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Theory & methods

San Diego State University

This section is the scientific heart of progenax: every model the package implements, derived from first principles, anchored on its canonical references, and accompanied by the implementation choices that made the model practical to differentiate through.

Every chapter follows the same template:

  1. Physical motivation — what observational phenomenon the model captures and why it matters.

  2. Mathematical formulation — closed-form derivations, ODEs, or sampling procedures, written so a non-specialist astronomer can follow the argument without needing to fall back on the source paper.

  3. Implementation in progenax — the class signature, the JAX patterns used to keep it differentiable, and any historical bugs that future readers should know about.

  4. Domain of validity and caveats — where the model fails, numerical-safety clips, and which alternative model to reach for when this one breaks.

  5. References{cite}-rendered links to the canonical paper(s), at least one pedagogical review, and any progenax-internal note the chapter draws on.

Suggested reading order

The map below is the index; this is the path. If you are learning the science from scratch, read the chapters in this order — each step builds on the conventions and models of the ones before it:

  1. What is an initial condition?start here. Defines what an “initial condition” is in progenax (the three orthogonal ingredients — density, velocity DF, masses), the virial-QQ convention, units, and the COM frame. Everything downstream assumes these conventions.

  2. Spatial profiles, in increasing generality: Plummer profile (the closed-form baseline) → King profile (tidal truncation via an ODE) → EFF profile (a free outer slope) → The differentiable lowered-model family (Engine A) (the unified Woolley/King/Wilson family) → Multimass equipartition saturation — the derived m_eq (multi-mass equilibrium). Plummer first because it is the analytic anchor the others are calibrated against; the family and multimass pages last because they generalize King.

  3. Velocity distribution functionshow each density is given velocities. A profile only becomes an equilibrium IC once paired with a matching velocity DF, so read this directly after the profiles it completes.

  4. Initial mass functionshow stars get masses. The third IC ingredient, independent of the spatial/velocity structure; from canonical IMFs to binary-aware and environment-dependent variants.

  5. Binary populationsmultiplicity. Builds on the IMF: orbital elements, period/eccentricity distributions, and resolving binaries into component kinematics.

  6. Tidal physics & substructuremodifiers and diagnostics. Tidal truncation, fractal/substructured ICs, and mass-segregation layers that act on top of a base profile, plus the diagnostics that measure them.

  7. Multi-component populationscomposite clusters. The capstone: multi-component populations sharing one self-consistent potential, which assembles everything above into a single equilibrium model.

For developers extending the package, pair each theory chapter with the architecture section — see Reading paths below.

Map of the section

Chapter family

Scientific scope

What is an initial condition?

What an “initial condition” is in progenax. Conventions, virial theorem, units, COM frame, half-mass radius. Read first if you are new.

Spatial density profiles

The spatial density profiles: Plummer profile, King profile, and EFF profile.

Velocity distribution functions

Velocity distribution functions paired with each spatial profile to produce dynamical equilibrium ICs, including the anisotropic Michie-King anisotropic model, and anisotropy and rotation extensions.

Initial mass functions

The initial mass function — from canonical Salpeter / Kroupa / Chabrier / Maschberger through binary-aware Moe & Di Stefano (2017) and environment-dependent Marks et al. (2012)Jeřábková et al. (2018) variants.

Binary populations

Kepler orbital elements, period and eccentricity distributions (Sana et al. (2012)Moe & Di Stefano (2017)), resolved binary kinematics.

Tidal physics & substructure

Tidal physics (Jacobi radius, differentiable truncation), the theory of fractal / substructured initial conditions, and Baumgardt et al. (2008) energy-ranked mass segregation.

Gravoturbulence

From the Federrath & Klessen (2012) density PDF through the Parmentier & Pasquali (2020) magnification factor and the Burkhart (2018)Burkhart & Mocz (2019) dense-gas SFR framework.

Multi-component populations

Multi-component clusters and other composite distributions.

What this section is not

Theory chapters describe what the package computes and why it is correct. They deliberately do not describe how to use the API — that lives in API reference. They also do not describe what the package chose not to do; that level of architectural rationale lives in Architecture & design. When a chapter needs to point at an implementation detail, it cross-links to the API page rather than duplicating signatures.

The boundary between theory and architecture is sometimes fuzzy. As a working rule: if the chapter content is invariant under a JAX-to- PyTorch port, it belongs in theory. If it depends on JAX-specific patterns (lax.scan, stop_gradient, smart dispatch), it belongs in architecture.

Reading paths

For students: read What is an initial condition? first, then any one chapter family that matches what you want to learn. Each family is self-contained.

For developers extending progenax: read the architecture section (Architecture & design) alongside the relevant theory chapter — theory tells you what physics to preserve, architecture tells you the patterns that preserve it under JAX.

For paper-writing: every theory chapter ends with a References block containing {cite} keys you can copy directly into your manuscript. The full bibliography (with DOIs / arXiv links) lives at Bibliography.

References
  1. Moe, M., & Di Stefano, R. (2017). Mind your Ps and Qs: The interrelation between period (P) and mass-ratio (Q) distributions of binary stars. The Astrophysical Journal Supplement Series, 230, 15. 10.3847/1538-4365/aa6fb6
  2. Marks, M., Kroupa, P., Dabringhausen, J., & Pawlowski, M. S. (2012). Evidence for top-heavy stellar initial mass functions with increasing density and decreasing metallicity. Monthly Notices of the Royal Astronomical Society, 422, 2246–2254. 10.1111/j.1365-2966.2012.20767.x
  3. Jeřábková, T., Kroupa, P., Dabringhausen, J., Hilker, M., & Bekki, K. (2018). Impact of metallicity and star formation rate on the time-dependent, galaxy-wide stellar initial mass function. Astronomy and Astrophysics, 620, A39. 10.1051/0004-6361/201833055
  4. Sana, H., de Mink, S. E., de Koter, A., Langer, N., Evans, C. J., Gieles, M., Gosset, E., Izzard, R. G., Le Bouquin, J.-B., & Schneider, F. R. N. (2012). Binary interaction dominates the evolution of massive stars. Science, 337, 444–446. 10.1126/science.1223344
  5. Baumgardt, H., De Marchi, G., & Kroupa, P. (2008). Evidence for primordial mass segregation in globular clusters. The Astrophysical Journal, 685, 247–253. 10.1086/590488
  6. Federrath, C., & Klessen, R. S. (2012). The star formation rate of turbulent magnetized clouds. The Astrophysical Journal, 761, 156. 10.1088/0004-637X/761/2/156
  7. Parmentier, G., & Pasquali, A. (2020). A new parameterization of the star formation rate–dense gas mass relation: Embracing gas density gradients. The Astrophysical Journal, 903, 56. 10.3847/1538-4357/abb8d3
  8. Burkhart, B. (2018). The Star Formation Rate in the Gravoturbulent Interstellar Medium. The Astrophysical Journal, 863, 118. 10.3847/1538-4357/aad002
  9. Burkhart, B., & Mocz, P. (2019). The self-gravitating gas fraction and the critical density for star formation. The Astrophysical Journal, 879, 129. 10.3847/1538-4357/ab25ed