Scattering and scintillation analysis scripts for the DSA-110

Scattering Pipeline

Burst‑Fit Pipeline

A lightweight, modular toolkit for fitting pulse-broadening and scintillation in Fast Radio Burst (FRB) dynamic spectra, and instrumental effects.

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Directory layout

scattering/
├── burstfittools.py              # core physics + MCMC wrappers
├── burstfit_casey_analysis.py  # example end‑to‑end script (CLI)
├── burstfit_modelselect.py     # sequential M0 → M1 → M2 → M3 model comparison
└── burstfit_robust.py          # sub‑band & per-channel leave‑one‑out diagnostics                  

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High‑level data flow

raw .npy       ┌──────────────────┐   best sampler & params
file  ───────▶ │ pre‑processing   │──┐
               │ (analysis script)│  │   ┌───────────────────┐     influence map
               └──────────────────┘  └──▶│  diagnostics      │──▶ (optional plots)
                         │   ds,f,t      │  (robust helper)  │
                         ▼               └───────────────────┘
               ┌──────────────────┐
               │  model scan      │  BIC table
               │  (modelselect)   │──▶ best model key
               └──────────────────┘

• pre‑processing — band‑pass correct, trim, down‑sample, normalise.
• model scan — runs MCMC for M0…M3, picks the winner by BIC.
• diagnostics — optional robustness checks before publication.

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One‑liner quick start

# fit a burst with 384×2 down‑sampling and run model selection
python burstfit_casey_analysis.py casey.npy \
       --downsample-f 384 --downsample-t 2 \
       --model-scan --plot

Flags of interest (see -h for full list):

Flag	Meaning
--model-scan	run burstfit_modelselect.fit_models_bic() internally
--plot	show data / model / residual heat‑maps
--save	pickle the sampler to disk for later inspection

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Module cheat‑sheet

Module	Responsibility	Public API
burstfit_v2.py	Physics kernel & sampler	FRBModel, FRBFitter, FRBParams, build_priors()
burstfit_casey_analysis.py	Command‑line “notebook replacement”	prep_dynamic(), CLI main
burstfit_modelselect.py	Sequential fits + BIC	fit_models_bic()
burstfit_robust.py	Robustness diagnostics	subband_consistency(), leave_one_out_influence()

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Diagnostics at a glance

• Sub‑band consistency — fit τ₁ GHz in N frequency chunks; large spread ⇒ per‑band systematics.
• Leave‑one‑out influence — χ²‑based heat‑map of how each channel pulls the global fit.
• (Optional) SBC — simulation‑based calibration helper planned for v2.1.

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Citing & license

Please cite Faber et al., in prep. (2025) if you use this code.

Scintillation Pipeline

scintillation/ │ ├── data/ │ └── burst_power.npz # Input data file │ ├── scint_analysis/ │ ├── configs/ │ │ ├── telescopes/ │ │ │ └── dsa.yaml # Telescope-specific config file │ │ └── bursts/ │ │ └── burst.yaml # Burst-specific config │ │ │ ├── scint_analysis/ # Pipeline source code │ │ ├── init.py │ │ ├── core.py │ │ ├── analysis.py │ │ ├── config.py │ │ ├── pipeline.py │ │ └── plotting.py │ │ │ └── run_analysis.py # The script you will execute │ └── cache/ # Cache to preserve intermediate data products

These scripts contain helper functions that facilitate the analysis of burst properties within the PARSEC dashboard (see dsa110-pol repository).