Chinook

Official Resources

• Homepage: https://chinookpy.readthedocs.io/
• Documentation: https://chinookpy.readthedocs.io/en/latest/
• Repository: https://github.com/rpday/chinook
• License: MIT License

Overview

Chinook is a Python package specifically designed for calculating Angle-Resolved Photoemission Spectroscopy (ARPES) matrix elements and simulating spectra from tight-binding models. Unlike standard tight-binding codes that only output band structures, Chinook incorporates orbital projection effects, experimental geometry (photon polarization, detector angles), and spin-orbit coupling to simulate the actual intensity intensity maps measured in experiments.

Scientific domain: ARPES, Surface Science, Tight-Binding Target user community: ARPES experimentalists and theorists analyzing band mapping data

Theoretical Methods

• Tight-Binding Construction: Support for arbitrary lattice bases (Slater-Koster or user-defined).
• ARPES Matrix Elements: Calculation of the transition probability $|\langle \psi_f | \mathbf{A} \cdot \mathbf{p} | \psi_i \rangle|^2$.
  • Dipole approximation.
  • Plane-wave final states.
• Spin-Orbit Coupling: Fully relativistic model Hamiltonians.
• Slab Calculation: Surface state projection for finite slabs.

Capabilities

• Simulations:
  • ARPES Intensity Maps $I(E, k_x, k_y)$.
  • Constant Energy Contours (Fermi surfaces with matrix element weighting).
  • Spin-ARPES spectra.
• Experimental Factors:
  • Linear/Circular polarization of incident light.
  • Geometry of the scattering plane.
  • Photon energy dependence ($k_z$ selection).
• Analysis:
  • Projected Density of States (pDOS).
  • Spin-texture plotting.

Key Strengths

• Experiment-Theory Link: Directly simulates what the machine measures, handling the crucial "matrix element effects" where bands disappear or change intensity based on symmetry and polarization.
• Python-Native: Fully integrated with NumPy/Matplotlib for seamless data analysis pipelines.
• Slab Generation: Easy tools to create slabs and study surface states and their decay.

Inputs & Outputs

• Inputs:
  • Python scripts defining the orbital basis (quantum numbers $n, l, m$) and lattice.
  • Experimental parameters (Photon energy $h\nu$, vector potential $\mathbf{A}$).
• Outputs:
  • NumPy arrays of Intensity vs $(E, k)$.
  • Matplotlib figures.

Interfaces & Ecosystem

• H_library: Built-in library of standard Hamiltonians (e.g., Kane-Mele, Rashba).
• Python: Can be used alongside Pybinding or other tools if Hamiltonians are manually converted.

Performance Characteristics

• Speed: Efficient for model Hamiltonians; $O(N_{bands})$ for matrix elements.
• Scaling: Python-based, suitable for effective models (dozens of orbitals) rather than large-scale DFT inputs.

Comparison with Other Codes

• vs. Pybinding: Pybinding is faster for pure band structure/transport (KPM) of huge systems. Chinook is specialized for ARPES intensities and matrix elements, which Pybinding does not calculate.
• vs. Chinook (IDL): This is the modern Python successor to earlier IDL-based ARPES tools.

Application Areas

• Topological Materials: Identifying surface states and their spin texture in ARPES.
• Quantum Materials: Disentangling orbital character in multi-band superconductors (e.g., FeSe).
• Dichroism: Calculating circular dichroism in angular distributions (CD-ARPES).

Community and Support

• Development: UBC / Damascelli Group (Ryan Day).
• Source: GitHub.

Verification & Sources

• Website: https://chinookpy.readthedocs.io/
• Primary Publication: R. P. Day et al., npj Quant Mater 4, 62 (2019).
• Verification status: ✅ VERIFIED
  • Active tool in the ARPES community.