Everyone and their dog makes a Spectral Energy Distribution (SED) fitting code. It’s not hard. The data are a few numbers, you just need Gaussian statistics, and connect to a model library.
The design choices vary wildly though:
Computation and uncertainty quantification
cigale is a community platform mostly focused on galaxies, with quite limited uncertainty quantification abilities. Their grid computation method faces the curse of dimenstionality and consequently allows only a handful of free parameters. Because of this, it is essentially unsuitable for AGN. The better computational approach pursued by AGNFitter and FortesFit is to at least interpolate two grids (galaxy + AGN), and scale them linearly (M* and LAGN, respectively). This escapes the curse of dimensionality. GRAHSP computes SEDs without a grid, on-the-fly.
The scalable approach to millions of objects is precomputing model photometry sampled from the parameter space and creating a neural network emulator. This is a lowd-to-lowd mapping, so quite feasible.
Another approach is simulation-based inference.
Galaxy model
Galaxy archeology focuses on reconstructing the star-formation history (SFH). For this, a recent hubbub was the change from parametric SFHs to non-parametric variants, including with Gaussian processes (see for example Iyer et al, 2019). For AGN hosts, we are usually quite happy if we can figure out whether a galaxy is there (M* lower bound) and whether it is red, green or blue (which in any case, will be attributed to AGN feedback). If you read the papers advocating non-parametric approaches closely, they actually do say that for estimating the SFR in the last 100Myrs, it does not matter what you use.
The more recent hubbub are binary populations (BPASS model), especially in young and low-metallicity environments seen in the UV. This is driven by high-z JWST spectra and photometry, but probably not relevant for z<6 AGN given current and future data quality.
So most of the time probably something simple suffices, such as a delayed-exponential plus burst. This is quite at odds with what galaxy researchers want to do, and thus – multiple codes.
AGN model
If you then look at AGN physical models, there are only a few in existence and use:
- for the accretion flow, Kobuta&Done’s AGNSED. seems to be most popular these days, which has the benefit of producing X-ray and optical emission.
- for the torus: Fritz+06, CLUMPY/Nikutta08, SKIRTOR, CAT3D
- there is essentially no good physics models for lines (broad, narrow, Fe forest).
In my opinion, if you are interested to study component X of a system with N components, you better model the N-1 other components correctly, otherwise you will skew your inference about the component X. So you need empirical models for the N-1 components.
Looking at empirical models, most common are Dale+14’s templates and, more recently, Temple+21 (broken powerlaw accretion disc, hot dust black body, broad and narrow lines, Fe forest, Balmer continuum), which has more parameters. GRAHSP’s model is similar in spirit, with a bending powerlaw accretion disc, hot and cold torus, broad and narrow lines, Fe forest, Balmer continuum). GRAHSP also models variability.
If we believe we have an empircal model flexible enough to describe the entire AGN population to the quality of data available for the next few years, the next step is to bridge the gap between empirical models and physical models.
The AGNSED model is a good starting point, because it models accretion physics. We just lack all the other parts:
- Broad lines and Fe forest: we are far from a self-consistent model with enumerable parameters. This has to remain an empirical model.
- Hot dust: this is essentially a black body.
- Cool dust: probably enough to model with a few parameters.
- Si feature: since this can be due to the host orientation, it is not worth modelling self-consistently.
-> ML framing: Can we use SDSS spectra to derive a broad line and Fe forest model? Ideally, as a function of AGNSED parameters? For this, the classic starting point is to identify line-free portions of the spectrum, and fit AGNSED. We also know the MBH and FWHMs of the best lines (Ha, Hb, MgII), which we tie to the AGNSED parameters. This assumes single-epoch black hole mass estimates hold. Then we need an autoencoder to, conditional on the continuum level, predict the remaining mess of the emission spectrum, and the latent space holds the unknown physics. For the architecture, probably something like spender is necessary. It would be best to preprocess by subtracting the AGNSED continuum fit and remove attenuated and galaxy-dominated AGN.
Biases in AGN morphology
Galaxy morphology is very pretty to look at, but in terms of physics, mostly lacks useful information and is very difficult to analyse in a reproducible fashion.
There are a few machine learning tools which use neural networks to classify morphology. Unfortunately, essentially all of them lack tests how the morphology indicators are impacted when you have a AGN in the center. So I do not trust them.
Such tools can be useful for detecting weirdos for follow-up, but that’s not reliable demographics, and we need demographics to escape the curse of AGN flickering.
-> ML framing: Can we carry over morphology information? Probably, if we use the contamination tests to quantify the impact of the morphology tool. The alternative, training a ML tool on images with contamination would be better, but likely we would still have false positives and false negatives (on morphology) to deal with.
Unbiased Spatial+SED fitting
There are a few approaches now which attempt resolved SED fitting. However, essentially all of them suffer from systematics because their morphology model is incomplete. You can see this by noticing that the decomposition is very fragile across bands, while flux errors are tiny.
If you can reliably detect an extended galaxy component, you are in luck though, and able to break SED fitting degeneracies with that extended flux lower limit (EFLL) to the galaxy component. I think this will be powerful for Euclid.
-> ML framing: Grant Stevens used the fact that AGN are the minority of galaxies to identify “AGN” as outliers in their central morphology. In this operational definition, any brighter than usual nucleus is an AGN. Similarly, we can use normal galaxies, and insert a blue-ish point source into the center and call that new hybrid object an AGN.
If you go the empirical route, starting with an AGN population for the blue-ish point is a bad idea, because then separating out will be confused by the host galaxy the AGN carried with it. Instead, you can start with isolated stars and paste them into the center.
For Euclid this should work, by selecting low extendedness objects and working in VIS, then training a ML tool to look at cutout images and predict the aperture flux without the central source. The loss should be punished for over-predicting the flux.
Unbiased AGN host morphology
Can we infer reliable morphology information? Probably, if we use the contamination tests to quantify the impact of the morphology tool. The alternative, training a ML tool on images with contamination would be better, but likely we would still have false positives and false negatives (detecting mergers for example) to deal with. The proper way to work here is to assign “Unknown” as a label when the AGN makes morphology unreliable and/or incorporate the skewed inference response into the analyses. No one is doing this properly though.
One of the simplest properties of galaxies is their compactness, quantified as half-light radius. From the curve of growth, one could in principle read this off even for AGN hosts, but the half-light is not known if you do not know the total.
Selection effects
Lastly, quantifying the properties of members of a sample tells you about that sample and its selection, but not directly about the underlying demographics. There are many papers with Venn diagrams, but these hold little information because it remains unclear whether the missing subsets are expected or unexpected considering the depth of the selections.
So it is essential to quantify selection effects. Ideally, you would analyse multiple selections and verify that you get the same results. A single generative model that creates populations of X-ray selected, optically selected, infrared selected, variability selected AGN is still missing. This is a necessary step to understand missing and disjoint populations.