In this section you can find the concept papers lenstronomy is based on the list of science publications that made use of lenstronomy. Please let the developers know when you publish a paper that made use of lenstronomy. We are happy to include your publication in this list.
- lenstronomy: Multi-purpose gravitational lens modelling software package; Birrer & Amara 2018
- This is the lenstronomy software paper. Please cite this paper whenever you make use of lenstronomy. The paper gives a design overview and gives some use cases.
- Gravitational Lens Modeling with Basis Sets; Birrer et al. 2015
- This is the method paper lenstronomy is primary based on. Please cite this paper whenever you publish results with lenstronomy by using Shapelet basis sets and/or the PSO and MCMC chain.
- A versatile tool for cluster lensing source reconstruction. I. methodology and illustration on sources in the Hubble Frontier Field Cluster MACS J0717.5+3745; Yang et al. 2020a
- reconstructing the intrinsic size-mass relation of strongly lensed sources in clusters
- SLITronomy: towards a fully wavelet-based strong lensing inversion technique; Galan et al. 2020
- This is the method paper presenting SLITromomy, an improved version of the SLIT algorithm fully implemented and compatible with lenstronomy.
- deeplenstronomy: A dataset simulation package for strong gravitational lensing; Morgan et al. 2021
- Software to simulating large datasets for applying deep learning to strong gravitational lensing.
- The mass-sheet degeneracy and time-delay cosmography: analysis of the strong lens RXJ1131-1231; Birrer et al. 2016
- This paper performs a cosmographic analysis and applies the Shapelet basis set scaling to marginalize over a major lensing degeneracy.
- H0LiCOW - IX. Cosmographic analysis of the doubly imaged quasar SDSS 1206+4332 and a new measurement of the Hubble constant; Birrer et al. 2019
- This paper performs a cosmographic analysis with power-law and composite models and covers a range in complexity in the source reconstruction
- Astrometric requirements for strong lensing time-delay cosmography; Birrer & Treu 2019
- Derives requirements on how well the image positions of time-variable sources has to be known to perform a time-delay cosmographic measurement
- H0LiCOW XIII. A 2.4% measurement of H0 from lensed quasars: 5.3σ tension between early and late-Universe probes; Wong et al. 2019
- Joint analysis of the six H0LiCOW lenses including the lenstronomy analysis of J1206
- STRIDES: A 3.9 per cent measurement of the Hubble constant from the strongly lensed system DES J0408-5354; Shajib et al. 2019
- most precise single lensing constraint on the Hubble constant. This analysis includes two source planes and three lensing planes
- TDCOSMO. I. An exploration of systematic uncertainties in the inference of H0 from time-delay cosmography Millon et al. 2020
- mock lenses to test accuracy on the recovered H0 value
- Lens modelling of the strongly lensed Type Ia supernova iPTF16geu Moertsell et al. 2020
- Modeling of a lensed supernova to measure the Hubble constant
- The impact of line-of-sight structures on measuring H0 with strong lensing time-delays Li, Becker and Dye 2020
- Point source position and time-delay modeling of quads
- TDCOSMO III: Dark matter substructure meets dark energy -- the effects of (sub)halos on strong-lensing measurements of H0 Gilman, Birrer and Treu 2020
- Full line-of-sight halo rendering and time-delay analysis on mock images
- TDCOSMO IV: Hierarchical time-delay cosmography -- joint inference of the Hubble constant and galaxy density profiles Birrer et al. 2020
- lenstronomy.Galkin for kinematics calculation that folds in the hierarchical analysis
- TDCOSMO V: strategies for precise and accurate measurements of the Hubble constant with strong lensing Birrer & Treu 2020
- lenstronomy.Galkin for kinematics calculation that folds in the hierarchical analysis for a forecast for future Hubble constant constraints
- Large-Scale Gravitational Lens Modeling with Bayesian Neural Networks for Accurate and Precise Inference of the Hubble Constant Park et al. 2020
- BBN lens model inference using lenstronomy through `baobab <https://github.com/jiwoncpark/baobab>`_ for training set generation.
- Improved time-delay lens modelling and H0 inference with transient sources Ding et al. 2021
- Simulations and models with and without lensed point sources to perform a time-delay cosmography analysis.
- Gravitational lensing H0 tension from ultralight axion galactic cores Blum & Teodori 2021
- Investigating the detectability of a cored component with mock imaging modeling and comparison of kinematic modeling.
- Lensing substructure quantification in RXJ1131-1231: a 2 keV lower bound on dark matter thermal relic mass; Birrer et al. 2017b
- This paper quantifies the substructure content of a lens by a sub-clump scanning procedure and the application of Approximate Bayesian Computing.
- Probing the nature of dark matter by forward modelling flux ratios in strong gravitational lenses; Gilman et al. 2018
- Probing dark matter structure down to 10**7 solar masses: flux ratio statistics in gravitational lenses with line-of-sight haloes; Gilman et al. 2019a
- Double dark matter vision: twice the number of compact-source lenses with narrow-line lensing and the WFC3 Grism; Nierenberg et al. 2019
- Warm dark matter chills out: constraints on the halo mass function and the free-streaming length of dark matter with 8 quadruple-image strong gravitational lenses; Gilman et al. 2019b
- Constraints on the mass-concentration relation of cold dark matter halos with 11 strong gravitational lenses; Gilman et al. 2019c
- Circumventing Lens Modeling to Detect Dark Matter Substructure in Strong Lens Images with Convolutional Neural Networks; Diaz Rivero & Dvorkin
- Dark Matter Subhalos, Strong Lensing and Machine Learning; Varma, Fairbairn, Figueroa
- Quantifying the Line-of-Sight Halo Contribution to the Dark Matter Convergence Power Spectrum from Strong Gravitational Lenses; Sengul et al. 2020
- Detecting Subhalos in Strong Gravitational Lens Images with Image Segmentation; Ostdiek et al. 2020a
- Extracting the Subhalo Mass Function from Strong Lens Images with Image Segmentation; Ostdiek et al. 2020b
- Strong lensing signatures of self-interacting dark matter in low-mass halos; Gilman et al. 2021
- Massive elliptical galaxies at z∼0.2 are well described by stars and a Navarro-Frenk-White dark matter halo; Shajib et al. 2020a
- Automatized modeling of 23 SLACS lenses with dolphin, a lenstronomy wrapper
- High-resolution imaging follow-up of doubly imaged quasars; Shajib et al. 2020b
- Modeling of doubly lensed quasars from Keck Adaptive Optics data
- The evolution of the size-mass relation at z=1-3 derived from the complete Hubble Frontier Fields data set; Yang et al. 2020b
- reconstructing the intrinsic size-mass relation of strongly lensed sources in clusters
- Is every strong lens model unhappy in its own way? Uniform modelling of a sample of 12 quadruply+ imaged quasars; Shajib et al. 2018
- This work presents a uniform modelling framework to model 13 quadruply lensed quasars in three HST bands.
- Hierarchical Inference With Bayesian Neural Networks: An Application to Strong Gravitational Lensing; Wagner-Carena et al. 2020
- This work conducts hierarchical inference of strongly-lensed systems with Bayesian neural networks.
- The mass relations between supermassive black holes and their host galaxies at 1<z<2 with HST-WFC3; Ding et al. 2019
- Quasar host galaxy decomposition at high redshift on HST imaging and marginalization over PSF uncertainties.
- Testing the Evolution of the Correlations between Supermassive Black Holes and their Host Galaxies using Eight Strongly Lensed Quasars; Ding et al. 2020
- Quasar host galaxy decomposition with lensed quasars.
- A local baseline of the black hole mass scaling relations for active galaxies. IV. Correlations between MBH and host galaxy σ, stellar mass, and luminosity; Bennert et al. 2021
- Detailed measurement of galaxy morphology, decomposing in spheroid, disk and bar, and central AGN
- The Sizes of Quasar Host Galaxies with the Hyper Suprime-Cam Subaru Strategic Program; Li et al. 2021
- Quasar-host decomposition of 5000 SDSS quasars
- lensingGW: a Python package for lensing of gravitational waves; Pagano et al. 2020
- A Python package designed to handle both strong and microlensing of compact binaries and the related gravitational-wave signals.
- Localizing merging black holes with sub-arcsecond precision using gravitational-wave lensing; Hannuksela et al. 2020
- solving the lens equation with lenstronomy using lensingGW
- Lensing magnification: gravitational wave from coalescing stellar-mass binary black holes; Shan & Hu 2020
- lensing magnificatoin calculations
- Identifying Type-II Strongly-Lensed Gravitational-Wave Images in Third-Generation Gravitational-Wave Detectors; Y. Wang et al. 2021
- solving the lens equation
- Line-of-sight effects in strong lensing: putting theory into practice; Birrer et al. 2017a
- This paper formulates an effective parameterization of line-of-sight structure for strong gravitational lens modelling and applies this technique to an Einstein ring in the COSMOS field
- Cosmic Shear with Einstein Rings; Birrer et al. 2018a
- Forecast paper to measure cosmic shear with Einstein ring lenses. The forecast is made based on lenstronomy simulations.
- Unified lensing and kinematic analysis for any elliptical mass profile; Shajib 2019
- Provides a methodology to generalize the multi-Gaussian expansion to general elliptical mass and light profiles
- Gravitational lensing formalism in a curved arc basis: A continuous description of observables and degeneracies from the weak to the strong lensing regime; Birrer 2021
- Lensing formalism with curved arc distortion formalism. Link to code repository `here <https://github.com/sibirrer/curved_arcs>`_.
- The LSST DESC DC2 Simulated Sky Survey; LSST Dark Energy Science Collaboration et al. 2020
- Strong lensing simulations produced by SLSprinkler utilizing lenstronomy functionalities
- The impact of mass map truncation on strong lensing simulations; Van de Vyvere et al. 2020
- Uses numerical integration to compute lensing quantities from projected mass maps from simulations.
- Combining strong and weak lensingestimates in the Cosmos field; Kuhn et al. 2020
- inferring cosmic shear with three strong lenses in the COSMOS field
- On machine learning search for gravitational lenses; Khachatryan 2021
- simulating training sets for lens searches
- Predicting future astronomical events using deep learning; Singh et al.
- simulating strongly lensed galaxy merger pairs in time sequence