BAGPIPES: Bayesian Galaxy Spectral Inference

• BAGPIPES is a Python-based framework that uses Bayesian methods (via MultiNest nested sampling) to generate and fit galaxy spectra from UV to far-IR wavelengths.
• It offers a modular design that facilitates the exploration of various star-formation histories, dust attenuation laws, and nebular emission models to infer key physical galaxy parameters.
• Validation studies and diverse applications—from quiescent to high-redshift galaxies—demonstrate its capability to quantify uncertainties and reveal biases in inferred mass-assembly histories.

BAGPIPES, an acronym for Bayesian Analysis of Galaxies for Physical Inference and Parameter EStimation, is a Python tool for generating model galaxy spectra and fitting them to arbitrary combinations of spectroscopic and photometric data within a fully Bayesian framework (Carnall et al., 2017). Its stated primary goals are on-the-fly generation of physically realistic galaxy spectra (200 Å–1 mm) including stars, nebular emission, dust and IGM effects, and Bayesian fitting of these models—either photometric, spectroscopic or combined—to infer physical parameters via MultiNest nested sampling (Carnall et al., 2017). Across the literature summarized here, BAGPIPES functions both as a methodological framework for spectral energy distribution (SED) inference and as a vehicle for studying how assumptions about star-formation histories, attenuation laws, and wavelength coverage propagate into inferred galaxy properties such as stellar mass, star-formation rate, age, metallicity, quenching timescale, and formation redshift (Carnall et al., 2017).

1. Definition, scope, and design philosophy

BAGPIPES was introduced as a new Python tool which can be used to rapidly generate complex model galaxy spectra and to fit these to arbitrary combinations of spectroscopic and photometric data using the MultiNest nested sampling algorithm (Carnall et al., 2017). The framework is described as open-source, flexible, high-performance, and fully Bayesian, with a design philosophy centered on modular, component-based construction of a galaxy model and an intuitive Python API in which all model components and priors are passed as standard dictionaries (Carnall et al., 2017).

Its model space spans the UV–far-IR wavelength range, and its capabilities include arbitrary filter-set photometry, arbitrary-resolution spectra, stellar and gas velocity-dispersion convolution, nebular emission, dust attenuation and re-emission, and IGM absorption (Carnall et al., 2017, Carnall et al., 2018). The code can load any grid of SSPs, with the default setup described as BC03 + MILES, Kroupa IMF (Carnall et al., 2017). A related paper characterizes BAGPIPES as a framework whose principal goals are to enable physically motivated priors on model parameters, rapidly compute posterior distributions for galaxy properties, and expose how priors and model choices affect inferences about physical parameters and mass-assembly histories (Carnall et al., 2018).

The scope of BAGPIPES has expanded beyond its original quiescent-galaxy use case. In the studies summarized here it is used for quiescent galaxies in UltraVISTA, low-redshift galaxies in GAMA, dusty galaxies at cosmic noon, faint galaxies at $z\sim 9$–16, stellar clusters in the Small Magellanic Cloud, and spectro-photometric JWST/NIRISS analyses of massive galaxies at $1Carnall et al., 2017, Carnall et al., 2018, Wise et al., 2023, Morales et al., 2023, Souza et al., 2023, Annunziatella et al., 23 Aug 2025). This suggests that BAGPIPES is less a narrowly specialized fitting script than a general SED-inference environment whose scientific behavior depends strongly on the chosen forward model and priors.

2. Spectral synthesis architecture and physical components

The generative model in BAGPIPES combines stellar populations, SFH components, dust attenuation, nebular emission, and IGM absorption into a composite luminosity. In the technical summary, each SFH is built from one or more components $SFR_j(t)$ summed as

$SFR(t)=\sum_{j=1}^{N_c} SFR_j(t),$

and the rest-frame luminosity per unit wavelength is written as

$$L_\lambda(\lambda)=\sum_{j}\sum_{i} SFR_j(t_i)\,SSP(a_i,\lambda,Z_j)\,T^+(a_i,\lambda)\,T^0(a_i,\lambda)\,\Delta a_i.$$

The observed flux is then

$$f_{\lambda_{\rm obs}}(\lambda_{\rm obs})=\frac{L_\lambda(\lambda_{\rm obs}/(1+z))}{4\pi D_L^2(1+z)}\cdot T_{\rm IGM}(\lambda,z)$$

(Carnall et al., 2017).

Nebular emission is described as being based on Cloudy (Byler 2017 prescription, age-dependent transmission $T^+$; user-set ionization parameter $\log U$ and birth-cloud lifetime $a_{BC}$), with 124 tracked features plus diffuse continuum and warm dust for young stellar populations, and with energy normalization to ensure photon conservation (Carnall et al., 2017). Dust attenuation can be modeled using Calzetti 2000, Cardelli 1989, and Charlot & Fall two-component laws, while cold dust re-emission is represented by a single-temperature greybody with

$S_{gb}(\nu)\propto \nu^\beta B_\nu(T)$

(Carnall et al., 2017). IGM attenuation is implemented using the Inoue 2014 analytic prescription over $1Carnall et al., 2017).

Several later studies exploit or modify these ingredients in domain-specific ways. For dusty galaxies at $1single modified blackbody (optically thin) is used to re-emit the total energy absorbed in the UV–optical, with energy balance enforcing equality between absorbed and re-emitted luminosity (Wise et al., 2023). In dust-law recovery experiments, BAGPIPES is coupled to a Salim-modified Calzetti law,

$1

with free $1Meldorf et al., 2023). For high-redshift JWST galaxies, the code uses Bruzual & Charlot (2003) SSPs, Calzetti et al. (1994) attenuation, Cloudy-based nebular emission, and a free Lyman-continuum escape fraction $1 to allow SEDs bluer than the default $1Morales et al., 2023). For integrated photometry of stellar clusters, the setup is reduced to a single SSP with dust parameters fixed to zero and the ionization parameter frozen, such that emission lines play no role (Souza et al., 2023).

A plausible implication is that BAGPIPES should be understood as a forward-modeling container rather than a single astrophysical model. The code’s outputs are conditioned not only on the data, but also on choices about SSP library, attenuation law, dust-emission prescription, and nebular treatment.

3. Star-formation histories and parameterization choices

A central feature of BAGPIPES is its support for multiple parametric SFH families and, in later applications, non-parametric ones. The technical summary lists the following implemented forms, all zero for $1delta, constant, exponential, delayed-$1, log-normal, double-power-law, and custom user-supplied tabulated histories (Carnall et al., 2017). The corresponding forms include

$SFR_j(t)$0

$SFR_j(t)$1

$SFR_j(t)$2

and

$SFR_j(t)$3

with $SFR_j(t)$4 controlling the falling slope and $SFR_j(t)$5 the rising slope in the double-power-law case (Carnall et al., 2017, Carnall et al., 2018).

The 2018 analysis of parametric models emphasizes that BAGPIPES implements four widely used, three-parameter families of parametric SFHs—exponentially declining, delayed exponentially declining, lognormal, and double power law—each normalized to the total mass formed by observation time (Carnall et al., 2018). That study shows that these models impose strong implicit priors on derived quantities even when those quantities are not directly assigned priors. Specifically, by drawing SFHs from the prior alone, Carnall et al. find that all four parametric families impose strongly peaked priors on sSFR $SFR_j(t)$6 and favor $SFR_j(t)$7 biased to times several Gyr later than the cosmic mass-assembly epoch implied by the Madau & Dickinson (2014) SFRD curve (Carnall et al., 2018). They further report that photometric data cannot discriminate among these SFH families, despite the fact that inferred masses, SFRs, and mass-weighted ages vary with SFH choice by at least 0.1, 0.3 and 0.2 dex respectively on high-quality mocks (Carnall et al., 2018).

The original BAGPIPES paper responds to this model-selection issue pragmatically in its quiescent-galaxy application by adopting double-power-law SFHs with log priors on $SFR_j(t)$8, finding on realistic MUFASA mocks that this configuration yields stellar mass bias +0.02 dex and $SFR_j(t)$9 and $SFR(t)=\sum_{j=1}^{N_c} SFR_j(t),$0 unbiased to $SFR(t)=\sum_{j=1}^{N_c} SFR_j(t),$1 Gyr, outperforming an exponential-$SFR(t)=\sum_{j=1}^{N_c} SFR_j(t),$2 parameterization (Carnall et al., 2017). Later work at $SFR(t)=\sum_{j=1}^{N_c} SFR_j(t),$3 expands the BAGPIPES SFH repertoire further by testing a delayed-$SFR(t)=\sum_{j=1}^{N_c} SFR_j(t),$4 single-population model, a two-component model combining delayed-$SFR(t)=\sum_{j=1}^{N_c} SFR_j(t),$5 and double-power-law burst terms, and a non-parametric “continuity” SFH with seven logarithmically spaced bins and Gaussian priors on adjacent-bin log-SFR ratios (Annunziatella et al., 23 Aug 2025). That study reports that non-parametric SFHs generally imply an earlier and slower mass assembly compared to parametric forms, especially for galaxies at $SFR(t)=\sum_{j=1}^{N_c} SFR_j(t),$6 (Annunziatella et al., 23 Aug 2025).

The literature therefore treats SFH selection not as a secondary implementation detail but as one of the dominant determinants of BAGPIPES inferences. The recurring conclusion is not that one universal SFH form is optimal, but that parametric convenience can encode highly informative priors that are difficult to overcome with broadband photometry alone (Carnall et al., 2018).

4. Bayesian inference, software organization, and workflow

BAGPIPES uses a standard Bayesian construction in which the posterior probability density of parameters $SFR(t)=\sum_{j=1}^{N_c} SFR_j(t),$7 given data $SFR(t)=\sum_{j=1}^{N_c} SFR_j(t),$8 is

$SFR(t)=\sum_{j=1}^{N_c} SFR_j(t),$9

with $$L_\lambda(\lambda)=\sum_{j}\sum_{i} SFR_j(t_i)\,SSP(a_i,\lambda,Z_j)\,T^+(a_i,\lambda)\,T^0(a_i,\lambda)\,\Delta a_i.$$0 serving as the Bayesian evidence (Carnall et al., 2018). For photometric data with independent Gaussian errors, the technical summary gives

$$L_\lambda(\lambda)=\sum_{j}\sum_{i} SFR_j(t_i)\,SSP(a_i,\lambda,Z_j)\,T^+(a_i,\lambda)\,T^0(a_i,\lambda)\,\Delta a_i.$$1

and BAGPIPES accesses MultiNest through PyMultiNest for nested sampling, posterior exploration, and evidence evaluation (Carnall et al., 2017). The sampler is described as suitable for multimodal, degenerate, high-dimensional spaces, with convergence diagnostics including sampling efficiency and evidence uncertainty (Carnall et al., 2017).

The documented software architecture includes bagpipes/model.py, sps.py, sfh.py, nebular.py, dust.py, igm.py, veldisp.py, priors.py, fitting.py, and visualization.py (Carnall et al., 2017). Dependencies are listed as Python 3.x, numpy, scipy, astropy, pymultinest, matplotlib, with pre-computed Cloudy tables included (Carnall et al., 2017). Inputs can be provided as Python dicts or JSON, with photometry as Astropy Table or numpy arrays, and outputs include posterior samples as HDF5/JSON alongside built-in serialization methods such as .to_json() and .to_fits() (Carnall et al., 2017).

The typical workflow described in the technical summary is: define model components and priors as Python dictionaries, load observational data, instantiate a Fit object with sampler='multinest', run the fit, extract posterior samples and summaries, save results, and generate corner and SED plots (Carnall et al., 2017). Typical runtime is reported as $$L_\lambda(\lambda)=\sum_{j}\sum_{i} SFR_j(t_i)\,SSP(a_i,\lambda,Z_j)\,T^+(a_i,\lambda)\,T^0(a_i,\lambda)\,\Delta a_i.$$2–$$L_\lambda(\lambda)=\sum_{j}\sum_{i} SFR_j(t_i)\,SSP(a_i,\lambda,Z_j)\,T^+(a_i,\lambda)\,T^0(a_i,\lambda)\,\Delta a_i.$$3 sec per galaxy with $$L_\lambda(\lambda)=\sum_{j}\sum_{i} SFR_j(t_i)\,SSP(a_i,\lambda,Z_j)\,T^+(a_i,\lambda)\,T^0(a_i,\lambda)\,\Delta a_i.$$4, depending on model complexity and data volume, while model generation proceeds at $$L_\lambda(\lambda)=\sum_{j}\sum_{i} SFR_j(t_i)\,SSP(a_i,\lambda,Z_j)\,T^+(a_i,\lambda)\,T^0(a_i,\lambda)\,\Delta a_i.$$5 seds/s and a typical fit on a dual-core machine takes $$L_\lambda(\lambda)=\sum_{j}\sum_{i} SFR_j(t_i)\,SSP(a_i,\lambda,Z_j)\,T^+(a_i,\lambda)\,T^0(a_i,\lambda)\,\Delta a_i.$$6–30 min per galaxy (Carnall et al., 2017).

Later applications retain this basic inference pattern while altering the likelihood surface through different data modalities. The dusty-galaxy study uses a Gaussian photometric likelihood over UV-to-FIR bands and notes that, in the general BAGPIPES framework, any nondetection can be included as an upper-limit by replacing the Gaussian term in the likelihood by an integral up to that limit (Wise et al., 2023). The JWST high-redshift study adds a fractional-error term $$L_\lambda(\lambda)=\sum_{j}\sum_{i} SFR_j(t_i)\,SSP(a_i,\lambda,Z_j)\,T^+(a_i,\lambda)\,T^0(a_i,\lambda)\,\Delta a_i.$$7 scaling $$L_\lambda(\lambda)=\sum_{j}\sum_{i} SFR_j(t_i)\,SSP(a_i,\lambda,Z_j)\,T^+(a_i,\lambda)\,T^0(a_i,\lambda)\,\Delta a_i.$$8 and draws 1,000 posterior models per galaxy for downstream UV-slope analysis (Morales et al., 2023). The MIDIS spectro-photometric analysis extends the data vector to include binned spectral pixels, convolving the model SED to $$L_\lambda(\lambda)=\sum_{j}\sum_{i} SFR_j(t_i)\,SSP(a_i,\lambda,Z_j)\,T^+(a_i,\lambda)\,T^0(a_i,\lambda)\,\Delta a_i.$$9 with the grism line-spread function and fitting the joint photometric-plus-spectroscopic likelihood under independent Gaussian uncertainties (Annunziatella et al., 23 Aug 2025).

5. Validation, degeneracies, and methodological limitations

BAGPIPES has been validated both through recovery experiments on mock galaxies and through tests aimed at identifying parameter degeneracies. In the original quiescent-galaxy paper, 677 simulated massive ($$f_{\lambda_{\rm obs}}(\lambda_{\rm obs})=\frac{L_\lambda(\lambda_{\rm obs}/(1+z))}{4\pi D_L^2(1+z)}\cdot T_{\rm IGM}(\lambda,z)$$0) quenched galaxies from MUFASA snapshots at $$f_{\lambda_{\rm obs}}(\lambda_{\rm obs})=\frac{L_\lambda(\lambda_{\rm obs}/(1+z))}{4\pi D_L^2(1+z)}\cdot T_{\rm IGM}(\lambda,z)$$1–2.5 are used to test recovery of SFHs (Carnall et al., 2017). The study reports that an exponential–$$f_{\lambda_{\rm obs}}(\lambda_{\rm obs})=\frac{L_\lambda(\lambda_{\rm obs}/(1+z))}{4\pi D_L^2(1+z)}\cdot T_{\rm IGM}(\lambda,z)$$2 SFH yields stellar mass bias +0.06 dex and underestimates formation and quenching times by $$f_{\lambda_{\rm obs}}(\lambda_{\rm obs})=\frac{L_\lambda(\lambda_{\rm obs}/(1+z))}{4\pi D_L^2(1+z)}\cdot T_{\rm IGM}(\lambda,z)$$3 Gyr, whereas the double-power-law SFH produces substantially smaller biases and stable recovery across observed redshift (Carnall et al., 2017). Evidence uncertainty is quoted as $$f_{\lambda_{\rm obs}}(\lambda_{\rm obs})=\frac{L_\lambda(\lambda_{\rm obs}/(1+z))}{4\pi D_L^2(1+z)}\cdot T_{\rm IGM}(\lambda,z)$$4 ($$f_{\lambda_{\rm obs}}(\lambda_{\rm obs})=\frac{L_\lambda(\lambda_{\rm obs}/(1+z))}{4\pi D_L^2(1+z)}\cdot T_{\rm IGM}(\lambda,z)$$5) with default settings (Carnall et al., 2017).

The broader methodological critique comes from the parametric-SFH analysis, which demonstrates that good photometric fits do not guarantee unbiased mass-assembly inference (Carnall et al., 2018). Carnall et al. show that all four tested SFH families provide statistically acceptable fits to mock photometry, yet produce systematically different stellar ages and SFRs, and that reconstructions of the cosmic SFRD from GAMA photometry peak at $$f_{\lambda_{\rm obs}}(\lambda_{\rm obs})=\frac{L_\lambda(\lambda_{\rm obs}/(1+z))}{4\pi D_L^2(1+z)}\cdot T_{\rm IGM}(\lambda,z)$$6, approximately 6 Gyr later than direct observations suggest (Carnall et al., 2018). Their conclusion is that simple parametric SFH models impose strong, informative priors that are often physically unmotivated and can dominate the inferred mass-assembly history (Carnall et al., 2018).

A distinct class of limitations arises from parameter degeneracies. In the dust-law study, BAGPIPES is used to show a pronounced posterior degeneracy between $$f_{\lambda_{\rm obs}}(\lambda_{\rm obs})=\frac{L_\lambda(\lambda_{\rm obs}/(1+z))}{4\pi D_L^2(1+z)}\cdot T_{\rm IGM}(\lambda,z)$$7 and $$f_{\lambda_{\rm obs}}(\lambda_{\rm obs})=\frac{L_\lambda(\lambda_{\rm obs}/(1+z))}{4\pi D_L^2(1+z)}\cdot T_{\rm IGM}(\lambda,z)$$8, and also between $$f_{\lambda_{\rm obs}}(\lambda_{\rm obs})=\frac{L_\lambda(\lambda_{\rm obs}/(1+z))}{4\pi D_L^2(1+z)}\cdot T_{\rm IGM}(\lambda,z)$$9 and SFR (Meldorf et al., 2023). However, adding WISE near-IR and mid-IR bands dramatically improves identifiability: the scatter in $T^+$0 shrinks from $T^+$1 mag to 0.06 mag, the scatter in $T^+$2 goes from 0.26 dex to 0.06 dex, and residual correlation coefficients fall from $T^+$3 to $T^+$4 (Meldorf et al., 2023). The same study concludes that BAGPIPES does not introduce spurious correlations between $T^+$5 and $T^+$6, and that the information required to constrain both parameters exists in the data, especially when IR bands are included (Meldorf et al., 2023).

At very high redshift, model flexibility itself becomes a limitation. The JWST NGDEEP analysis reports a blue-floor bias: even with $T^+$7, the adopted BC03+Calzetti dust+nebular continuum setup limits the bluest recoverable $T^+$8, and simulated recovery plateaus at $T^+$9 for intrinsically bluer slopes (Morales et al., 2023). That study therefore notes that more extreme stellar models would be needed to test $\log U$0 (Morales et al., 2023). This suggests that BAGPIPES can quantify uncertainty within a chosen model family while still inheriting hard boundaries from that family’s astrophysical ingredients.

6. Scientific results enabled by BAGPIPES

The original large-scale BAGPIPES application analyzed 9,289 quenched galaxies from UltraVISTA DR3 with $\log U$1 over $\log U$2, characterizing their SFHs using a mass-weighted formation redshift $\log U$3, a quenching redshift $\log U$4, and a dimensionless quenching timescale

$\log U$5

(Carnall et al., 2017). The study finds three distinct $\log U$6 populations: a rapid class with $\log U$7 corresponding to $\log U$8 Gyr quenching, a dominant class with $\log U$9 corresponding to 1–2 Gyr, and a slow class with $a_{BC}$0 corresponding to $a_{BC}$1 Gyr (Carnall et al., 2017). These are interpreted respectively as being consistent with quasar-mode AGN feedback, jet-mode AGN feedback, and cosmic gas exhaustion/strangulation (Carnall et al., 2017). The same analysis confirms a downsizing trend and finds that $a_{BC}$2 per cent of $a_{BC}$3 massive quenched galaxies undergo significant further evolution by $a_{BC}$4 (Carnall et al., 2017).

In the context of dusty massive galaxies at cosmic noon, BAGPIPES is used to assess the impact of adding Herschel-SPIRE data to UV-to-NIR fitting. For a sample of 92 massive dusty galaxies at $a_{BC}$5 to 3.0, the study finds that adding FIR/sub-mm data leaves stellar masses essentially unchanged, with

$a_{BC}$6

in almost all galaxies, but can shift SFRs by up to an order of magnitude (Wise et al., 2023). The direction of the SFR shift depends on the UV–NIR-inferred obscuration, and the changes are reported to correlate tightly with $a_{BC}$7, roughly as $a_{BC}$8 (Wise et al., 2023). This is presented as evidence that FIR/sub-mm data are essential for accurately deriving the SFRs of massive dusty galaxies at $a_{BC}$9 (Wise et al., 2023).

BAGPIPES has also been applied to measurement problems not centered on quenching. In the NGDEEP JWST study of 36 faint star-forming galaxies at $S_{gb}(\nu)\propto \nu^\beta B_\nu(T)$0–16, BAGPIPES-derived SED fits are used to estimate the UV spectral slope $S_{gb}(\nu)\propto \nu^\beta B_\nu(T)$1 and physical parameters (Morales et al., 2023). The reported medians for the full sample are $S_{gb}(\nu)\propto \nu^\beta B_\nu(T)$2, $S_{gb}(\nu)\propto \nu^\beta B_\nu(T)$3, $S_{gb}(\nu)\propto \nu^\beta B_\nu(T)$4, $S_{gb}(\nu)\propto \nu^\beta B_\nu(T)$5, $S_{gb}(\nu)\propto \nu^\beta B_\nu(T)$6, $S_{gb}(\nu)\propto \nu^\beta B_\nu(T)$7 mag, and $S_{gb}(\nu)\propto \nu^\beta B_\nu(T)$8 (Morales et al., 2023). The analysis finds no strong evidence for ultra-blue UV spectral slopes ($S_{gb}(\nu)\propto \nu^\beta B_\nu(T)$9) and states that the observations are consistent with metallicities $1 for galaxies of those stellar masses (Morales et al., 2023).

A further application moves outside galaxy-integrated SFH inference into stellar-cluster work. Using 12-band S-PLUS photometry, one study fits 88 clusters in the SMC with a single-SSP BAGPIPES model and constructs an empirical age–metallicity relation (Souza et al., 2023). The reported agreement with literature values is $1 and $1, while the cluster age distribution is described as bimodal with peaks at $1, and the metallicity distribution as bimodal at $1 (Souza et al., 2023). The study confirms an age gradient, with younger clusters toward the SMC center and older clusters at larger radii, but finds no significant metallicity gradient (Souza et al., 2023).

7. Extensions, interfaces, and future directions

BAGPIPES has acquired both interactive and spectro-photometric extensions. The pipes_vis interface is presented as a new interactive graphical user interface (GUI) tool pipes_vis based on Bagpipes, allowing real-time manipulation of a model galaxy's star formation history, dust and other relevant properties through sliders and text boxes, with the effect on the predicted SED reflected instantaneously (Leung et al., 2021). Its stated purpose is to help build intuition about the nonlinear relationship between galaxy properties and predicted SEDs, potentially helping to speed up fitting stages such as prior construction, and supporting undergraduate and graduate teaching (Leung et al., 2021). Within the boundaries of the provided abstract, pipes_vis is therefore best understood as a visualization layer rather than an alternative inference engine.

On the scientific side, the original BAGPIPES paper already identified future directions including joint photometry + spectroscopy fitting, non-parametric SFHs and hierarchical population modeling, and inclusion of $1 (Carnall et al., 2017). Subsequent work has partly realized this trajectory. The MIDIS study combines broadband photometry from HST and JWST with low-resolution grism spectroscopy from JWST/NIRISS, analyzing the data with BAGPIPES and a second code under different SFH assumptions (Annunziatella et al., 23 Aug 2025). It reports that inclusion of NIRISS spectroscopy significantly improves constraints on key physical parameters, such as the mass-weighted stellar age ($1, and that the most massive galaxies in the sample formed 50\% of their mass between $1 (Annunziatella et al., 23 Aug 2025). The same study finds that quiescent galaxies are, on average, older ($1 and assembled more rapidly at earlier times than star-forming counterparts, in support of the downsizing scenario (Annunziatella et al., 23 Aug 2025).

Across these developments, one recurrent theme is that BAGPIPES is most informative when used comparatively: comparing SFH classes, comparing data combinations, or comparing prior configurations. The code’s importance in the literature summarized here lies not only in parameter estimation, but also in making explicit how SED-fitting assumptions shape astrophysical conclusions (Carnall et al., 2017, Carnall et al., 2018).