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RateLab_Project

This repo is a learning-by-coding implementation of a small thermonuclear reaction network (an α-capture chain) using REAClib rates and a simple one‑zone (parameterized) thermodynamic trajectory. The goal is to connect the physics definitions and equations (Iliadis) to the actual numbers your code evolves.

Primary reference: Christian Iliadis, Nuclear Physics of Stars (2nd ed., 2015).

1) Core physical quantities (what we evolve)

Number density, mass density

  • Number density of species i:
    $$N_i \equiv \text{nuclei of species } i \text{ per unit volume}\quad [\mathrm{cm^{-3}}]$$
  • Mass density: $$\rho = \frac{1}{N_A}\sum_i N_i M_i$$ where $M_i$ is the relative atomic mass (in u) and $N_A$ is Avogadro’s constant.

Mass fraction $X_i$ and mole fraction / molar abundance $Y_i$

Iliadis defines: $$X_i \equiv \frac{N_i M_i}{\rho N_A}$$ and $$Y_i \equiv \frac{X_i}{M_i} = \frac{N_i}{\rho N_A}$$

  • $X_i$: fraction of mass in species (i) (dimensionless)
  • $Y_i$: mole fraction / molar abundance (often treated as “mol per gram” in practice); it stays constant under pure expansion/compression if no reactions occur (useful numerically).

Useful identity (from $Y_i = N_i/(\rho N_A)$: $N_i = \rho N_A Y_i$ This is what turns per‑volume rates into *ODEs for *Y.

2) Thermonuclear reaction rates $N_A\langle\sigma v\rangle$

From “pairs collide” to $\langle\sigma v\rangle$

For a two‑body reaction (0+1 \to ...), Iliadis starts with:

$$r_{01} = N_0 N_1 \int_0^\infty v,P(v),\sigma(v),dv \equiv N_0 N_1 <\sigma v>_{01}$$

(where $r_{01}$ is reactions per unit volume per unit time).

For identical reactants, the number of distinct pairs is reduced by 1/2; Iliadis writes the general expression using a Kronecker‑delta factor.
(In our α‑captures, reactants are different, so this factor is 1.)

Maxwell–Boltzmann distribution $P(v)$ and energy form

At thermodynamic equilibrium (non‑degenerate, non‑relativistic), relative velocities are Maxwellian: $P(v),dv = \left(\frac{m_{01}}{2\pi kT}\right)^{3/2} e^{-m_{01}v^2/(2kT)} 4\pi v^2,dv$ and can be rewritten as an energy distribution $P(E),dE$.

With that, Iliadis obtains the standard Maxwellian average:

$$&lt;\sigma v&gt;_{01} = \left(\frac{8}{\pi m_{01}}\right)^{1/2}\frac{1}{(kT)^{3/2}}\int_0^\infty E,\sigma(E),e^{-E/kT},dE$$

Why everyone tabulates $N_A\langle\sigma v\rangle$ and what $T_9$ is

In practice, the literature typically tabulates:

  • $N_A\langle\sigma v\rangle$ in $[\mathrm{cm^3,mol^{-1},s^{-1}}]$

Iliadis also gives a convenient numerical form:

$$N_A\langle\sigma v\rangle= 3.7318\times 10^{10},\frac{1}{T_9^{3/2}}\sqrt{\frac{M_0+M_1}{M_0 M_1}}\int_0^\infty E\sigma(E),e^{-11.605,E/T_9},dE$$

(with $E$ in MeV, $T_9 \equiv T/10^9\ \mathrm{K}$, $\sigma$ in barns).

And explicitly:

$$T_9 \equiv \frac{T}{10^9\ \mathrm{K}}$$

Interpretation: $$N_A\langle\sigma v\rangle(T_9)$$ is the thermally averaged microscopic probability of reaction, folded with the Maxwellian velocities.

3) Turning rates into abundance ODEs

Using $N_i=\rho N_A Y_i$, a two‑body capture $i+j\to k$ has (schematically):

$$\frac{dY_i}{dt}\sim -\rho,Y_iY_j,N_A &lt;\sigma v&gt;_{ij\to k},\qquad\frac{dY_k}{dt}\sim +\rho,Y_iY_j,N_A &lt;\sigma v&gt;_{ij\to k}.$$

This is exactly the structure implemented in ratelab/network.py for each α‑capture step.

A photodisintegration (reverse) reaction acts like a decay with rate (decay constant) $\lambda_\gamma$:

$$\frac{dY_{\text{parent}}}{dt} = -\lambda_\gamma,Y_{\text{parent}}$$

(see Iliadis’ general decay‑constant definition).

4) Forward ↔ reverse (photodisintegration) and detailed balance

For capture ↔ photodisintegration pairs, Iliadis shows that the reverse photodisintegration decay constant can be computed from the forward stellar rate using spin factors, partition functions, masses, and an exponential Boltzmann factor in $Q/T_9$. Example 3.2 explicitly evaluates: $\lambda_\gamma^*(\text{reverse})\propto T_9^{3/2},e^{-11.605,Q/T_9},N_A\langle\sigma v\rangle^*_{\text{forward}}$ (with additional multiplicative factors from spins and partition functions).

How we do it in this repo (toy approach)

For learning and rapid prototyping, we allow two options:

  1. Use REAClib-provided reverse fits: if the library already provides the reverse reaction as its own fit, we can evaluate $\lambda_\gamma(T_9)$ directly with the same 7‑parameter form (this is what your current network code structure assumes).

  2. Derive $\lambda_\gamma$ from the forward rate (more “physics transparent”): implement the detailed‑balance formula (as in Iliadis Example 3.2) using $Q$-values, spins, and partition functions.

Option (1) is fast and consistent with how large networks are often wired; option (2) is excellent for mastery because you see every physical factor.

5) REAClib 7‑parameter fit (what our code evaluates)

REAClib rates are provided as fits to tabulated $N_A\langle\sigma v\rangle(T_9)$, designed for very fast evaluation. For one coefficient set $a_1,\dots,a_7$,

$$N_A\langle\sigma v\rangle(T_9)= \exp!\Big(a_1 + a_2T_9^{-1} + a_3T_9^{-1/3} + a_4T_9^{1/3} + a_5T_9 + a_6T_9^{5/3} + a_7\ln T_9\Big)$$

(as implemented in ratelab/reaclib.py).

Many reactions have multiple sets (different temperature regions / components). We sum them: $\text{rate}(T_9) = \sum_k \exp(E_k(T_9))$ (as implemented in ratelab/rates.py).

Temperature sensitivity

We also compute: $$\frac{d\ln(\text{rate})}{d\ln T_9}$$ both analytically (chain rule) and by finite‑difference for sanity checks.

6) The network we evolve

We start with a minimal α‑chain relevant to O/Si‑region processing:

  • $$^{16}\mathrm{O}(\alpha,\gamma)^{20}\mathrm{Ne}$$
  • $$^{20}\mathrm{Ne}(\alpha,\gamma)^{24}\mathrm{Mg}$$
  • $$^{24}\mathrm{Mg}(\alpha,\gamma)^{28}\mathrm{Si}$$
  • $$^{28}\mathrm{Si}(\alpha,\gamma)^{32}\mathrm{S}$$

Forward ODE structure is in ratelab/network.py.

7) One‑zone “shock / explosive burning” trajectory

A common approach in explosive nucleosynthesis is to prescribe $T(t)$ and $\rho(t)$ with a simple parameterization once $T_{\text{peak}}$ and $\rho_{\text{peak}}$ are chosen. Iliadis discusses this idea and gives an exponential density falloff with an expansion timescale $\tau_{\mathrm{exp}}$, e.g.: $$\rho(t) = \rho_{\mathrm{peak}},e^{-t/\tau_{\mathrm{exp}}}$$

In this repo we use a simplified “shock_trajectory” parameterization in code (see ratelab/trajectory.py), and wire it into the integrator in scripts/run_onezone.py.

8) Fluxes (what is “driving” the network?)

A helpful diagnostic is the abundance flux for each reaction, i.e. the instantaneous flow of material through a link.

For an α‑capture $\alpha + i \to k$:

$$F_{\alpha i\to k}(t) = \rho(t),Y_\alpha(t),Y_i(t),N_A <\sigma v>_{\alpha i\to k}(T_9(t))$$

For a photodisintegration $k\to \alpha + i$:

$$F_{k\to \alpha i}(t) = \lambda_\gamma(T_9(t)),Y_k(t)$$

(Your fluxes() helper in network.py should return these as a dictionary so run_onezone.py can plot them.)

9) Running the demo

Plot rate curves + sensitivities

python scripts/plot_rates.py

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