Model fitting¶

Naima can derive the best-fit and uncertainty distributions of spectral model parameters through Markov Chain Monte Carlo (MCMC) sampling of their likelihood distributions. The following will only describe the implementation of Naima to do so, but a full explanation of MCMC or the sampling algorithm can be found in MacKay (2003), and Foreman-Mackey et al. (2013). It is also advisable to consult the documentation for the emcee package, which is used for the MCMC sampling.

If you use the MCMC fitting in your research, please cite the emcee package through the publication Foreman-Mackey, D., Hogg, D.W., Lang, D., & Goodman, J. 2013, PASP, 125, 306.

The measurements and uncertainties in the provided spectrum are assumed to be correct, Gaussian, and independent (note that this is unlikely to be the case, see Overcoming the Gaussian error assumption on how this might be tackled in the future). Under this assumption, the likelihood of observed data given the spectral model $$S(\vec{p};E)$$, for a parameter vector $$\vec{p}$$, is

$\mathcal{L} = \prod^N_{i=1} \frac{1}{\sqrt{2 \pi \sigma^2_i}} \exp\left(-\frac{(S(\vec{p};E_i) - F_i)^2}{2\sigma^2_i}\right),$

where $$(F_i,\sigma_i)$$ are the flux measurement and uncertainty at an energy $$E_i$$ over $$N$$ spectral measurements. Taking the logarithm,

$\ln\mathcal{L} = K - \sum^N_{i=1} \frac{(S(\vec{p};E_i) - F_i)^2}{2\sigma^2_i}.$

Given that the MCMC procedure will sample the areas of the distribution with maximum value of the objective function, it is useful to define the objective function as the log-likelihood disregarding constant factors:

$\ln\mathcal{L} \propto \sum^N_{i=1} \frac{(S(\vec{p};E_i) - F_i)^2}{\sigma^2_i}.$

The $$\ln\mathcal{L}$$ function in this assumption can be related to the $$\chi^2$$ parameter as $$\chi^2=-2\ln\mathcal{L}$$, so that maximization of the log-likelihood is equivalent to a minimization of $$\chi^2$$.

In addition to the likelihood from the observed spectral points, a prior likelihood factor should be considered for all parameters. This prior likelihood encodes our prior knowledge of the probability distribution of a given model parameter. If a given parameter is constrained by a previous measurement, it can be considered using a normal distribution (naima.normal_prior). If you need to constrain a parameter to be within a certain range, a uniform prior can be used (naima.uniform_prior). For parameters expected to have a flat prior in log-space (e.g., normalizations, cutoff energies, etc.) you can either sample the logarithm of the parameter or use a log-uniform prior (naima.log_uniform_prior).

The combination of the prior and data likelihood functions is passed onto the emcee.EnsembleSampler, and the MCMC run is started. emcee uses an affine-invariant MCMC sampler (Goodman & Weare 2010) that has the advantage of being able to sample complex parameter spaces without any tuning required. In addition, having multiple simultaneous walkers improves the efficiency of the sampling and reduces the number of computationally-expensive likelihood calls required.

The sampler works best by using as many samplers as possible, and starting them in a compact ball around the best fitting parameter values. After a burn-in period (there is no clear answer to many steps it takes for the sampler to stabilize), the samples that are accepted by the sampler are recorded, along with the model spectrum, and any metadata blobs provided by the model function (see Saving additional information — Metadata blobs). These can then be accessed through the sampler object, and Naima provides several convenience functions to analyse the results and compare them to the input spectrum: see Inspecting and analysing results of the run.

Overcoming the Gaussian error assumption¶

Naima provides an alternative to MCMC fitting by providing wrappers around the radiative models that can be used in sherpa. This package allows to take into account instrument response functions that include bin correlation. See Sherpa models for more details on these sherpa wrappers.

However, within the framework of MCMC fitting in Naima, several approaches will be considered for inclusion in the future to overcome the assumption of correct, Gaussian, independent errors.

• The first is a probabilistic approach to the uncertainty, by including generative models for the uncertainties in the NLL function. Such models could account for systematics and bin correlation. Their parameters would be fitted simultaneously with the model parameters, obtaining model parameter distributions that take them into account through marginalisation.

• An alternative approach to avoid bin correlation would be to call an external program that can do forward-folding comparison of models. However, doing this requires a full set of Instrument Response Functions that might not be available for all published data.

• A proper Poisson statistic could also be used is the fit was performed in counts space rather than in flux space. For this, the effective area and exposure in each bin would be required to convert between the model flux and the expected counts.

How to select which model best fits the data?¶

Model selection is the method through which a given model is selected from a set of possible models. You can find a good overview of the process and possible pitfalls in a blog post by Jake VanderPlas. In general, computing the Bayes factor for all competing models tends to be the best way to gauge which model provides a better fit. However, computing the Bayes Factor is often non-trivial, and a simpler way to obtain an estimate is using the Bayesian Information Criterion (BIC). The BIC for a Naima run can be found as a metadata keyword in the results table saved with naima.save_results_table. Note that the BIC is only a valid approximation for the Bayes factor when the number of datapoints is much larger than the number of parameters.