Automated galaxy-galaxy strong lens modelling: no lens left behind

Etherington, Amy; Nightingale, J. W.; Massey, Richard; Cao, Xiaoyue; Robertson, Andrew W.; Amorisco, Nicola C.; Amvrosiadis, Aristeidis; Cole, Shaun; Frenk, Carlos S.; He, Qiuhan; Li, Ran; Tam, Sut-Ieng

doi:10.48550/arxiv.2202.09201

Cited by 4 publications

(7 citation statements)

References 52 publications

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“…There is also currently a lot of effort spent on automated modeling with-out machine learning (e.g., Nightingale et al 2018Nightingale et al , 2021Rojas et al 2021;Savary et al 2021;Ertl et al in prep. ;Etherington et al 2022;Gu et al 2022;Schmidt et al in prep. ), which typically performs better than neural networks but has significantly longer run time of hours to days.…”

Section: Network Results and Performancementioning

confidence: 96%

HOLISMOKES -- IX. Neural network inference of strong-lens parameters and uncertainties from ground-based images

Schuldt¹,

Cañameras²,

Shu³

et al. 2022

Preprint

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Modeling of strong gravitational lenses is a necessity for further applications in astrophysics and cosmology. Especially with the large number of detections in current and upcoming surveys such as the Rubin Legacy Survey of Space and Time (LSST), it is timely to investigate in automated and fast analysis techniques beyond the traditional and time consuming Markov chain Monte Carlo sampling methods. Building upon our (simple) convolutional neural network (CNN) presented in Schuldt et al. (2021b), we present here another CNN, specifically a residual neural network (ResNet), that predicts the five mass parameters of a Singular Isothermal Ellipsoid (SIE) profile (lens center x and y, ellipticity e x and e y , Einstein radius θ E ) and the external shear (γ ext,1 , γ ext,2 ) from ground-based imaging data. In contrast to our CNN, this ResNet further predicts a 1σ uncertainty for each parameter. To train our network, we use our improved pipeline from Schuldt et al. (2021b) to simulate lens images using real images of galaxies from the Hyper Suprime-Cam Survey (HSC) and from the Hubble Ultra Deep Field as lens galaxies and background sources, respectively. We find overall very good recoveries for the SIE parameters, especially for the lens center in comparison to our previous CNN, while significant differences remain in predicting the external shear. From our multiple tests, most likely the low ground-based image resolution is the limiting factor for predicting the external shear. Given the run time of milli-seconds per system, our network is perfectly suited to predict the next appearing image and time delays of lensed transients in time. Therefore, we also present the performance of the network on these quantities in comparison to our simulations although the achieved precision allows only a first-order estimate of time-delays on real lens systems and needs further refinement through follow-up modeling. Nonetheless, our ResNet is able to predict the SIE and shear parameter values in fractions of a second on a single CPU such that we are able to process efficiently the huge amount of expected galaxy-scale lenses in the near future.

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Section: Network Results and Performancementioning

confidence: 96%

HOLISMOKES -- IX. Neural network inference of strong-lens parameters and uncertainties from ground-based images

Schuldt¹,

Cañameras²,

Shu³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…Recent works using PyAutoLens include modeling strong lenses simulated using stellar dynamics models (Cao et al 2021) and via a cosmological simulation He et al (2022), an automated analysis of 59 lenses (Etherington et al 2022) and studies of dark matter substructure (He et al 2020;Amorisco et al 2022).…”

Section: Aligned Elliptical Componentsmentioning

confidence: 99%

“…Identical prior passing is used in the Source and Light pipelines as in Etherington et al (2022) and we also use the likelihood cap described in this work to infer errors on lens Table D3. The inferred model parameters of the power-law (PL) and broken power-law (BPL) total mass models fitted to the F390W image in the Mass pipeline.…”

Section: Data Availabilitymentioning

confidence: 99%

Abell 1201: detection of an ultramassive black hole in a strong gravitational lens

Nightingale

Smith

et al. 2023

Monthly Notices of the Royal Astronomical Society

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Supermassive black holes (SMBHs) are a key catalyst of galaxy formation and evolution, leading to an observed correlation between SMBH mass MBH and host galaxy velocity dispersion σe. Outside the local Universe, measurements of MBH are usually only possible for SMBHs in an active state: limiting sample size and introducing selection biases. Gravitational lensing makes it possible to measure the mass of non-active SMBHs. We present models of the $z$ = 0.169 galaxy-scale strong lens Abell 1201. A cD galaxy in a galaxy cluster, it has sufficient ‘external shear’ that a magnified image of a $z$ = 0.451 background galaxy is projected just ∼1 kpc from the galaxy centre. Using multiband Hubble Space Telescope imaging and the lens modelling software PYAUTOLENS, we reconstruct the distribution of mass along this line of sight. Bayesian model comparison favours a point mass with MBH = 3.27 ± 2.12 × 1010 M⊙ (3σ confidence limit); an ultramassive black hole. One model gives a comparable Bayesian evidence without an SMBH; however, we argue this model is nonphysical given its base assumptions. This model still provides an upper limit of MBH ≤ 5.3 × 1010 M⊙, because an SMBH above this mass deforms the lensed image ∼1 kpc from Abell 1201’s centre. This builds on previous work using central images to place upper limits on MBH, but is the first to also place a lower limit and without a central image being observed. The success of this method suggests that surveys during the next decade could measure thousands more SMBH masses, and any redshift evolution of the MBH−σe relation. Results are available at https://github.com/Jammy2211/autolens_abell_1201.

show abstract

“…Recent works using PyAutoLens include modeling strong lenses simulated using stellar dynamics models (Cao et al 2021) and via a cosmological simulation He et al (2023), an automated analysis of 59 lenses (Etherington et al 2022a; and studies of dark matter substructure (He et al 2022b;a;Amorisco et al 2022).…”

Section: Aligned Elliptical Componentsmentioning

confidence: 99%

Abell 1201: Detection of an Ultramassive Black Hole in a Strong Gravitational Lens

Nightingale,

Smith,

et al. 2023

Preprint

View full text Add to dashboard Cite

Supermassive black holes (SMBHs) are a key catalyst of galaxy formation and evolution, leading to an observed correlation between SMBH mass M BH and host galaxy velocity dispersion σ e . Outside the local Universe, measurements of M BH are usually only possible for SMBHs in an active state: limiting sample size and introducing selection biases. Gravitational lensing makes it possible to measure the mass of non-active SMBHs. We present models of the z = 0.169 galaxy-scale strong lens Abell 1201. A cD galaxy in a galaxy cluster, it has sufficient 'external shear' that a magnified image of a z = 0.451 background galaxy is projected just ∼ 1 kpc from the galaxy centre. Using multi-band Hubble Space Telescope imaging and the lens modeling software PyAutoLens we reconstruct the distribution of mass along this line of sight. Bayesian model comparison favours a point mass with M BH = 3.27 ± 2.12 × 10 10 M (3σ confidence limit); an ultramassive black hole. One model gives a comparable Bayesian evidence without a SMBH, however we argue this model is nonphysical given its base assumptions. This model still provides an upper limit of M BH 5.3×10 10 M , because a SMBH above this mass deforms the lensed image ∼ 1 kpc from Abell 1201's centre. This builds on previous work using central images to place upper limits on M BH , but is the first to also place a lower limit and without a central image being observed. The success of this method suggests that surveys during the next decade could measure thousands more SMBH masses, and any redshift evolution of the M BH -σ e relation. Results are available at https://github.com/Jammy2211/autolens_abell_1201.

show abstract

Automated galaxy-galaxy strong lens modelling: no lens left behind

Cited by 4 publications

References 52 publications

HOLISMOKES -- IX. Neural network inference of strong-lens parameters and uncertainties from ground-based images

HOLISMOKES -- IX. Neural network inference of strong-lens parameters and uncertainties from ground-based images

Abell 1201: detection of an ultramassive black hole in a strong gravitational lens

Abell 1201: Detection of an Ultramassive Black Hole in a Strong Gravitational Lens

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