Extragalactic

I’m stranded at sea at the moment so I may as well make a short post. First, here is the complete Stan code of the current model that I’m running. I haven’t gotten around to version controlling this yet or uploading a copy to my github account, so this is the only non-local copy for the moment.

One thing I mentioned in passing way back in this post is that making the stellar contribution parameter vector a simplex seemed to improve sampler performance both in terms of execution time and convergence diagnostics. This was for a toy model where the inputs were nonnegative and summed to one, but it turns out to work better with real data as well. At some point I may want to resurrect old code to document this more carefully, but the main thing I’ve noticed is that post-warmup execution time per sample is often dramatically faster than previously (the execution time for the same number of warmup iterations is about the same or larger though). This appears to be largely due to needing fewer “leapfrog” steps per iteration. It’s fairly common for a model run to have a few “divergent transitions,” which Stan’s developers seem to think is a strong indicator of model failure¹I’ve rarely actually seen anything particularly odd about the locations of the divergences and inferences I care about seem unaffected. I’m seeing fewer of these and a larger percentage of model runs with no divergences at all, which indicates the geometry in the unconstrained space may be more favorable.

Here’s the Stan source:

// calzetti attenuation
// precomputed emission lines

functions {
  vector calzetti_mod(vector lambda, real tauv, real delta) {
    int nl = rows(lambda);
    real lt;
    vector[nl] fk;
    for (i in 1:nl) {
      lt = 5500./lambda[i];
      fk[i] = -0.10177 + 0.549882*lt + 1.393039*pow(lt, 2) - 1.098615*pow(lt, 3)
            +0.260618*pow(lt, 4);
      fk[i] *= pow(lt, delta);
    }

    return exp(-tauv * fk);
  }

}
data {
    int<lower=1> nt;  //number of stellar ages
    int<lower=1> nz;  //number of metallicity bins
    int<lower=1> nl;  //number of data points
    int<lower=1> n_em; //number of emission lines
    vector[nl] lambda;
    vector[nl] gflux;
    vector[nl] g_std;
    real norm_g;
    vector[nt*nz] norm_st;
    real norm_em;
    matrix[nl, nt*nz] sp_st; //the stellar library
    vector[nt*nz] dT;        // width of age bins
    matrix[nl, n_em] sp_em; //emission line profiles
}
parameters {
    real a;
    simplex[nt*nz] b_st_s;
    vector<lower=0>[n_em] b_em;
    real<lower=0> tauv;
    real delta;
}
model {
    b_em ~ normal(0, 100.);
    a ~ normal(1, 10.);
    tauv ~ normal(0, 1.);
    delta ~ normal(0., 0.1);
    gflux ~ normal(a * (sp_st*b_st_s) .* calzetti_mod(lambda, tauv, delta) 
                    + sp_em*b_em, g_std);
}

// put in for posterior predictive checking and log_lik

generated quantities {
    vector[nt*nz] b_st;
    vector[nl] gflux_rep;
    vector[nl] mu_st;
    vector[nl] mu_g;
    vector[nl] log_lik;
    real ll;
    
    b_st = a * b_st_s;
    
    mu_st = (sp_st*b_st) .* calzetti_mod(lambda, tauv, delta);
    mu_g = mu_st + sp_em*b_em;
    
    for (i in 1:nl) {
        gflux_rep[i] = norm_g*normal_rng(mu_g[i] , g_std[i]);
        log_lik[i] = normal_lpdf(gflux[i] | mu_g[i], g_std[i]);
        mu_g[i] = norm_g * mu_g[i];
    }
    ll = sum(log_lik);
}

The functions block at the top of the code defines the modified Calzetti attenuation relation that I’ve been discussing in the last several posts. This adds a single parameter delta to the model that acts as a sort of slope difference from the standard curve. As I explained in the last couple posts I give it a tighter prior in the model block than seems physically justified.

There are two sets of parameters for the stellar contributions declared in lines 36-37 of the parameters block. Besides the simplex declaration for the individual SSP contributions there is an overall multiplicative scale parameter a. The way I (re)scale the data, which I’ll get to momentarily, this should be approximately unit valued. This is expressed in the prior in line 44 of the model block. Even though negative values of a would be unphysical I don’t explicitly constrain it since the data should guarantee that almost all posterior probability mass is on the positive side of the real line.

The current version of the code doesn’t have an explicit prior for the simplex valued SSP model contributions which means they have an implicit prior of a maximally dispersed Dirichlet distribution. This isn’t necessarily what I would choose based on domain knowledge and I plan to investigate further, but as I discussed in that post I linked it’s really hard to influence the modeled star formation history much through the choice of prior.

In the MILES SSP models one solar mass is distributed over a given IMF and then allowed to evolve according to one of a choice of stellar isochrones. According to the MILES website the flux units of the model spectra have the peculiar scaling

\((L_\lambda/L_\odot) M_\odot^{-1} \unicode{x00c5}^{-1}\)

This convention, which is the same as and dates back to at least BC03, is convenient for stellar mass computations since the model coefficient values are proportional to the number of initially solar mass units of each SSP. Calculating quantities like total stellar masses or star formation rates is then a relatively trivial operation. Forward modeling (evolutionary synthesis) is also convenient since there’s a straightforward relationship between star formation histories and predictions for spectra. It may be less convenient for spectrum fitting however, especially using MCMC based methods. The plot below shows a small subset of the SSP library that I’m currently using. Not too surprisingly there’s a multiple order of magnitude range of absolute flux levels with the main source of variation being age. What might be less evident in this plot is that in the units SDSS uses coefficient values might be in the 10’s or hundreds of thousands, with multiple order of magnitude variations among SSP contributors. A simple rescaling of the library spectra to make at least some of the coefficients more nearly unit scaled will address the first problem, but won’t affect the multiple order of magnitude variations among coefficients.

One alternative that’s close to standard statistical practice is to rescale the individual inputs to have approximately the same scale and central value. A popular choice for SED fitting is to normalize to 1 at some fiducial wavelength, often (for UV-optical-NIR data) around V. For my latest model runs I tried renormalizing each SSP spectrum to have a mean value of 1 in a ±150Å window around 5500Å, which happens to be relatively lacking in strong absorption lines at all ages and metallicities.

This normalization makes the SSP coefficients estimates of light fractions at V, which is intuitively appealing since we are after all fitting light. The effect on sampler performance is hard to predict, but as I said at the top of the post a combination of light weighted fitting with the stellar contribution vector declared as a simplex seems to have favorable performance both in terms of execution time and convergence diagnostics. That’s based on a very small set of model runs and no rigorous performance comparisons at all²among other issues I’ve run versions of these models on at least 4 separate PCs with different generations of Intel processors and both Windows and Linux OSs. This alone produces factor of 2 or more variations in execution time..

For a quick visual comparison here are coefficient values for a single model run on a spectrum from somewhere in the disk of the spiral galaxy that’s been the subject of the last few posts. These are “interval” plots of the marginal posterior distributions of the SSP model contributions, arranged by metallicity bin (of which there are 4) from lowest to highest and within each bin by age from lowest to highest. The left hand strip of plots are the actual coefficient values representing light fractions at V, while the right hand strip are values proportional to the mass contributions. The latter is what gets transformed into star formation histories and other related quantities. The largest fraction of the light is coming from the youngest populations, which is not untypical for a star forming region. Most of the mass is in older populations, which again is typical.

Another thing that’s very typical is that contributions come from all age and metallicity bins, with similar but not quite identical age trends within each metallicity bin. I find it a real stretch to believe this tells us anything about the real chemical evolution distribution trend with lookback time, but how to interpret this still needs investigation.

Here’s a problem that isn’t a huge surprise but that I hadn’t quite anticipated. I initially chose, without a lot of thought, a prior \(\delta \sim \mathcal{N}(0, 0.5)\) for the slope parameter delta in the modified Calzetti relation from the last post. This seemed reasonable given Salim et al.’s result showing that most galaxies have slopes \(-0.1 \lesssim \delta \lesssim +1\) (my wavelength parameterization reverses the sign of \(\delta\) ). At the end of the last post I made the obvious comment that if the modeled optical depth is near 0 the data can’t constrain the shape of the attenuation curve and in that case the best that can be hoped for is the model to return the prior for delta. Unfortunately the actual model behavior was more complicated than that for a spectrum that had a posterior mean tauv near 0:

Although there weren’t any indications of convergence issues in this run experts in the Stan community often warn that “banana” shaped posteriors like the joint distribution of tauv and delta above are difficult for the HMC algorithm in Stan to explore. I also suspect the distribution is multi-modal and at least one mode was missed, since the fit to the flux data as indicated by the summed log-likelihood is slightly worse than the unmodified Calzetti model.

A value of \(\delta\) this small actually reverses the slope of the attenuation curve, making it larger in the red than in the blue. It also resulted in a stellar mass estimate about 0.17 dex larger than the original model, which is well outside the statistical uncertainty:

I next tried a tighter prior on delta of 0.1 for the scale parameter, with the following result:

Now this is what I hoped to see. The marginal posterior of delta almost exactly returns its prior, properly reflecting the inability of the data to say anything about it. The posterior of tauv looks almost identical to the original model with a very slightly longer tail:

So this solves one problem but now I must worry that the prior is too tight in general since Salim’s results predict a considerably larger spread of slopes. As a first tentative test I ran another spectrum from the same spiral galaxy (this is mangaid 1-382712) that had moderately large attenuation in the original model (1.08 ± 0.04) with both “tight” and “loose” priors on delta with the following results:

The distributions of tauv look nearly the same, while delta shrinks very slightly towards 0 with a tight prior, but with almost the same variance. Some shrinkage towards Calzetti’s original curve might be OK. Anyway, on to a larger sample.

I finally got around to reading a paper by Salim, Boquien, and Lee (2018), who proposed a simple modification to Calzetti‘s well known starburst attenuation relation that they claim accounts for most of the diversity of curves in both star forming and quiescent galaxies. For my purposes their equation 3, which summarizes the relation, can be simplified in two ways. First, for mostly historical reasons optical astronomers typically quantify the effect of dust with a color excess, usually E(B-V). If the absolute attenuation is needed, which is certainly the case in SFH modeling, the ratio of absolute to “selective” attenuation, usually written as \(\mathrm{R_V = A_V/E(B-V)}\) is needed. Parameterizing attenuation by color excess adds an unnecessary complication for my purposes. Salim et al. use a family of curves that differ only in a “slope” parameter \(\delta\) in their notation. Changing \(\delta\) changes \(\mathrm{R_V}\) according to their equation 4. But I have always parametrized dust attenuation by optical depth at V, \(\tau_V\) so that the relation between intrinsic and observed flux is

\(F_o(\lambda) = F_i(\lambda) \mathrm{e}^{-\tau_V k(\lambda)}\)

Note that parameterizing by optical depth rather than total attenuation \(\mathrm{A_V}\) is just a matter of taste since they only differ by a factor 1.08. The wavelength dependent part of the relationship is the same.

The second simplification results from the fact that the UV or 2175Å “bump” in the attenuation curve will never be redshifted into the spectral range of MaNGA data and in any case the subset of the EMILES library I currently use doesn’t extend that far into the UV. That removes the bump amplitude parameter and the second term in Salim et al.’s equation 3, reducing it to the form

\(k_{mod}(\lambda) = k_{Cal}(\lambda)(\frac{\lambda}{5500})^\delta\)

The published expression for \(k(\lambda)\) in Calzetti et al. (2000) is given in two segments with a small discontinuity due to rounding at the transition wavelength of 6300Å. This produces a small unphysical discontinuity when applied to spectra, so I just made a polynomial fit to the Calzetti curve over a longer wavelength range than gets used for modeling MaNGA or SDSS data. Also, I make the wavelength parameter \(y = 5500/\lambda\) instead of using the wavelength in microns as in Calzetti. With these adjustments Calzetti’s relation is, to more digits than necessary:

\(k_{Cal}(y) = -0.10177 + 0.549882y + 1.393039 y^2 – 1.098615 y^3 + 0.260628 y^4\)

and Salim’s modified curve is

\(k_{mod}(\lambda) = k_{Cal}(\lambda)y^\delta\)

Note that δ has the opposite sign as in Salim et al. Here is what the curve looks like over the observer frame wavelength range of MaNGA. A positive value of δ produces a steeper attenuation curve than Calzetti’s, while a negative value is shallower (grayer in common astronomers’ jargon). Salim et al. found typical values to range between about -0.1 and +1.

For a first pass attempt at modeling some real data I chose a spectrum from near the northern nucleus of Mrk 848 but outside the region of the highest velocity outflows. This spectrum had large optical depth \(\tau_V \approx 1.52\) and the unusual nature of the galaxy gave reason to think its extinction curve might differ significantly from Calzetti’s.

Encouragingly, the Stan model converged without difficulty and with an acceptable run time. Below are some posterior plots of the two attenuation parameters and a comparison to the optical depth parameter in the unmodified Calzetti dust model. I used a fairly informative prior of Normal(0, 0.5) for the parameter delta. The data actually constrain the value of delta, since its posterior marginal density is centered around -0.06 with a standard deviation of just 0.02. In the pairs plot below we can see there’s some correlation between the posteriors of tauv and delta, but not so much as to be concerning (yet).

Overall the modified Calzetti model favors a slightly grayer attenuation curve with lower absolute attenuation:

Here’s a quick look at the effect of the model modification on some key quantities. In the plot below the red symbols are for the unmodified Calzetti attenuation model, and gray or blue the modified one. These show histograms of the marginal posterior density of total stellar mass, 100Myr averaged star formation rate, and specific star formation rate. Because the modified model has lower total attenuation it needs fewer stars, so the lower stellar mass (by ≈ 0.05 dex) is a fairly predictable consequence. The star formation rate is also lower by a similar amount, making the estimates of specific star formation rate nearly identical.

The lower right pane compares model mass growth histories. I don’t have any immediate intuition about how the difference in attenuation models affects the SFH models, but notice that both show recent acceleration in star formation, which was a main theme of my Markarian 848 posts.

So, this first run looks ok. Of course the problem with real data is there’s no way to tell if a model modification actually brings it closer to reality — in this case it did improve the fit to the data a little bit (by about 0.2% in log likelihood) but some improvement is expected just from adding a parameter.

My concern right now is that if the dust attenuation is near 0 the data can’t constrain the value of δ. The best that can happen in this situation is for the model to return the prior. Preliminary investigation of a low attenuation spectrum (per the original model) suggests that in fact a tighter prior on delta is needed than what I originally chose.