Lead

Joseph Rodriguez

Formation Channels Research

Project Overview

This project develops a multi-code, physics-informed framework to infer the astrophysical formation channels of compact-object mergers observed in GWTC-4, while explicitly quantifying and diagnosing theoretical uncertainty in population-synthesis modeling. Using ensembles generated from independent stellar-evolution codes (COMPAS and COSMIC, with POSYDON planned), the pipeline applies realistic selection effects and simulation-based inference to recover posterior distributions over population hyperparameters and formation-channel fractions. Unlike single-model studies, this work treats cross-code disagreement as a first-class scientific object, mapping where and why simulators diverge in parameter, metallicity, and observable space. A domain-adaptation layer aligns simulated and observed events, and built-in falsification criteria flag regimes where simulator systematics dominate over observational information, rendering channel inference unreliable. The result is not just a set of inferred channel fractions, but a structured assessment of where current binary-evolution theory succeeds, fails, and must be refined to support robust gravitational-wave population inference.

Scope of Work

Multi-code Population Synthesis

Generate and harmonize compact-binary populations across independent stellar-evolution codes (COMPAS and COSMIC; POSYDON planned) over shared hyperparameter grids, enabling controlled comparison of formation-channel predictions under differing physical prescriptions.

Multi-code Population Synthesis

Generate and harmonize compact-binary populations across independent stellar-evolution codes (COMPAS and COSMIC; POSYDON planned) over shared hyperparameter grids, enabling controlled comparison of formation-channel predictions under differing physical prescriptions.

Multi-code Population Synthesis

Generate and harmonize compact-binary populations across independent stellar-evolution codes (COMPAS and COSMIC; POSYDON planned) over shared hyperparameter grids, enabling controlled comparison of formation-channel predictions under differing physical prescriptions.

Realistic Observation Modeling

Apply cosmology, metallicity evolution, and gravitational-wave selection effects to produce detector-frame observables directly comparable to GWTC-4 posterior samples, ensuring inference is performed on catalog-level quantities rather than idealized populations.

Realistic Observation Modeling

Apply cosmology, metallicity evolution, and gravitational-wave selection effects to produce detector-frame observables directly comparable to GWTC-4 posterior samples, ensuring inference is performed on catalog-level quantities rather than idealized populations.

Realistic Observation Modeling

Apply cosmology, metallicity evolution, and gravitational-wave selection effects to produce detector-frame observables directly comparable to GWTC-4 posterior samples, ensuring inference is performed on catalog-level quantities rather than idealized populations.

Simulation-based Inference With Uncertainty Decomposition

Use neural density estimation to infer population hyperparameters and formation-channel fractions while explicitly separating aleatoric (measurement and sample-size) uncertainty from epistemic uncertainty arising from model disagreement.

Simulation-based Inference With Uncertainty Decomposition

Use neural density estimation to infer population hyperparameters and formation-channel fractions while explicitly separating aleatoric (measurement and sample-size) uncertainty from epistemic uncertainty arising from model disagreement.

Simulation-based Inference With Uncertainty Decomposition

Use neural density estimation to infer population hyperparameters and formation-channel fractions while explicitly separating aleatoric (measurement and sample-size) uncertainty from epistemic uncertainty arising from model disagreement.

Epistemic Diagnostics and Falsification

Quantify cross-code disagreement using mutual-information and code-identifiability diagnostics, and apply operational falsification criteria to identify regions of parameter and observable space where formation-channel inference is unreliable due to theoretical systematics.

Epistemic Diagnostics and Falsification

Quantify cross-code disagreement using mutual-information and code-identifiability diagnostics, and apply operational falsification criteria to identify regions of parameter and observable space where formation-channel inference is unreliable due to theoretical systematics.

Epistemic Diagnostics and Falsification

Quantify cross-code disagreement using mutual-information and code-identifiability diagnostics, and apply operational falsification criteria to identify regions of parameter and observable space where formation-channel inference is unreliable due to theoretical systematics.

Technical Details

Ensemble generation and standardization

Run COMPAS and COSMIC over matched sparse grids in key hyperparameters (e.g., common-envelope efficiency, natal kick dispersion, wind mass loss, metallicity), store outputs in a unified HDF5 schema, and tag each sample with simulator identity for downstream epistemic analysis.

Selection-function and observable construction

Convert source-frame populations to detector-frame observables by applying cosmology, metallicity-dependent formation rates, and catalog-level detectability criteria, producing simulated distributions in ( 𝑚 1 , 𝑚 2 , 𝜒 e f f , 𝑧 , 𝑝 d e t ) (m 1, m 2 , χ eff, z, p det).

Latent-space alignment and domain adaptation

Map simulated detections and GWTC-4 posterior samples into a shared latent space using learned encoders with distribution-matching losses (e.g., MMD and/or adversarial objectives) to reduce simulator–detector mismatch prior to inference.

Neural simulation-based inference and diagnostics

Train set-based neural density estimators (normalizing flows) on aligned populations to approximate 𝑝 ( 𝜃 ∣ G W T C - 4 ) p(θ∣GWTC-4), while computing mutual-information and code-identifiability metrics to quantify epistemic uncertainty and trigger falsification criteria.

Ongoing work: Full production ensemble runs on AWS (COMPAS), cross-code mutual-information studies, and event-level formation-channel inference on GWTC-4 are currently in progress, with POSYDON integration planned as a high-fidelity benchmark.