diff --git a/docs/overview/overview_3_features.md b/docs/overview/overview_3_features.md index 37009aaad..d7ff94a9b 100644 --- a/docs/overview/overview_3_features.md +++ b/docs/overview/overview_3_features.md @@ -224,9 +224,84 @@ accounted for in the model to ensure robust and accurate fits. Checkout `autogalaxy_workspace/notebooks/features/sky_background.ipynb` to learn how to use include the sky background in your model. -## Other +## Mass Models -- mass models (aris paper) -- Automated pipelines / SLaM. -- Dark matter subhalos. -- Graphical models. +The examples above model the lens's mass with an isothermal profile, but **PyAutoLens** supports a wide +range of mass models: + +Total mass profiles (e.g. the isothermal and power-law) represent the combined stellar and dark matter mass of the +lens galaxy with a single profile. Decomposed mass models fit the stellar and dark matter separately, tying the +stellar mass to the light (e.g. via an MGE) and adding an NFW dark matter halo, directly measuring the balance of +stellar and dark matter in the galaxy. + +Multipole perturbations (m=1, m=3 and m=4) extend any mass model with departures from elliptical symmetry, capturing +lopsidedness, boxiness / disciness and bar-like structures in the mass distribution. Accounting for this angular +complexity is critical for many science cases, as unmodeled angular structure can masquerade as other signals, +for example dark matter substructure. + +The following paper measures angular mass complexity alongside dark matter substructure in JWST +strong lens imaging: + +The role of the m=1 multipole (lopsidedness) is detailed in: + +Checkout `autolens_workspace/*/guides/profiles` for the full range of mass profiles +and `autolens_workspace/*/imaging/features/advanced/mass_stellar_dark` for decomposed stellar + dark matter +modeling. + +## Automated Pipelines / SLaM + +Fitting complex lens models (e.g. a decomposed mass model with a pixelized source) in one non-linear search is +often infeasible: the parameter space is too complex for the fit to converge reliably or efficiently. + +Search chaining breaks the fit into a sequence of simpler searches, where the results of each search initialize +the model of the next. The Source, Light and Mass (SLaM) pipelines are **PyAutoLens**'s pre-built implementation +of this approach: they first build a robust model of the source, then the lens's light, and finally its mass, +gradually increasing model complexity. The SLaM pipelines have been used in many published **PyAutoLens** analyses +and are the recommended way to perform detailed lens modeling of large samples. + +Checkout `autolens_workspace/*/guides/modeling/slam_start_here.py` to get started with the SLaM pipelines. + +## Dark Matter Subhalos + +The dark matter model predicts that galaxies are surrounded by many low-mass dark matter subhalos, which host no +stars and are therefore invisible to ordinary observations. Strong lensing is one of the only probes which can +detect them, via the small perturbations they imprint on a lensed source's light, testing the nature of +dark matter (e.g. cold, warm or self-interacting). + +**PyAutoLens** provides a complete dark matter subhalo analysis: lens models with and without a subhalo are fitted +and compared via their Bayesian evidence, quantifying whether the data favors the subhalo's presence. Sensitivity +mapping then simulates subhalos of different masses at different locations in the data, quantifying which +subhalos a given dataset could actually detect. + +The following paper performs this analysis on a sample of HST strong lenses: + +Checkout `autolens_workspace/*/imaging/features/advanced/subhalo/detect` for subhalo detection +and `autolens_workspace/*/imaging/features/advanced/subhalo/sensitivity` for sensitivity mapping. + +## Graphical Models + +The examples above fit each strong lens dataset one-by-one. However, many lens properties are shared across a +sample (e.g. population-level mass distributions, cosmological parameters), and fitting lenses independently does +not exploit this shared structure. + +Graphical models fit multiple datasets simultaneously, explicitly defining which parameters are unique to each +lens and which are shared across the sample. For example, the Hubble constant can be inferred jointly from many +time-delay lenses, with each lens having its own mass model but all sharing a single H0. Hierarchical extensions +assume parameters are drawn from a parent distribution (e.g. the population's distribution of mass slopes), whose +properties are inferred from the full sample, extracting significantly more information than one-by-one fitting. + +Checkout `autolens_workspace/*/guides/modeling/advanced/graphical.py` to learn how to fit a graphical model +and `autolens_workspace/*/guides/modeling/advanced/hierarchical.py` for hierarchical models. + +## Weak Lensing + +In the weak lensing regime, a foreground mass distribution (e.g. a galaxy cluster) subtly distorts the shapes of +many background galaxies, coherently aligning their ellipticities, without producing the multiple images and +giant arcs of strong lensing. + +**PyAutoLens** fits weak lensing shear catalogues using the same mass profiles and non-linear search tools as +strong lensing: model-independent mass maps, parametric halo model fits and tangential shear profiles. Its +quickstart example fits the real JWST-era shape catalogue of Abell 2744, and strong and weak lensing data can +be combined in joint analyses of the same cluster. + +Checkout `autolens_workspace/*/weak/start_here.py` to fit your first weak lensing dataset.