Strong-lensing source reconstruction with variationally optimised Gaussian processes Article Swipe
Konstantin Karchev
,
Adam Coogan
,
Christoph Weniger
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2105.09465
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2105.09465
Strong-lensing images provide a wealth of information both about the magnified source and about the dark matter distribution in the lens. Precision analyses of these images can be used to constrain the nature of dark matter. However, this requires high-fidelity image reconstructions and careful treatment of the uncertainties of both lens mass distribution and source light, which are typically difficult to quantify. In anticipation of future high-resolution datasets, in this work we leverage a range of recent developments in machine learning to develop a new Bayesian strong-lensing image analysis pipeline. Its highlights are: (A) a fast, GPU-enabled, end-to-end differentiable strong-lensing image simulator; (B) a new, statistically principled source model based on a computationally highly efficient approximation to Gaussian processes that also takes into account pixellation; and (C) a scalable variational inference framework that enables simultaneously deriving posteriors for tens of thousands of lens and source parameters and optimising hyperparameters via stochastic gradient descent. Besides efficient and accurate parameter estimation and lens model uncertainty quantification, the main aim of the pipeline is the generation of training data for targeted simulation-based inference of dark matter substructure, which we will exploit in a companion paper.
Related Topics
Concepts
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2105.09465
- https://arxiv.org/pdf/2105.09465
- OA Status
- green
- Cited By
- 4
- References
- 3
- Related Works
- 20
- OpenAlex ID
- https://openalex.org/W3161441416
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W3161441416Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.48550/arxiv.2105.09465Digital Object Identifier
- Title
-
Strong-lensing source reconstruction with variationally optimised Gaussian processesWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2021Year of publication
- Publication date
-
2021-05-20Full publication date if available
- Authors
-
Konstantin Karchev, Adam Coogan, Christoph WenigerList of authors in order
- Landing page
-
https://arxiv.org/abs/2105.09465Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2105.09465Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://arxiv.org/pdf/2105.09465Direct OA link when available
- Concepts
-
Computer science, Leverage (statistics), Dark matter, Weak gravitational lensing, Inference, Pipeline (software), Lens (geology), Hyperparameter, Gaussian process, Gaussian, Algorithm, Artificial intelligence, Physics, Optics, Galaxy, Astrophysics, Redshift, Quantum mechanics, Programming languageTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
4Total citation count in OpenAlex
- Citations by year (recent)
-
2022: 2, 2021: 2Per-year citation counts (last 5 years)
- References (count)
-
3Number of works referenced by this work
- Related works (count)
-
20Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W3161441416 |
|---|---|
| doi | https://doi.org/10.48550/arxiv.2105.09465 |
| ids.doi | https://doi.org/10.48550/arxiv.2105.09465 |
| ids.mag | 3161441416 |
| ids.openalex | https://openalex.org/W3161441416 |
| fwci | 0.5644143 |
| type | preprint |
| title | Strong-lensing source reconstruction with variationally optimised Gaussian processes |
| biblio.issue | |
| biblio.volume | |
| biblio.last_page | |
| biblio.first_page | |
| topics[0].id | https://openalex.org/T12814 |
| topics[0].field.id | https://openalex.org/fields/17 |
| topics[0].field.display_name | Computer Science |
| topics[0].score | 0.9998999834060669 |
| topics[0].domain.id | https://openalex.org/domains/3 |
| topics[0].domain.display_name | Physical Sciences |
| topics[0].subfield.id | https://openalex.org/subfields/1702 |
| topics[0].subfield.display_name | Artificial Intelligence |
| topics[0].display_name | Gaussian Processes and Bayesian Inference |
| topics[1].id | https://openalex.org/T13126 |
| topics[1].field.id | https://openalex.org/fields/31 |
| topics[1].field.display_name | Physics and Astronomy |
| topics[1].score | 0.9793000221252441 |
| topics[1].domain.id | https://openalex.org/domains/3 |
| topics[1].domain.display_name | Physical Sciences |
| topics[1].subfield.id | https://openalex.org/subfields/3109 |
| topics[1].subfield.display_name | Statistical and Nonlinear Physics |
| topics[1].display_name | Scientific Research and Discoveries |
| topics[2].id | https://openalex.org/T10848 |
| topics[2].field.id | https://openalex.org/fields/17 |
| topics[2].field.display_name | Computer Science |
| topics[2].score | 0.9659000039100647 |
| topics[2].domain.id | https://openalex.org/domains/3 |
| topics[2].domain.display_name | Physical Sciences |
| topics[2].subfield.id | https://openalex.org/subfields/1703 |
| topics[2].subfield.display_name | Computational Theory and Mathematics |
| topics[2].display_name | Advanced Multi-Objective Optimization Algorithms |
| is_xpac | False |
| apc_list | |
| apc_paid | |
| concepts[0].id | https://openalex.org/C41008148 |
| concepts[0].level | 0 |
| concepts[0].score | 0.6616784334182739 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[0].display_name | Computer science |
| concepts[1].id | https://openalex.org/C153083717 |
| concepts[1].level | 2 |
| concepts[1].score | 0.54905766248703 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q6535263 |
| concepts[1].display_name | Leverage (statistics) |
| concepts[2].id | https://openalex.org/C159249277 |
| concepts[2].level | 2 |
| concepts[2].score | 0.5184807181358337 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q79925 |
| concepts[2].display_name | Dark matter |
| concepts[3].id | https://openalex.org/C190670322 |
| concepts[3].level | 4 |
| concepts[3].score | 0.4740702211856842 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q13073796 |
| concepts[3].display_name | Weak gravitational lensing |
| concepts[4].id | https://openalex.org/C2776214188 |
| concepts[4].level | 2 |
| concepts[4].score | 0.4690573513507843 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q408386 |
| concepts[4].display_name | Inference |
| concepts[5].id | https://openalex.org/C43521106 |
| concepts[5].level | 2 |
| concepts[5].score | 0.4670034646987915 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q2165493 |
| concepts[5].display_name | Pipeline (software) |
| concepts[6].id | https://openalex.org/C15336307 |
| concepts[6].level | 2 |
| concepts[6].score | 0.4577312767505646 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q1766051 |
| concepts[6].display_name | Lens (geology) |
| concepts[7].id | https://openalex.org/C8642999 |
| concepts[7].level | 2 |
| concepts[7].score | 0.43790918588638306 |
| concepts[7].wikidata | https://www.wikidata.org/wiki/Q4171168 |
| concepts[7].display_name | Hyperparameter |
| concepts[8].id | https://openalex.org/C61326573 |
| concepts[8].level | 3 |
| concepts[8].score | 0.4100888967514038 |
| concepts[8].wikidata | https://www.wikidata.org/wiki/Q1496376 |
| concepts[8].display_name | Gaussian process |
| concepts[9].id | https://openalex.org/C163716315 |
| concepts[9].level | 2 |
| concepts[9].score | 0.4021570682525635 |
| concepts[9].wikidata | https://www.wikidata.org/wiki/Q901177 |
| concepts[9].display_name | Gaussian |
| concepts[10].id | https://openalex.org/C11413529 |
| concepts[10].level | 1 |
| concepts[10].score | 0.3409196138381958 |
| concepts[10].wikidata | https://www.wikidata.org/wiki/Q8366 |
| concepts[10].display_name | Algorithm |
| concepts[11].id | https://openalex.org/C154945302 |
| concepts[11].level | 1 |
| concepts[11].score | 0.3393543064594269 |
| concepts[11].wikidata | https://www.wikidata.org/wiki/Q11660 |
| concepts[11].display_name | Artificial intelligence |
| concepts[12].id | https://openalex.org/C121332964 |
| concepts[12].level | 0 |
| concepts[12].score | 0.2180267572402954 |
| concepts[12].wikidata | https://www.wikidata.org/wiki/Q413 |
| concepts[12].display_name | Physics |
| concepts[13].id | https://openalex.org/C120665830 |
| concepts[13].level | 1 |
| concepts[13].score | 0.16891184449195862 |
| concepts[13].wikidata | https://www.wikidata.org/wiki/Q14620 |
| concepts[13].display_name | Optics |
| concepts[14].id | https://openalex.org/C98444146 |
| concepts[14].level | 2 |
| concepts[14].score | 0.13876277208328247 |
| concepts[14].wikidata | https://www.wikidata.org/wiki/Q318 |
| concepts[14].display_name | Galaxy |
| concepts[15].id | https://openalex.org/C44870925 |
| concepts[15].level | 1 |
| concepts[15].score | 0.09145531058311462 |
| concepts[15].wikidata | https://www.wikidata.org/wiki/Q37547 |
| concepts[15].display_name | Astrophysics |
| concepts[16].id | https://openalex.org/C33024259 |
| concepts[16].level | 3 |
| concepts[16].score | 0.0 |
| concepts[16].wikidata | https://www.wikidata.org/wiki/Q76250 |
| concepts[16].display_name | Redshift |
| concepts[17].id | https://openalex.org/C62520636 |
| concepts[17].level | 1 |
| concepts[17].score | 0.0 |
| concepts[17].wikidata | https://www.wikidata.org/wiki/Q944 |
| concepts[17].display_name | Quantum mechanics |
| concepts[18].id | https://openalex.org/C199360897 |
| concepts[18].level | 1 |
| concepts[18].score | 0.0 |
| concepts[18].wikidata | https://www.wikidata.org/wiki/Q9143 |
| concepts[18].display_name | Programming language |
| keywords[0].id | https://openalex.org/keywords/computer-science |
| keywords[0].score | 0.6616784334182739 |
| keywords[0].display_name | Computer science |
| keywords[1].id | https://openalex.org/keywords/leverage |
| keywords[1].score | 0.54905766248703 |
| keywords[1].display_name | Leverage (statistics) |
| keywords[2].id | https://openalex.org/keywords/dark-matter |
| keywords[2].score | 0.5184807181358337 |
| keywords[2].display_name | Dark matter |
| keywords[3].id | https://openalex.org/keywords/weak-gravitational-lensing |
| keywords[3].score | 0.4740702211856842 |
| keywords[3].display_name | Weak gravitational lensing |
| keywords[4].id | https://openalex.org/keywords/inference |
| keywords[4].score | 0.4690573513507843 |
| keywords[4].display_name | Inference |
| keywords[5].id | https://openalex.org/keywords/pipeline |
| keywords[5].score | 0.4670034646987915 |
| keywords[5].display_name | Pipeline (software) |
| keywords[6].id | https://openalex.org/keywords/lens |
| keywords[6].score | 0.4577312767505646 |
| keywords[6].display_name | Lens (geology) |
| keywords[7].id | https://openalex.org/keywords/hyperparameter |
| keywords[7].score | 0.43790918588638306 |
| keywords[7].display_name | Hyperparameter |
| keywords[8].id | https://openalex.org/keywords/gaussian-process |
| keywords[8].score | 0.4100888967514038 |
| keywords[8].display_name | Gaussian process |
| keywords[9].id | https://openalex.org/keywords/gaussian |
| keywords[9].score | 0.4021570682525635 |
| keywords[9].display_name | Gaussian |
| keywords[10].id | https://openalex.org/keywords/algorithm |
| keywords[10].score | 0.3409196138381958 |
| keywords[10].display_name | Algorithm |
| keywords[11].id | https://openalex.org/keywords/artificial-intelligence |
| keywords[11].score | 0.3393543064594269 |
| keywords[11].display_name | Artificial intelligence |
| keywords[12].id | https://openalex.org/keywords/physics |
| keywords[12].score | 0.2180267572402954 |
| keywords[12].display_name | Physics |
| keywords[13].id | https://openalex.org/keywords/optics |
| keywords[13].score | 0.16891184449195862 |
| keywords[13].display_name | Optics |
| keywords[14].id | https://openalex.org/keywords/galaxy |
| keywords[14].score | 0.13876277208328247 |
| keywords[14].display_name | Galaxy |
| keywords[15].id | https://openalex.org/keywords/astrophysics |
| keywords[15].score | 0.09145531058311462 |
| keywords[15].display_name | Astrophysics |
| language | en |
| locations[0].id | pmh:oai:arXiv.org:2105.09465 |
| locations[0].is_oa | True |
| locations[0].source.id | https://openalex.org/S4306400194 |
| locations[0].source.issn | |
| locations[0].source.type | repository |
| locations[0].source.is_oa | True |
| locations[0].source.issn_l | |
| locations[0].source.is_core | False |
| locations[0].source.is_in_doaj | False |
| locations[0].source.display_name | arXiv (Cornell University) |
| locations[0].source.host_organization | https://openalex.org/I205783295 |
| locations[0].source.host_organization_name | Cornell University |
| locations[0].source.host_organization_lineage | https://openalex.org/I205783295 |
| locations[0].license | |
| locations[0].pdf_url | https://arxiv.org/pdf/2105.09465 |
| locations[0].version | submittedVersion |
| locations[0].raw_type | text |
| locations[0].license_id | |
| locations[0].is_accepted | False |
| locations[0].is_published | False |
| locations[0].raw_source_name | |
| locations[0].landing_page_url | http://arxiv.org/abs/2105.09465 |
| locations[1].id | mag:3161441416 |
| locations[1].is_oa | True |
| locations[1].source.id | https://openalex.org/S4306400194 |
| locations[1].source.issn | |
| locations[1].source.type | repository |
| locations[1].source.is_oa | True |
| locations[1].source.issn_l | |
| locations[1].source.is_core | False |
| locations[1].source.is_in_doaj | False |
| locations[1].source.display_name | arXiv (Cornell University) |
| locations[1].source.host_organization | https://openalex.org/I205783295 |
| locations[1].source.host_organization_name | Cornell University |
| locations[1].source.host_organization_lineage | https://openalex.org/I205783295 |
| locations[1].license | |
| locations[1].pdf_url | |
| locations[1].version | submittedVersion |
| locations[1].raw_type | |
| locations[1].license_id | |
| locations[1].is_accepted | False |
| locations[1].is_published | False |
| locations[1].raw_source_name | arXiv (Cornell University) |
| locations[1].landing_page_url | http://arxiv.org/pdf/2105.09465.pdf |
| locations[2].id | doi:10.48550/arxiv.2105.09465 |
| locations[2].is_oa | True |
| locations[2].source.id | https://openalex.org/S4306400194 |
| locations[2].source.issn | |
| locations[2].source.type | repository |
| locations[2].source.is_oa | True |
| locations[2].source.issn_l | |
| locations[2].source.is_core | False |
| locations[2].source.is_in_doaj | False |
| locations[2].source.display_name | arXiv (Cornell University) |
| locations[2].source.host_organization | https://openalex.org/I205783295 |
| locations[2].source.host_organization_name | Cornell University |
| locations[2].source.host_organization_lineage | https://openalex.org/I205783295 |
| locations[2].license | |
| locations[2].pdf_url | |
| locations[2].version | |
| locations[2].raw_type | article-journal |
| locations[2].license_id | |
| locations[2].is_accepted | False |
| locations[2].is_published | |
| locations[2].raw_source_name | |
| locations[2].landing_page_url | https://doi.org/10.48550/arxiv.2105.09465 |
| indexed_in | arxiv, datacite |
| authorships[0].author.id | https://openalex.org/A5048451185 |
| authorships[0].author.orcid | https://orcid.org/0000-0001-9344-736X |
| authorships[0].author.display_name | Konstantin Karchev |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Konstantin Karchev |
| authorships[0].is_corresponding | False |
| authorships[1].author.id | https://openalex.org/A5056146576 |
| authorships[1].author.orcid | https://orcid.org/0000-0002-0055-1780 |
| authorships[1].author.display_name | Adam Coogan |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | Adam Coogan |
| authorships[1].is_corresponding | False |
| authorships[2].author.id | https://openalex.org/A5021706010 |
| authorships[2].author.orcid | https://orcid.org/0000-0001-7579-8684 |
| authorships[2].author.display_name | Christoph Weniger |
| authorships[2].author_position | last |
| authorships[2].raw_author_name | Christoph Weniger |
| authorships[2].is_corresponding | False |
| has_content.pdf | False |
| has_content.grobid_xml | False |
| is_paratext | False |
| open_access.is_oa | True |
| open_access.oa_url | https://arxiv.org/pdf/2105.09465 |
| open_access.oa_status | green |
| open_access.any_repository_has_fulltext | False |
| created_date | 2025-10-10T00:00:00 |
| display_name | Strong-lensing source reconstruction with variationally optimised Gaussian processes |
| has_fulltext | False |
| is_retracted | False |
| updated_date | 2025-11-06T06:51:31.235846 |
| primary_topic.id | https://openalex.org/T12814 |
| primary_topic.field.id | https://openalex.org/fields/17 |
| primary_topic.field.display_name | Computer Science |
| primary_topic.score | 0.9998999834060669 |
| primary_topic.domain.id | https://openalex.org/domains/3 |
| primary_topic.domain.display_name | Physical Sciences |
| primary_topic.subfield.id | https://openalex.org/subfields/1702 |
| primary_topic.subfield.display_name | Artificial Intelligence |
| primary_topic.display_name | Gaussian Processes and Bayesian Inference |
| related_works | https://openalex.org/W3112007145, https://openalex.org/W2018283301, https://openalex.org/W2871250032, https://openalex.org/W1996000811, https://openalex.org/W2105905869, https://openalex.org/W2769302428, https://openalex.org/W2904703261, https://openalex.org/W3098610707, https://openalex.org/W3103610914, https://openalex.org/W1865771124, https://openalex.org/W2022511650, https://openalex.org/W3113013033, https://openalex.org/W2917923318, https://openalex.org/W2062981079, https://openalex.org/W2945742880, https://openalex.org/W3210615731, https://openalex.org/W3048592282, https://openalex.org/W2912451899, https://openalex.org/W2801421298, https://openalex.org/W2792224070 |
| cited_by_count | 4 |
| counts_by_year[0].year | 2022 |
| counts_by_year[0].cited_by_count | 2 |
| counts_by_year[1].year | 2021 |
| counts_by_year[1].cited_by_count | 2 |
| locations_count | 3 |
| best_oa_location.id | pmh:oai:arXiv.org:2105.09465 |
| best_oa_location.is_oa | True |
| best_oa_location.source.id | https://openalex.org/S4306400194 |
| best_oa_location.source.issn | |
| best_oa_location.source.type | repository |
| best_oa_location.source.is_oa | True |
| best_oa_location.source.issn_l | |
| best_oa_location.source.is_core | False |
| best_oa_location.source.is_in_doaj | False |
| best_oa_location.source.display_name | arXiv (Cornell University) |
| best_oa_location.source.host_organization | https://openalex.org/I205783295 |
| best_oa_location.source.host_organization_name | Cornell University |
| best_oa_location.source.host_organization_lineage | https://openalex.org/I205783295 |
| best_oa_location.license | |
| best_oa_location.pdf_url | https://arxiv.org/pdf/2105.09465 |
| best_oa_location.version | submittedVersion |
| best_oa_location.raw_type | text |
| best_oa_location.license_id | |
| best_oa_location.is_accepted | False |
| best_oa_location.is_published | False |
| best_oa_location.raw_source_name | |
| best_oa_location.landing_page_url | http://arxiv.org/abs/2105.09465 |
| primary_location.id | pmh:oai:arXiv.org:2105.09465 |
| primary_location.is_oa | True |
| primary_location.source.id | https://openalex.org/S4306400194 |
| primary_location.source.issn | |
| primary_location.source.type | repository |
| primary_location.source.is_oa | True |
| primary_location.source.issn_l | |
| primary_location.source.is_core | False |
| primary_location.source.is_in_doaj | False |
| primary_location.source.display_name | arXiv (Cornell University) |
| primary_location.source.host_organization | https://openalex.org/I205783295 |
| primary_location.source.host_organization_name | Cornell University |
| primary_location.source.host_organization_lineage | https://openalex.org/I205783295 |
| primary_location.license | |
| primary_location.pdf_url | https://arxiv.org/pdf/2105.09465 |
| primary_location.version | submittedVersion |
| primary_location.raw_type | text |
| primary_location.license_id | |
| primary_location.is_accepted | False |
| primary_location.is_published | False |
| primary_location.raw_source_name | |
| primary_location.landing_page_url | http://arxiv.org/abs/2105.09465 |
| publication_date | 2021-05-20 |
| publication_year | 2021 |
| referenced_works | https://openalex.org/W1663973292, https://openalex.org/W1746819321, https://openalex.org/W2945048248 |
| referenced_works_count | 3 |
| abstract_inverted_index.a | 3, 73, 83, 94, 103, 111, 127, 189 |
| abstract_inverted_index.In | 62 |
| abstract_inverted_index.be | 27 |
| abstract_inverted_index.in | 18, 68, 78, 188 |
| abstract_inverted_index.is | 170 |
| abstract_inverted_index.of | 5, 23, 33, 45, 48, 64, 75, 139, 141, 167, 173, 180 |
| abstract_inverted_index.on | 110 |
| abstract_inverted_index.to | 29, 60, 81, 116 |
| abstract_inverted_index.we | 71, 185 |
| abstract_inverted_index.(A) | 93 |
| abstract_inverted_index.(B) | 102 |
| abstract_inverted_index.(C) | 126 |
| abstract_inverted_index.Its | 90 |
| abstract_inverted_index.aim | 166 |
| abstract_inverted_index.and | 12, 42, 53, 125, 143, 146, 155, 159 |
| abstract_inverted_index.are | 57 |
| abstract_inverted_index.can | 26 |
| abstract_inverted_index.for | 137, 176 |
| abstract_inverted_index.new | 84 |
| abstract_inverted_index.the | 9, 14, 19, 31, 46, 164, 168, 171 |
| abstract_inverted_index.via | 149 |
| abstract_inverted_index.also | 120 |
| abstract_inverted_index.are: | 92 |
| abstract_inverted_index.both | 7, 49 |
| abstract_inverted_index.dark | 15, 34, 181 |
| abstract_inverted_index.data | 175 |
| abstract_inverted_index.into | 122 |
| abstract_inverted_index.lens | 50, 142, 160 |
| abstract_inverted_index.main | 165 |
| abstract_inverted_index.mass | 51 |
| abstract_inverted_index.new, | 104 |
| abstract_inverted_index.tens | 138 |
| abstract_inverted_index.that | 119, 132 |
| abstract_inverted_index.this | 37, 69 |
| abstract_inverted_index.used | 28 |
| abstract_inverted_index.will | 186 |
| abstract_inverted_index.work | 70 |
| abstract_inverted_index.about | 8, 13 |
| abstract_inverted_index.based | 109 |
| abstract_inverted_index.fast, | 95 |
| abstract_inverted_index.image | 40, 87, 100 |
| abstract_inverted_index.lens. | 20 |
| abstract_inverted_index.model | 108, 161 |
| abstract_inverted_index.range | 74 |
| abstract_inverted_index.takes | 121 |
| abstract_inverted_index.these | 24 |
| abstract_inverted_index.which | 56, 184 |
| abstract_inverted_index.future | 65 |
| abstract_inverted_index.highly | 113 |
| abstract_inverted_index.images | 1, 25 |
| abstract_inverted_index.light, | 55 |
| abstract_inverted_index.matter | 16, 182 |
| abstract_inverted_index.nature | 32 |
| abstract_inverted_index.paper. | 191 |
| abstract_inverted_index.recent | 76 |
| abstract_inverted_index.source | 11, 54, 107, 144 |
| abstract_inverted_index.wealth | 4 |
| abstract_inverted_index.Besides | 153 |
| abstract_inverted_index.account | 123 |
| abstract_inverted_index.careful | 43 |
| abstract_inverted_index.develop | 82 |
| abstract_inverted_index.enables | 133 |
| abstract_inverted_index.exploit | 187 |
| abstract_inverted_index.machine | 79 |
| abstract_inverted_index.matter. | 35 |
| abstract_inverted_index.provide | 2 |
| abstract_inverted_index.Bayesian | 85 |
| abstract_inverted_index.Gaussian | 117 |
| abstract_inverted_index.However, | 36 |
| abstract_inverted_index.accurate | 156 |
| abstract_inverted_index.analyses | 22 |
| abstract_inverted_index.analysis | 88 |
| abstract_inverted_index.deriving | 135 |
| abstract_inverted_index.descent. | 152 |
| abstract_inverted_index.gradient | 151 |
| abstract_inverted_index.learning | 80 |
| abstract_inverted_index.leverage | 72 |
| abstract_inverted_index.pipeline | 169 |
| abstract_inverted_index.requires | 38 |
| abstract_inverted_index.scalable | 128 |
| abstract_inverted_index.targeted | 177 |
| abstract_inverted_index.training | 174 |
| abstract_inverted_index.Precision | 21 |
| abstract_inverted_index.companion | 190 |
| abstract_inverted_index.constrain | 30 |
| abstract_inverted_index.datasets, | 67 |
| abstract_inverted_index.difficult | 59 |
| abstract_inverted_index.efficient | 114, 154 |
| abstract_inverted_index.framework | 131 |
| abstract_inverted_index.inference | 130, 179 |
| abstract_inverted_index.magnified | 10 |
| abstract_inverted_index.parameter | 157 |
| abstract_inverted_index.pipeline. | 89 |
| abstract_inverted_index.processes | 118 |
| abstract_inverted_index.quantify. | 61 |
| abstract_inverted_index.thousands | 140 |
| abstract_inverted_index.treatment | 44 |
| abstract_inverted_index.typically | 58 |
| abstract_inverted_index.end-to-end | 97 |
| abstract_inverted_index.estimation | 158 |
| abstract_inverted_index.generation | 172 |
| abstract_inverted_index.highlights | 91 |
| abstract_inverted_index.optimising | 147 |
| abstract_inverted_index.parameters | 145 |
| abstract_inverted_index.posteriors | 136 |
| abstract_inverted_index.principled | 106 |
| abstract_inverted_index.simulator; | 101 |
| abstract_inverted_index.stochastic | 150 |
| abstract_inverted_index.information | 6 |
| abstract_inverted_index.uncertainty | 162 |
| abstract_inverted_index.variational | 129 |
| abstract_inverted_index.GPU-enabled, | 96 |
| abstract_inverted_index.anticipation | 63 |
| abstract_inverted_index.developments | 77 |
| abstract_inverted_index.distribution | 17, 52 |
| abstract_inverted_index.pixellation; | 124 |
| abstract_inverted_index.approximation | 115 |
| abstract_inverted_index.high-fidelity | 39 |
| abstract_inverted_index.statistically | 105 |
| abstract_inverted_index.substructure, | 183 |
| abstract_inverted_index.uncertainties | 47 |
| abstract_inverted_index.Strong-lensing | 0 |
| abstract_inverted_index.differentiable | 98 |
| abstract_inverted_index.simultaneously | 134 |
| abstract_inverted_index.strong-lensing | 86, 99 |
| abstract_inverted_index.computationally | 112 |
| abstract_inverted_index.high-resolution | 66 |
| abstract_inverted_index.hyperparameters | 148 |
| abstract_inverted_index.quantification, | 163 |
| abstract_inverted_index.reconstructions | 41 |
| abstract_inverted_index.simulation-based | 178 |
| cited_by_percentile_year.max | 96 |
| cited_by_percentile_year.min | 93 |
| countries_distinct_count | 0 |
| institutions_distinct_count | 3 |
| citation_normalized_percentile.value | 0.72659659 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |