GPU-Accelerated Gravitational Lensing & Dynamical (GLaD) Modeling for Cosmology and Galaxies
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866908454842531840 |
|---|---|
| author | Wang, Han Suyu, Sherry H. Galan, Aymeric Halkola, Aleksi Cappellari, Michele Shajib, Anowar J. Cernetic, Miha |
| author_facet | Wang, Han Suyu, Sherry H. Galan, Aymeric Halkola, Aleksi Cappellari, Michele Shajib, Anowar J. Cernetic, Miha |
| contents | Time-delay distance measurements from strongly lensed quasars provide a robust, independent method for determining the Hubble constant ($H_0$). This approach cross-checks $H_0$ estimates from the distance ladder in the late universe and the cosmic microwave background in the early universe. However, the mass-sheet degeneracy in lensing models introduces systematic uncertainty, limiting precision. Dynamical modeling complements strong lensing by constraining the mass distribution with independent observational data. We develop a methodology and software framework for joint modeling of stellar kinematics and lensing data. Using simulated data for the lensed quasar RXJ1131$-$1131, we demonstrate that high-quality kinematic data can achieve $\sim$4% precision on $H_0$. Through extensive modeling, we examine the impact of the presence of a supermassive black hole in the lens galaxy and potential systematic biases in kinematic data on $H_0$ measurements. Our results show that imposing priors on black hole mass and orbital anisotropy, or excluding central kinematic bins, mitigates biases in $H_0$ estimates. By testing on mock kinematic data with systematic biases, we highlight the need for sub-percent control of kinematic systematics, which is achievable with current technology. Additionally, we leverage GPU parallelization to accelerate Bayesian inference, reducing a previously month-long process by an order of magnitude. This pipeline offers significant potential for advancing cosmological and galaxy evolution studies with large datasets. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_01302 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | GPU-Accelerated Gravitational Lensing & Dynamical (GLaD) Modeling for Cosmology and Galaxies Wang, Han Suyu, Sherry H. Galan, Aymeric Halkola, Aleksi Cappellari, Michele Shajib, Anowar J. Cernetic, Miha Cosmology and Nongalactic Astrophysics Time-delay distance measurements from strongly lensed quasars provide a robust, independent method for determining the Hubble constant ($H_0$). This approach cross-checks $H_0$ estimates from the distance ladder in the late universe and the cosmic microwave background in the early universe. However, the mass-sheet degeneracy in lensing models introduces systematic uncertainty, limiting precision. Dynamical modeling complements strong lensing by constraining the mass distribution with independent observational data. We develop a methodology and software framework for joint modeling of stellar kinematics and lensing data. Using simulated data for the lensed quasar RXJ1131$-$1131, we demonstrate that high-quality kinematic data can achieve $\sim$4% precision on $H_0$. Through extensive modeling, we examine the impact of the presence of a supermassive black hole in the lens galaxy and potential systematic biases in kinematic data on $H_0$ measurements. Our results show that imposing priors on black hole mass and orbital anisotropy, or excluding central kinematic bins, mitigates biases in $H_0$ estimates. By testing on mock kinematic data with systematic biases, we highlight the need for sub-percent control of kinematic systematics, which is achievable with current technology. Additionally, we leverage GPU parallelization to accelerate Bayesian inference, reducing a previously month-long process by an order of magnitude. This pipeline offers significant potential for advancing cosmological and galaxy evolution studies with large datasets. |
| title | GPU-Accelerated Gravitational Lensing & Dynamical (GLaD) Modeling for Cosmology and Galaxies |
| topic | Cosmology and Nongalactic Astrophysics |
| url | https://arxiv.org/abs/2504.01302 |