Analysis workflows

These chapters walk through end-to-end μSR data analyses and demonstrate the Asymmetry GUI and Python API in lock-step. Almost all are reproducible walkthroughs on real instrument runs from the WiMDA muon-school corpus — several on the very datasets behind published papers — each with the model, seeds, and expected numbers; the Knight-shift chapter (and a few illustrative subsections) use synthetic versions of canonical experiments grounded in textbook archetypes (Blundell et al. 2022; Amato & Morenzoni 2024).

The narratives are the recommended onboarding path for users who have collected real data and want to see how to analyse it. They cross-reference the reference pages elsewhere in this user guide for full feature documentation.

New users should start with EMU detector grouping and α calibration.

• EMU detector grouping and α calibration
  • The runs
  • Step 1 — Load a run
  • Step 2 — Open the Grouping window
  • Step 3 — Calibrate α from the TF run
  • Step 4 — The visual Detector Layout Editor
  • Step 5 — Dead-time correction
  • Step 6 — Timing: t0 and the good-data window
  • Step 7 — A fit table with a manual column
  • Reusing the calibration
  • Where to go next
• Muon diffusion in copper: Kubo–Toyabe, Abragam, and QLCR
  • The experiment and the corpus data
  • The static Kubo–Toyabe signature
  • Low-temperature quantum diffusion
  • The dynamic Kubo–Toyabe fit and the hop rate
  • The hop-rate curve and the mobility minimum
  • Cross-check: the transverse-field Abragam line shape
  • Quadrupolar level-crossing resonance (QLCR)
  • Assumptions and limitations
  • References
  • Cross-references
• Photo-μSR in silicon: carrier recombination from period-mode runs
  • The data
  • Period-mode data handling: red and green
  • The scriptable API
  • Light on versus light off
  • Extracting λ: the light-ON fit
  • Calibrating λ against Δn
  • The headline: carrier lifetime τ₀
  • Assumptions and limitations
  • See also
• ALC field scan in TCNQ
  • The runs
  • Building the integral scan
  • Fitting the resonance
  • From resonance field to hyperfine coupling
  • The temperature series: motional narrowing
  • Scripting the scan
  • Advanced: a four-resonance µLCR spectrum
  • Assumptions and limitations
  • References
  • Cross-references
• Temperature scan through a magnetic transition
  • Physical motivation
  • The data
  • Step 1 — Load and inspect
  • Step 2 — Group consistently across the scan
  • Step 3 — Fit an ordered run
  • Step 4 — Trend the order parameter
  • Step 5 — Fit the order parameter to a power law
  • Interpretation
  • Variants
  • Common pitfalls
  • References
  • Cross-references
• Global fitting across fields: Li⁺ ionic motion in a garnet electrolyte
  • Why a global fit is unavoidable here
  • The runs
  • Step 1 — Calibrate α from the TF run
  • Step 2 — Read the raw field triplet
  • Step 3 — The model and the parameter classification
  • Step 4 — Run the joint fit
  • Step 5 — Trend ν(T) and extract the activation energy
  • Assumptions and limitations
  • References
  • Cross-references
• F–µ–F entangled states: measuring a bond length with muons
  • Why PTFE hosts an entangled three-spin state
  • The runs
  • Step 1 — Calibrate α on the transverse-field run
  • Step 2 — Read the raw zero-field signature
  • Step 3 — Fit the F–µ–F polarisation and read off the bond length
  • Step 4 — The frequency-domain view
  • Assumptions and limitations
  • References
  • Cross-references
• Angle-dependent Knight shift
  • Physical motivation
  • The data
  • Walkthrough
  • Reading the result
  • See also
  • References
• Superconductor penetration depth from σ(T)
  • Physical motivation
  • The data
  • Step 1 — Fit a TF run for σ
  • Step 2 — Inspect the vortex line shape
  • Step 3 — Trend σ(T) through T_c
  • Step 4 — Compare the two fields
  • Fitting a gap model
  • Cross-reference — LiFeAs, the field width B_rms(T)
  • Cross-reference — Re₆Zr, superfluid density and a TRSB step
  • Interpretation
  • Assumptions and limitations
  • References
  • Cross-references
• LF decoupling and static-vs-dynamic field distributions
  • Physical motivation
  • The data
  • Step 1 — Load and overlay the decoupling series
  • Step 2 — Fit each run with a single exponential
  • Step 3 — Build the λ(B) trend
  • Step 4 — The Redfield linearisation (headline)
  • Contrast: the static counter-example
  • The static LF-KT decoupling series
  • Interpretation
  • Common pitfalls
  • Further reading
  • Cross-references

Quick chooser

Workflow	Typical experiment	Key Asymmetry features
EMU detector grouping and α calibration	Set up EMU detector grouping and calibrate α from a TF run (onboarding)	Grouping window, Estimate α, visual Detector Layout Editor, dead-time
Muon diffusion in copper: Kubo–Toyabe, Abragam, and QLCR	Measure the muon hop rate ν in copper from a ZF dynamic-KT fit	DynamicGaussianKT, fixed Δ / floated ν, motional narrowing
Photo-μSR in silicon: carrier recombination from period-mode runs	Compare light-ON vs light-OFF muonium dynamics in a photo-μSR run	Red/green RG box, select_period / load(period=…) API
ALC field scan in TCNQ	Locate an ALC resonance from a field-stepped scan	ALC mode, Build Scan, baseline + peak fit, integral asymmetry
Temperature scan through a magnetic transition	Locate Tc and measure the order parameter of a ferromagnet or antiferromagnet (real EuO / Ni / molecular-magnet data)	Logbook sort, Fit Wizard, parameter trending, power-law fit
Global fitting across fields: Li⁺ ionic motion in a garnet electrolyte	Extract an ion-hopping activation energy from LF triplets in a solid electrolyte (real Li₇La₃Zr₂O₁₂ data)	Global (batch) fit with shared parameters, Keren model, axis transforms, Arrhenius trend
F–µ–F entangled states: measuring a bond length with muons	Identify an F–µ–F entangled state and measure the muon–fluorine bond length (real PTFE data)	FmuF models, ZF beating signature, sub-MHz FFT
Angle-dependent Knight shift	Find the muon site from the angle dependence of the Knight shift in a rotated single crystal	Knight shift analysis window, Angle axis + Fold, Joint K(θ) fit, KnightAnisotropy, Suggest next angle
Superconductor penetration depth from σ(T)	Extract λ(T) from TF μSR in the vortex state of a superconductor (real BiSCCO / LiFeAs / Re₆Zr data)	Multi-run TF fits, parameter trending, multi-series overlay, MaxEnt vortex lineshape, SC gap models
LF decoupling and static-vs-dynamic field distributions	Distinguish static from dynamic local fields with an LF scan (real Ca₃Co₂O₆ data)	LF exponential fits, axis transforms (1/λ vs B² Redfield), static-KT decoupling diagnostic

Further workflows

The internal porting workspace (docs/porting/practical-workflows/workflow-catalogue.md) catalogues additional workflows that Asymmetry partially or fully supports — F–μ–F identification, spin-glass freezing, muonium-radical hyperfine, and others. ALC resonance scans have a worked example above (ALC field scan in TCNQ) and a full feature reference in Integral scan mode (avoided-level-crossing field scans); the angle-dependent Knight shift has a worked example above (Angle-dependent Knight shift) and a feature reference at Knight shift. Workflows that Asymmetry does not yet support (e.g. multi-period arithmetic, LEM depth profiling) are catalogued there too and surface as candidates in docs/porting/ROADMAP.md.