distributions.
datamodels/detection.py	Detection dataclass parsed from Cramér-Rao CSV output.
cosmological_model.py	Model1CrossCheck wraps the EMRI event rate model; BayesianStatistics orchestrates the H₀ evaluation.
galaxy_catalogue/handler.py	Interfaces with the GLADE galaxy catalog (BallTree-based lookups).
physical_relations.py	Canonical distance functions: dist(), dist_vectorized(), hubble_function(), redshifted_mass().
constants.py	All physical constants and simulation configuration. Key: H=0.73, SNR_THRESHOLD=20.

GPU / CPU Portability

All computation functions resolve the array module via the _get_xp pattern, so the same code runs on both NumPy (CPU) and CuPy (GPU) without branching. See Quickstart for how to install the GPU extras.