SMEFT + SM fits with low-energy observables - #146
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Pull request overview
Adds a new external likelihood for superallowed beta decays intended to enable simultaneous fits of SMEFT coefficients together with selected SM/nuisance parameters (e.g., (V_{ud})), using an analytic Jacobian from rgevolve plus optional Gaussian constraints.
Changes:
- Introduces
BetaDecayChi2RGevolveexternal (\chi^2) implementation usingrgevolveto compute an analytic SMEFT→WET linear response. - Adds standalone Gaussian-constraint external (\chi^2) terms for DRV, eta2, eta3, and (V_{ud}).
- Provides an example runcard wiring the beta-decay likelihood + constraints into a combined SM+SMEFT fit.
Reviewed changes
Copilot reviewed 3 out of 3 changed files in this pull request and generated 7 comments.
| File | Description |
|---|---|
| external_chi2/beta_decays/example_runcard_beta_decays_sm_smeft.yaml | Example configuration for running a combined SM+SMEFT fit including beta-decay external likelihood and Gaussian constraints. |
| external_chi2/beta_decays/beta_decays.py | Implements beta-decay external (\chi^2) using rgevolve to precompute an analytic Jacobian for a linearized SMEFT dependence. |
| external_chi2/beta_decays/beta_decays_gaussian_nuisance.py | Implements Gaussian prior penalty terms as separate external (\chi^2) components for nuisance/SM parameters. |
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| import jax.numpy as jnp | ||
| from smefit.rge import RGE | ||
| from rgevolve.tools.functions import run_and_match, get_wc_basis | ||
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| jax.config.update("jax_enable_x64", True) | ||
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| # Experimental Ft values and 1-sigma uncertainties from 2010.13797 (in units of 10^-3 s) | ||
| _EXP_MEAN = jnp.array( |
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| elif coeff.expr is not None and coeff.vars: | ||
| for var in coeff.vars: | ||
| if var not in self._free_smeft_names: | ||
| continue | ||
| local = {v: (1.0 if v == var else 0.0) for v in coeff.vars} | ||
| try: | ||
| factor = float(eval(coeff.expr, {"__builtins__": {}}, local)) | ||
| except Exception: | ||
| factor = 1.0 |
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| local = {v: (1.0 if v == var else 0.0) for v in coeff.vars} | ||
| try: | ||
| factor = float(eval(coeff.expr, {"__builtins__": {}}, local)) | ||
| except Exception: | ||
| factor = 1.0 | ||
| for k, v in contrib.items(): | ||
| self._eff_translation[var][k] = ( | ||
| self._eff_translation[var].get(k, 0.0) + factor * v | ||
| ) |
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| class BetaDecayChi2RGevolve: | ||
| """SMEFiT external chi2 for superallowed beta decays — rgevolve Jacobian. | ||
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| The Jacobian dL/dc_i is computed analytically at initialisation via | ||
| rgevolve.tools.functions.run_and_match, with no Wilson calls at any stage. | ||
| The chi2 is then a pure JAX linear function of the free parameters, identical | ||
| in form to BetaDecayChi2Linear but using rgevolve-derived derivatives. | ||
| """ |
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| def __init__(self, coefficients, rge_dict=None, central=None, sigma=None, **_): | ||
| free_names = list(coefficients.free_names) | ||
| self._idx = free_names.index(self._param_name) if self._param_name in free_names else None | ||
| self._central = jnp.float64(float(central if central is not None else self._default_central)) | ||
| self._sigma = jnp.float64(float(sigma if sigma is not None else self._default_sigma)) | ||
| self.num_data = 1 | ||
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| def compute_chi2(self, coefficient_values): | ||
| if self._idx is None: | ||
| return jnp.float64(0.0) | ||
| val = jnp.asarray(coefficient_values, dtype=jnp.float64)[self._idx] | ||
| return ((val - self._central) / self._sigma) ** 2 |
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PR for ongoing project with Juan and Kamil
Adding superallowed betadecays as an analytical external likelihood. It depends both on LEFT parameters as well as on SM ones (Vud).
Assuming that the other SMEFiT datasets do not depend on the SM, one can easily fit SMEFT+SM simultaneously.
It also contains an implementation of nuisance parameters relying a predefined gaussian constraints.