Targeted Detectability Range

Targeted Detectability Range#

This tutorial develops the Targeted Detectability Range (TDR) from first principles, then follows the method in Ronchini et al. and walks through the public gw_tdr pipeline.

The TDR answers a conditional question:

Given what an electromagnetic or other external messenger tells us about a possible compact-binary source, how far away could that source be and still be detectable by the gravitational-wave detector network?

It is a rapid, source-informed sensitivity estimate—not a replacement for a coherent gravitational-wave search, a search ranking statistic, a false-alarm rate, or a formal exclusion analysis.

Learning path#

  1. GW signals, detection, and the paper: compact binaries, waveforms, noise and PSDs, matched filtering, SNR, significance, template banks, and the paper’s definition and validation of TDR.

  2. From the repository to source-informed TDR: follow the pipeline, explore sky localization and inclination/mass dependence, compare source-prior choices, and complete coding exercises.

The first notebook develops the concepts and uses PyCBC for its antenna response example. The second notebook runs the production gw_tdr code for GRB 200228A (bn200228291) using the real Fermi/GBM HEALPix localization and public GWOSC O3b strain. The paper identifies this as the O3 GRB with the largest (D_{90}^{\rm TDR}) and uses it for the TDR-map example. It needs a Python 3.11+ kernel with the GW-analysis dependencies and the local gw_tdr repository installed editably in that same environment, plus internet access to the Fermi/HEASARC and GWOSC archives.

Paper and implementation#

  • Ronchini et al., “Gravitational-wave detectability range informed by external messengers,” Astronomy & Astrophysics (2026): arXiv:2605.21578. Figure 6 identifies GRB 200228291 as the sample’s highest-(D_{90}^{\rm TDR}) event; its localization is provided by Fermi/GBM GCN 27247.

  • Source code: samueleronchini/gw_tdr.

  • To run the production pipeline, follow the repository’s installation guide and install it in editable mode (python -m pip install -e /path/to/gw_tdr) using the notebook kernel’s environment. In VS Code, explicitly select the Python 3.11+ TDR environment and restart the kernel before running cells. ModuleNotFoundError: gwosc means the active environment lacks a dependency; ModuleNotFoundError: targ_ac_git means the local repository is not installed in that environment. The second notebook documents the full run, public-data access, generated output files, and the real functions behind each analysis step.

Scope of priors in the current public code#

The command-line pipeline accepts either a fixed sky position or a HEALPix sky-probability map. Its inclination option is an isotropic prior truncated to a chosen interval. It evaluates a small, fixed grid of BNS and NSBH masses. It does not currently accept an arbitrary source-posterior file or an arbitrary mass posterior. The notebook compares inclination intervals using the production range function, demonstrates its sky-map interface, and identifies the production functions that would need extending for correlated source-posterior samples.

For the isotropic truncated inclination prior, sample uniformly in (\cos\iota), not uniformly in (\iota). If external data constrain inclination, use the conditional posterior (or a physically justified truncation); do not silently treat a jet opening angle as an exact viewing angle.

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