Adding Rates from the Literature

New rate measurements that appear in papers can take a while (years) before they are picked up by one of the major libraries (ReacLib or StarLib).

If these rates are given in ReacLib fit format or as a tabulation (temperature, rate), then it is very easy to add these to pynucastro and use them in your networks.

Rates in ReacLib format

If a paper provides a rate in ReacLib format, it is straightforward to add the rate following the model in alternate_rates, but subclassing ReacLibRate. Then you just need to pass the reactants, products, and SingleSet objects into the constructor. An example is, for a hypothetical process \(A(B,\gamma)C\) would be:

class NewRate(ReacLibRate):

   def __init__(self):

       reactants = ["A", "B"]
       products = ["C"]

       labelprops = "newrate"

       sets = []
       sets.append(SingleSet([a0, a1, a2, a3, a4, a5, a6],
                             labelprops=labelprops))

       super().__init__(reactants=reactants,
                        products=products,
                        sets=sets,
                        label="newrate")

Where sets can contain as many SingleSet objects as needed to describe the rate.

Tabulated Rates

For rates that are given in terms of \((T, \lambda)\) pairs, we can subclass TemperatureTabularRate.

Important

The data must be given as natural logs, e.g., \((\ln(T_9), \ln(\lambda)\).

For example, for a hypothetical process \(A(B,\gamma)C\) we could do:

class NewRate(TemperatureTabularRate):

   def __init__(self):

       reactants = ["A", "B"]
       products = ["C"]

       labelprops = "newrate"

       T9 = np.array([1, 2, 3, 4])
       rate = np.array([1.e-20, 1.e-15, 1.e-10, 1.e-5])

       super().__init__(np.log(T9), np.log(rate),
                        reactants=reactants,
                        products=products,
                        label="newrate")

where the rate data is provided in the NumPy arrays T9 and rate.