LimberJack.jl: auto-differentiable methods for angular power spectra analyses
We present LimberJack.jl, a fully auto-differentiable code for cosmological analyses of 2 point auto- and cross-correlation measurements from galaxy clustering, CMB lensing and weak lensing data written in Julia. Using Julia’s auto-differentiation ecosystem, LimberJack.jl can obtain gradients for it...
| Main Authors: | , , , , , , , |
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| Format: | Journal article |
| Language: | English |
| Published: |
Maynooth Academic Publishing
2024
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| _version_ | 1797112866951659520 |
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| author | Ruiz-Zapatero, J Alonso, D García-García, C Nicola, A Mootoovaloo, A Sullivan, JM Bonici, M Ferreira, PG |
| author_facet | Ruiz-Zapatero, J Alonso, D García-García, C Nicola, A Mootoovaloo, A Sullivan, JM Bonici, M Ferreira, PG |
| author_sort | Ruiz-Zapatero, J |
| collection | OXFORD |
| description | We present LimberJack.jl, a fully auto-differentiable code for cosmological analyses of 2 point auto- and cross-correlation measurements from galaxy clustering, CMB lensing and weak lensing data written in Julia. Using Julia’s auto-differentiation ecosystem, LimberJack.jl can obtain gradients for its outputs an order of magnitude faster than traditional finite difference methods. This makes LimberJack.jl greatly synergistic with gradient-based sampling methods, such as Hamiltonian Monte Carlo, capable of efficiently exploring parameter spaces with hundreds of dimensions. We first prove LimberJack.jl’s reliability by reanalysing the DES Y1 3×2-point data. We then showcase its capabilities by using a O(100) parameters Gaussian Process to reconstruct the cosmic growth from a combination of DES Y1 galaxy clustering and weak lensing data, eBOSS QSO’s, CMB lensing and redshift-space distortions. Our Gaussian process reconstruction of the growth factor is statistically consistent with the ΛCDM Planck 2018 prediction at all redshifts. Moreover, we show that the addition of RSD data is extremely beneficial to this type of analysis, reducing the uncertainty in the reconstructed growth factor by 20% on average across redshift. LimberJack.jl is a fully open-source project available on Julia’s general repository of packages and GitHub. |
| first_indexed | 2024-03-07T08:29:57Z |
| format | Journal article |
| id | oxford-uuid:f114cd01-c016-4f01-9a2a-d7d4a4ab5815 |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-03-07T08:29:57Z |
| publishDate | 2024 |
| publisher | Maynooth Academic Publishing |
| record_format | dspace |
| spelling | oxford-uuid:f114cd01-c016-4f01-9a2a-d7d4a4ab58152024-03-04T14:29:02ZLimberJack.jl: auto-differentiable methods for angular power spectra analysesJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:f114cd01-c016-4f01-9a2a-d7d4a4ab5815EnglishSymplectic ElementsMaynooth Academic Publishing2024Ruiz-Zapatero, JAlonso, DGarcía-García, CNicola, AMootoovaloo, ASullivan, JMBonici, MFerreira, PGWe present LimberJack.jl, a fully auto-differentiable code for cosmological analyses of 2 point auto- and cross-correlation measurements from galaxy clustering, CMB lensing and weak lensing data written in Julia. Using Julia’s auto-differentiation ecosystem, LimberJack.jl can obtain gradients for its outputs an order of magnitude faster than traditional finite difference methods. This makes LimberJack.jl greatly synergistic with gradient-based sampling methods, such as Hamiltonian Monte Carlo, capable of efficiently exploring parameter spaces with hundreds of dimensions. We first prove LimberJack.jl’s reliability by reanalysing the DES Y1 3×2-point data. We then showcase its capabilities by using a O(100) parameters Gaussian Process to reconstruct the cosmic growth from a combination of DES Y1 galaxy clustering and weak lensing data, eBOSS QSO’s, CMB lensing and redshift-space distortions. Our Gaussian process reconstruction of the growth factor is statistically consistent with the ΛCDM Planck 2018 prediction at all redshifts. Moreover, we show that the addition of RSD data is extremely beneficial to this type of analysis, reducing the uncertainty in the reconstructed growth factor by 20% on average across redshift. LimberJack.jl is a fully open-source project available on Julia’s general repository of packages and GitHub. |
| spellingShingle | Ruiz-Zapatero, J Alonso, D García-García, C Nicola, A Mootoovaloo, A Sullivan, JM Bonici, M Ferreira, PG LimberJack.jl: auto-differentiable methods for angular power spectra analyses |
| title | LimberJack.jl: auto-differentiable methods for angular power spectra analyses |
| title_full | LimberJack.jl: auto-differentiable methods for angular power spectra analyses |
| title_fullStr | LimberJack.jl: auto-differentiable methods for angular power spectra analyses |
| title_full_unstemmed | LimberJack.jl: auto-differentiable methods for angular power spectra analyses |
| title_short | LimberJack.jl: auto-differentiable methods for angular power spectra analyses |
| title_sort | limberjack jl auto differentiable methods for angular power spectra analyses |
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