lenstronomy II: A gravitational lensing software ecosystem

Birrer, Simon; Shajib, Anowar J.; Gilman, Daniel; Galan, A.; Aalbers, J.; Millon, M.; Morgan, R.; Pagano, G.; Park, Ji Won; Teodori, Luca; Tessore, Nicolas; Ueland, Madison; Vyvere, Laura Van de; Wagner-Carena, Sebastian; Wempe, Ewoud; Yang, Lilan; Ding, Xuheng; Schmidt, Thomas; Sluse, Dominique; Zhang, Ming; Amara, Adam

doi:10.21105/joss.03283

Cited by 105 publications

(62 citation statements)

References 56 publications

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“…The software relies on the python standard library and the opensource libraries including numpy [15], scipy [16], astropy [17,18], matplotlib [19] and photutils [20]. In particular, the package lenstronomy [3,4] is the work horse that performs the fitting process. galight is compatible with both python 2 and python 3.…”

Section: Package Overviewmentioning

confidence: 99%

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Galaxy shapes of Light (GaLight): a 2D modeling of galaxy images

Ding¹,

Birrer²,

Treu³

et al. 2021

Preprint

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galight is a Python-based open-source package that can be used to perform two-dimensional model fitting of optical and nearinfrared images to characterize the light distribution of galaxies with components including a disk, bulge, bar, and quasar. The decomposition of stellar components has been demonstrated in published studies of inactive galaxies and quasar host galaxies observed by the Hubble Space Telescope and Subaru's Hyper Suprime-Cam. galight utilizes the image modeling capabilities of lenstronomy while redesigning the user interface for the analysis of large samples of extragalactic sources. The package is user-friendly with some automatic features such as determining the cutout size of the modeling frame, searching for PSF-stars in field-of-view, estimating the noise map of the data, identifying all the objects to set the initial model and associated parameters to fit them simultaneously. These features minimize the manpower and allow the automatic fitting tasks. The software is distributed under the MIT license. The source code, installation guidelines, and example notebooks code can be found at https://galight. readthedocs.io/en/latest/.

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Section: Package Overviewmentioning

confidence: 99%

“…For galight version 0.1.0 source python software package to model galaxy images including those hosting AGN. galight uses routines available through lenstronomy [3,4] that are further described below. galight adds a number of features to process large numbers of images from survey data.…”

Section: Introductionmentioning

confidence: 99%

Galaxy shapes of Light (GaLight): a 2D modeling of galaxy images

Ding¹,

Birrer²,

Treu³

et al. 2021

Preprint

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“…To obtain the intrinsic size of the lensed galaxies, we employ the python softwares Lenstruction 4 / Lenstronomy 5 which adopt a forward modeling technique to reconstruct the source brightness distribution correcting the lensing and blurring effects simultaneously. Lenstruction (Yang et al 2020) is a wrapper of the strong lensing software Lenstronomy (Birrer et al 2015;Birrer & Amara 2018;Birrer et al 2021) that provides the interface to handle the cluster-scale source reconstruction and local lens model perturbation .…”

Section: Intrinsic Size Measurementmentioning

confidence: 99%

The size-luminosity relation of lensed galaxies at $z=6-9$ in the Hubble Frontier Fields

Yang,

Leethochawalit,

Treu

et al. 2022

Preprint

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We measure the size-luminosity relation of photometrically-selected galaxies within the redshift range 𝑧 ∼ 6 − 9, using galaxies lensed by six foreground Hubble Frontier Fields (HFF) clusters. The power afforded by strong gravitational lensing allows us to observe fainter and smaller galaxies than in blank fields. We select our sample of galaxies and obtain their properties, e.g., redshift, magnitude, from the photometrically-derived ASTRODEEP catalogues. The intrinsic size is measured with the Lenstruction software, and completeness maps are created as a function of size and luminosity via the GLACiAR2 software. We perform a Bayesian analysis to estimate the intrinsic and incompleteness-corrected size-luminosity distribution, with parameterization 𝑟 𝑒 ∝ 𝐿 𝛽 . We find slopes of 𝛽 ∼ 0.48 ± 0.08 at 𝑧 ∼ 6 − 7 and 𝛽 ∼ 0.68 ± 0.14 at 𝑧 ∼ 8.5, adopting the Bradac lens model. The slope derived by lensed galaxies is steeper than that obtained in blank fields and is consistent with other independent determinations of the size-luminosity relation from the HFF dataset. We also investigate the systematic uncertainties correlated with the choice of lens models, finding that the slopes of size-luminosity relations derived from different models are consistent with each other, i.e. the modeling errors are not a significant source of uncertainty in the size-luminosity relation.

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“…We simulated images of astronomical strong lensing systems with the open-source software package, deeplenstronomy (Morgan et al 2021b), which is built around lenstronomy (Birrer & Amara 2018;Birrer et al 2021b), a widely used package that performs gravitational lensing calculations, modeling, and simulations in a variety of contexts. deeplenstronomy provides additional features that are important for the era of largescale surveys and deep learning studies of strong lenses -e.g., image and SN injection, probability distribution sampling, and realistic observing conditions.…”

Section: Data Simulationmentioning

confidence: 99%

DeepZipper: A Novel Deep Learning Architecture for Lensed Supernovae Identification

Morgan,

Nord,

Bechtol

et al. 2021

Preprint

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Large-scale astronomical surveys have the potential to capture data on large numbers of strongly gravitationally lensed supernovae (LSNe). To facilitate timely analysis and spectroscopic follow-up before the supernova fades, an LSN needs to be identified soon after it begins. To quickly identify LSNe in optical survey datasets, we designed ZipperNet, a multi-branch deep neural network that combines convolutional layers (traditionally used for images) with long short-term memory (LSTM) layers (traditionally used for time series). We tested ZipperNet on the task of classifying objects from four categories -no lens, galaxy-galaxy lens, lensed type Ia supernova, lensed core-collapse supernova -within high-fidelity simulations of three cosmic survey data sets -the Dark Energy Survey (DES), Rubin Observatory's Legacy Survey of Space and Time (LSST), and a Dark Energy Spectroscopic Instrument (DESI) imaging survey. Among our results, we find that for the LSST-like dataset, Zip-perNet classifies LSNe with a receiver operating characteristic area under the curve of 0.97, predicts the spectroscopic type of the lensed supernovae with 79% accuracy, and demonstrates similarly high performance for LSNe 1-2 epochs after first detection. We anticipate that a model like ZipperNet, which simultaneously incorporates spatial and temporal information, can play a significant role in the rapid identification of lensed transient systems in cosmic survey experiments.

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lenstronomy II: A gravitational lensing software ecosystem

Cited by 105 publications

References 56 publications

Galaxy shapes of Light (GaLight): a 2D modeling of galaxy images

Galaxy shapes of Light (GaLight): a 2D modeling of galaxy images

The size-luminosity relation of lensed galaxies at $z=6-9$ in the Hubble Frontier Fields

DeepZipper: A Novel Deep Learning Architecture for Lensed Supernovae Identification

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