KMS National Astronomical Observatories, CAS
| Strong lens modelling: comparing and combining Bayesian neural networks and parametric profile fitting | |
| Pearson, James1; Maresca, Jacob1; Li, Nan1,2; Dye, Simon1 | |
| 2021-08-01 | |
| Source Publication | MONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY
 |
| ISSN | 0035-8711 |
| Volume | 505Issue:3Pages:4362-4382 |
| Abstract | The vast quantity of strong galaxy-galaxy gravitational lenses expected by future large-scale surveys necessitates the development of automated methods to efficiently model their mass profiles. For this purpose, we train an approximate Bayesian convolutional neural network (CNN) to predict mass profile parameters and associated uncertainties, and compare its accuracy to that of conventional parametric modelling for a range of increasingly complex lensing systems. These include standard smooth parametric density profiles, hydrodynamical EAGLE galaxies, and the inclusion of foreground mass structures, combined with parametric sources and sources extracted from the Hubble Ultra Deep Field. In addition, we also present a method for combining the CNN with traditional parametric density profile fitting in an automated fashion, where the CNN provides initial priors on the latter's parameters. On average, the CNN achieved errors 19 +/- 22 percent lower than the traditional method's blind modelling. The combination method instead achieved 27 +/- 11 percent lower errors over the blind modelling, reduced further to 37 +/- 11 percent when the priors also incorporated the CNN-predicted uncertainties, with errors also 17 +/- 21 percent lower than the CNN by itself. While the CNN is undoubtedly the fastest modelling method, the combination of the two increases the speed of conventional fitting alone by factors of 1.73 and 1.19 with and without CNN-predicted uncertainties, respectively. This, combined with greatly improved accuracy, highlights the benefits one can obtain through combining neural networks with conventional techniques in order to achieve an efficient automated modelling approach. |
| Keyword | gravitational lensing: strong galaxies: structure |
| Funding Organization | UK Science and Technology Facilities Council (STFC) ; UK Science and Technology Facilities Council (STFC) ; UK STFC Rutherford Fellowship ; UK STFC Rutherford Fellowship ; UK Science and Technology Facilities Council (STFC) ; UK Science and Technology Facilities Council (STFC) ; UK STFC Rutherford Fellowship ; UK STFC Rutherford Fellowship ; UK Science and Technology Facilities Council (STFC) ; UK Science and Technology Facilities Council (STFC) ; UK STFC Rutherford Fellowship ; UK STFC Rutherford Fellowship ; UK Science and Technology Facilities Council (STFC) ; UK Science and Technology Facilities Council (STFC) ; UK STFC Rutherford Fellowship ; UK STFC Rutherford Fellowship |
| DOI | 10.1093/mnras/stab1547 |
| WOS Keyword | STRONG GRAVITATIONAL LENSES ; SPECTROSCOPICALLY SELECTED SAMPLE ; STAR-FORMATION ; TIME DELAYS ; HUBBLE CONSTANT ; GALAXY ; MASS ; INFERENCE ; H-0 ; SUBSTRUCTURE |
| Language | 英语 |
| Funding Project | UK Science and Technology Facilities Council (STFC) ; UK STFC Rutherford Fellowship |
| Funding Organization | UK Science and Technology Facilities Council (STFC) ; UK Science and Technology Facilities Council (STFC) ; UK STFC Rutherford Fellowship ; UK STFC Rutherford Fellowship ; UK Science and Technology Facilities Council (STFC) ; UK Science and Technology Facilities Council (STFC) ; UK STFC Rutherford Fellowship ; UK STFC Rutherford Fellowship ; UK Science and Technology Facilities Council (STFC) ; UK Science and Technology Facilities Council (STFC) ; UK STFC Rutherford Fellowship ; UK STFC Rutherford Fellowship ; UK Science and Technology Facilities Council (STFC) ; UK Science and Technology Facilities Council (STFC) ; UK STFC Rutherford Fellowship ; UK STFC Rutherford Fellowship |
| WOS Research Area | Astronomy & Astrophysics |
| WOS Subject | Astronomy & Astrophysics |
| WOS ID | WOS:000671481700084 |
| Publisher | OXFORD UNIV PRESS |
| Citation statistics | |
| Document Type | 期刊论文 |
| Identifier | http://ir.bao.ac.cn/handle/114a11/76219 |
| Collection | 中国科学院国家天文台 |
| Corresponding Author | Pearson, James |
| Affiliation | 1.Univ Nottingham, Sch Phys & Astron, Univ Pk, Nottingham NG7 2RD, England 2.Natl Astron Observ China, 20A Datun Rd, Beijing, Peoples R China |
| Recommended Citation GB/T 7714 | Pearson, James,Maresca, Jacob,Li, Nan,et al. Strong lens modelling: comparing and combining Bayesian neural networks and parametric profile fitting[J]. MONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY,2021,505(3):4362-4382. |
| APA | Pearson, James,Maresca, Jacob,Li, Nan,&Dye, Simon.(2021).Strong lens modelling: comparing and combining Bayesian neural networks and parametric profile fitting.MONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY,505(3),4362-4382. |
| MLA | Pearson, James,et al."Strong lens modelling: comparing and combining Bayesian neural networks and parametric profile fitting".MONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY 505.3(2021):4362-4382. |
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