PyAutoFit: A Classy Probabilistic Programming Language for Model Composition and Fitting

Nightingale, J. W.; Hayes, Richard G.; Griffiths, Matthew

doi:10.21105/joss.02550

Cited by 20 publications

(7 citation statements)

References 17 publications

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“…• Astropy (Astropy Collaboration et al, 2013;Price-Whelan et al, 2018) • COLOSSUS (Diemer, 2018) • corner.py (Foreman-Mackey, 2016) • dynesty (Speagle, 2020) • emcee (Foreman-Mackey et al, 2013) • Matplotlib (Hunter, 2007) • numba (Lam et al, 2015) • NumPy (van der Walt et al, 2011) • PyAutoFit (Nightingale, Hayes, & Griffiths, 2021) • PyLops (Ravasi & Vasconcelos, 2019) • PyNUFFT (Lin, 2018) • pyprojroot (https://github.com/chendaniely/pyprojroot) • PySwarms (Miranda, 2018) • scikit-image (Van der Walt et al, 2014) • scikit-learn (Pedregosa et al, 2011) • Scipy (Virtanen et al, 2020) Related Software…”

Section: Software Citationsmentioning

confidence: 99%

PyAutoGalaxy: Open-Source Multiwavelength Galaxy Structure & Morphology

Nightingale¹,

Amvrosiadis²,

Hayes³

et al. 2023

JOSS

Self Cite

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Nearly a century ago, Edwin Hubble famously classified galaxies into three distinct groups: ellipticals, spirals and irregulars (Hubble, 1926). Today, by analysing millions of galaxies with advanced image processing techniques Astronomers have expanded on this picture and revealed the rich diversity of galaxy morphology in both the nearby and distant Universe (Kormendy, 2015;Van Der Wel et al., 2012;Vulcani et al., 2014). PyAutoGalaxy is an open-source Python 3.8+ package for analysing the morphologies and structures of large multiwavelength galaxy samples, with core features including fully automated Bayesian model-fitting of galaxy two-dimensional surface brightness profiles, support for imaging and interferometer datasets and comprehensive tools for simulating galaxy images. The software places a focus on big data analysis, including support for hierarchical models that simultaneously fit thousands of galaxies, massively parallel model-fitting and an SQLite3 database that allows large suites of modeling results to be loaded, queried and analysed. Accompanying PyAutoGalaxy is the autogalaxy workspace, which includes example scripts, datasets and the HowToGalaxy lectures in Jupyter notebook format which introduce non-experts to studies of galaxy morphology using PyAutoGalaxy. Readers can try PyAutoGalaxy right now by going to the introduction Jupyter notebook on Binder or checkout the readthedocs for a complete overview of PyAutoGalaxy's features.

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Section: Software Citationsmentioning

confidence: 99%

PyAutoGalaxy: Open-Source Multiwavelength Galaxy Structure & Morphology

Nightingale¹,

Amvrosiadis²,

Hayes³

et al. 2023

JOSS

Self Cite

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“…In real data, isophotal twists, elliptical gradients, and offsets in the centres and alignments of dark and stellar matter all increase uncertainty on model parameters. These have all been seen in the stellar mass of SLACS lenses (Nightingale et al 2019). We have not been able to quantify how much each source contributes to the scatter or bias in a real measurement of external shear.…”

Section: Strong Lensing External Shears Are Not Measuring Shearmentioning

confidence: 84%

Strong gravitational lensing's `external shear' is not shear

Etherington¹,

Nightingale²,

Massey³

et al. 2023

Preprint

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The distribution of mass in galaxy-scale strong gravitational lenses is often modelled as an elliptical power law plus 'external shear', which notionally accounts for neighbouring galaxies and cosmic shear. We show that it does not. Except in a handful of rare systems, the best-fit values of external shear do not correlate with independent measurements of shear: from weak lensing in 45 Hubble Space Telescope images, or in 50 mock images of lenses with complex distributions of mass. Instead, the best-fit shear is aligned with the major or minor axis of 88% of lens galaxies; and the amplitude of the external shear increases if that galaxy is disky. We conclude that 'external shear' attached to a power law model is not physically meaningful, but a fudge to compensate for lack of model complexity. Since it biases other model parameters that are interpreted as physically meaningful in several science analyses (e.g. measuring galaxy evolution, dark matter physics or cosmological parameters), we recommend that future studies of galaxy-scale strong lensing should employ more flexible mass models.

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“…In contrast to that, we are able to model a sample of dozens of lenses with our automated traditional pipeline to better accuracy and we can also evaluate the quality of the fit in terms of a χ 2 , which is not possible for the network output. The glee tools.py code enables us to further refine the models obtained with our fully automated procedure or also other dedicated automated modeling codes (e.g., Hezaveh et al 2017;Perreault Levasseur et al 2017;Nightingale et al 2018Nightingale et al , 2021aPearson et al 2019Pearson et al , 2021Adam et al 2022;Ertl et al 2022;Etherington et al 2022;Schmidt et al 2023). The combination of all three codes enables us to handle different sample sizes of lenses, and thus takes us a huge step forward in handling the newly detected lenses in current and upcoming wide-field imaging surveys such as LSST and Euclid.…”

Section: Discussionmentioning

confidence: 99%

Holismokes

Schuldt¹,

Suyu²,

Cañameras³

et al. 2023

A&A

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Modeling of strongly gravitationally lensed galaxies is often required in order to use them as astrophysical or cosmological probes. With current and upcoming wide-field imaging surveys, the number of detected lenses is increasing significantly such that automated and fast modeling procedures for ground-based data are urgently needed. This is especially pertinent to short-lived lensed transients in order to plan follow-up observations. Therefore, we present in a companion paper a neural network predicting the parameter values with corresponding uncertainties of a singular isothermal ellipsoid (SIE) mass profile with external shear. In this work, we also present a newly developed pipeline glee auto.py that can be used to model any galaxy-scale lensing system consistently. In contrast to previous automated modeling pipelines that require high-resolution space-based images, glee auto.py is optimized to work well on ground-based images such as those from the Hyper-Suprime-Cam (HSC) Subaru Strategic Program or the upcoming Rubin Observatory Legacy Survey of Space and Time. We further present glee tools.py, a flexible automation code for individual modeling that has no direct decisions and assumptions implemented on the lens system setup or image resolution. Both pipelines, in addition to our modeling network, minimize the user input time drastically and thus are important for future modeling efforts. We applied the network to 31 real galaxy-scale lenses of HSC and compare the results to traditional, Markov-Chain Monte-Carlo sampling-based models obtained from our semi-autonomous pipelines. In the direct comparison, we find a very good match for the Einstein radius. The lens mass center and ellipticity show reasonable agreement. The main discrepancies pretrain to the external shear, as is expected from our tests on mock systems where the neural network always predicts values close to zero for the complex components of the shear. In general, our study demonstrates that neural networks are a viable and ultra fast approach for measuring the lens-galaxy masses from ground-based data in the upcoming era with ∼ 10 5 lenses expected.

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PyAutoFit: A Classy Probabilistic Programming Language for Model Composition and Fitting

Cited by 20 publications

References 17 publications

PyAutoGalaxy: Open-Source Multiwavelength Galaxy Structure & Morphology

PyAutoGalaxy: Open-Source Multiwavelength Galaxy Structure & Morphology

Strong gravitational lensing's `external shear' is not shear

Holismokes

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