Gravity.jl: fast and accurate gravitational lens modeling in Julia

Marco Lombardi

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Gravity.jl: fast and accurate gravitational lens modeling in Julia

Marco Lombardi

Instrumentation and Methods for Astrophysics · Submitted 21 Jun 2024

abstract compiled · 2026-09-23 · no language-model review

The authors' main claim We present Gravity.jl, a new software for the modeling of gravitational lens systems.

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The paper's own sentences, arranged. Each label is drawn from a closed vocabulary; the number beside it is the probability the classifier gave that label. Numbers in the tables are the authors', checked by code. No sentence in this record was written by a model.

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Compiled by classifier-1.13.0 via TypeSafe API, question bank abstract-p1-20260923, 2026-09-23: 1 request, 5,448 input tokens.

What the abstract itself states, as probabilities: claims first 0.12 · states limitation 0.83 · human participants 0.02 · controlled comparison 0.22 · randomized assignment 0.01 · states quantitative result 0.05 · compares with prior results 0.95 · mentions code or data availability 0.18.

The abstract, sentence by sentence

arXiv listing abstract v2. Results and the main claim are underlined; show every role.

We present Gravity.jl, a new software for the modeling of gravitational lens systems. Gravity.jl is written in the Julia programming language, and is designed to be fast, accurate, and flexible. It can be used to model gravitational lens systems composed of multiple lensing planes, and to perform Bayesian inference on the lens model parameters. In this paper we present the theoretical and statistical ideas behind the code, and we describe its main features. In this first paper of the series, we focus on the modeling of point-like and small extended sources, for which we can linearize the lens equation. We show a practical use of Gravity.jl on a galaxy-scale lens, and we compare the results with those obtained with other codes. We also show how Gravity.jl can be used to perform Bayesian inference on cosmological parameters.

background contribution method result interpretation main claim

What the classifier reports for each sentence. The number is the probability it gave the label.
#	sentence	role	the authors' own work	states the main conclusion	strength
s0	We present Gravity.jl, a new software for the modeling of gravitational lens systems.	contribution 1.00	0.79	0.16	asserted 1.00
s1	Gravity.jl is written in the Julia programming language, and is designed to be fast, accurate, and flexible.	contribution 0.64	0.52	0.08	asserted 1.00
s2	It can be used to model gravitational lens systems composed of multiple lensing planes, and to perform Bayesian inference on the lens model parameters.	contribution 0.53	0.37	0.08	asserted 0.80
s3	In this paper we present the theoretical and statistical ideas behind the code, and we describe its main features.	contribution 0.94	0.75	0.05	asserted 0.94
s4	In this first paper of the series, we focus on the modeling of point-like and small extended sources, for which we can linearize the lens equation.	limitation 0.75	0.49	0.06	asserted 0.96
s5	We show a practical use of Gravity.jl on a galaxy-scale lens, and we compare the results with those obtained with other codes.	contribution 0.75	0.95	0.16	asserted 1.00
s6	We also show how Gravity.jl can be used to perform Bayesian inference on cosmological parameters.	contribution 0.91	0.91	0.15	asserted 0.99

What kind of paper the classifier reports this is: method or tool 1.00 · evidence from simulation or computation 0.91.

What was examined, and what was not

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arXiv listing abstract v2

not examined

full text
figures and tables
references and citing sentences
relations to other papers

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P abstract

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S sections
T body sentences
C citations
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Every quoted sentence on this record is the authors'. Every label is from Pith's closed vocabulary, with the probability shown beside it. Every number in the measurements table is the authors', checked by code. Record as JSON

The paper

Original abstract

We present Gravity.jl, a new software for the modeling of gravitational lens systems. Gravity.jl is written in the Julia programming language, and is designed to be fast, accurate, and flexible. It can be used to model gravitational lens systems composed of multiple lensing planes, and to perform Bayesian inference on the lens model parameters. In this paper we present the theoretical and statistical ideas behind the code, and we describe its main features. In this first paper of the series, we focus on the modeling of point-like and small extended sources, for which we can linearize the lens equation. We show a practical use of Gravity.jl on a galaxy-scale lens, and we compare the results with those obtained with other codes. We also show how Gravity.jl can be used to perform Bayesian inference on cosmological parameters.

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References

Forward citations

Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

High-z galaxies with the JWST and the ELT: Toward Ever-finer Detail
astro-ph.GA 2026-09 unverdicted novelty 1.0 of 10

ELT-class adaptive optics will resolve sub-100-parsec scales at any redshift, extending JWST-era galaxy studies from star-forming clumps down to star-cluster scales.
Cosmology with strong gravitational lenses
astro-ph.CO 2026-10 accept

Strong gravitational lensing provides a purely gravitational probe that measures H0 at 3% precision from 11 lensed quasars and hints at dark matter self-interaction from dense subhalos.

How the literature uses it

2 arXiv papers cite this in 3 sentences

Each citing sentence is taken from the citing paper's LaTeX source and typed by Pith's classifier. A label appears at probability 0.8 or more (0.95 for support), a contest at 0.7 or more; the quoted words are the citing authors'. How each label was checked · JSON

Papers citing it · 1 sentence without a confident label

Uses its method or tool 1 paper

To this aim, we used a newly developed lens modelling software, [ref] , and independently validated the results with the public software [ref] .

A new quasar strongly-lensed candidate by the galaxy cluster WHJ0400-27 with a $18''$ image-separation · Strong lensing modelling · 1.00

What it cites · 2 sentences without a confident label

Uses the method or tool of 2 papers

Currently, the code uses forward-mode automatic differentiation implemented in the package [ref] .

Forward-Mode Automatic Differentiation in Julia · Automatic differentiation · 1.00
The library used in , [ref] , also implements the No-U-Turn Sampler (NUTS) approach of [ref] .

The No-U-Turn Sampler: Adaptively Setting Path Lengths in Hamiltonian Monte Carlo · Sampling techniques · 0.98

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