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Hauptverfasser: Thomsen, A., Bucko, J., Kacprzak, T., Ajani, V., Fluri, J., Refregier, A., Anbajagane, D., Castander, F. J., Ferté, A., Gatti, M., Jeffrey, N., Alarcon, A., Amon, A., Bechtol, K., Becker, M. R., Bernstein, G. M., Campos, A., Rosell, A. Carnero, Chang, C., Chen, R., Choi, A., Crocce, M., Davis, C., DeRose, J., Dodelson, S., Doux, C., Eckert, K., Elvin-Poole, J., Everett, S., Fosalba, P., Gruen, D., Harrison, I., Herner, K., Huff, E. M., Jarvis, M., Kuropatkin, N., Leget, P. -F., MacCrann, N., McCullough, J., Myles, J., Navarro-Alsina, A., Pandey, S., Porredon, A., Prat, J., Raveri, M., Rodriguez-Monroy, M., Rollins, R. P., Roodman, A., Rykoff, E. S., Sánchez, C., Secco, L. F., Sheldon, E., Shin, T., Troxel, M. A., Tutusaus, I., Varga, T. N., Weaverdyck, N., Wechsler, R. H., Yanny, B., Yin, B., Zhang, Y., Zuntz, J., Aguena, M., Allam, S., Andrade-Oliveira, F., Bacon, D., Blazek, J., Brooks, D., Camilleri, R., Carretero, J., Cawthon, R., da Costa, L. N., Pereira, M. E. da Silva, Davis, T. M., De Vicente, J., Desai, S., Doel, P., García-Bellido, J., Gutierrez, G., Hinton, S. R., Hollowood, D. L., Honscheid, K., James, D. J., Kuehn, K., Lahav, O., Lee, S., Marshall, J. L., Mena-Fernández, J., Menanteau, F., Miquel, R., Muir, J., Ogando, R. L. C., Malagón, A. A. Plazas, Sanchez, E., Cid, D. Sanchez, Sevilla-Noarbe, I., Smith, M., Suchyta, E., Swanson, M. E. C., Thomas, D., To, C., Tucker, D. L.
Format: Preprint
Veröffentlicht: 2025
Schlagworte:
Cosmology and Nongalactic Astrophysics
Machine Learning
Online-Zugang:https://arxiv.org/abs/2511.04681
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author Thomsen, A.
Bucko, J.
Kacprzak, T.
Ajani, V.
Fluri, J.
Refregier, A.
Anbajagane, D.
Castander, F. J.
Ferté, A.
Gatti, M.
Jeffrey, N.
Alarcon, A.
Amon, A.
Bechtol, K.
Becker, M. R.
Bernstein, G. M.
Campos, A.
Rosell, A. Carnero
Chang, C.
Chen, R.
Choi, A.
Crocce, M.
Davis, C.
DeRose, J.
Dodelson, S.
Doux, C.
Eckert, K.
Elvin-Poole, J.
Everett, S.
Fosalba, P.
Gruen, D.
Harrison, I.
Herner, K.
Huff, E. M.
Jarvis, M.
Kuropatkin, N.
Leget, P. -F.
MacCrann, N.
McCullough, J.
Myles, J.
Navarro-Alsina, A.
Pandey, S.
Porredon, A.
Prat, J.
Raveri, M.
Rodriguez-Monroy, M.
Rollins, R. P.
Roodman, A.
Rykoff, E. S.
Sánchez, C.
Secco, L. F.
Sheldon, E.
Shin, T.
Troxel, M. A.
Tutusaus, I.
Varga, T. N.
Weaverdyck, N.
Wechsler, R. H.
Yanny, B.
Yin, B.
Zhang, Y.
Zuntz, J.
Aguena, M.
Allam, S.
Andrade-Oliveira, F.
Bacon, D.
Blazek, J.
Brooks, D.
Camilleri, R.
Carretero, J.
Cawthon, R.
da Costa, L. N.
Pereira, M. E. da Silva
Davis, T. M.
De Vicente, J.
Desai, S.
Doel, P.
García-Bellido, J.
Gutierrez, G.
Hinton, S. R.
Hollowood, D. L.
Honscheid, K.
James, D. J.
Kuehn, K.
Lahav, O.
Lee, S.
Marshall, J. L.
Mena-Fernández, J.
Menanteau, F.
Miquel, R.
Muir, J.
Ogando, R. L. C.
Malagón, A. A. Plazas
Sanchez, E.
Cid, D. Sanchez
Sevilla-Noarbe, I.
Smith, M.
Suchyta, E.
Swanson, M. E. C.
Thomas, D.
To, C.
Tucker, D. L.
author_facet Thomsen, A.
Bucko, J.
Kacprzak, T.
Ajani, V.
Fluri, J.
Refregier, A.
Anbajagane, D.
Castander, F. J.
Ferté, A.
Gatti, M.
Jeffrey, N.
Alarcon, A.
Amon, A.
Bechtol, K.
Becker, M. R.
Bernstein, G. M.
Campos, A.
Rosell, A. Carnero
Chang, C.
Chen, R.
Choi, A.
Crocce, M.
Davis, C.
DeRose, J.
Dodelson, S.
Doux, C.
Eckert, K.
Elvin-Poole, J.
Everett, S.
Fosalba, P.
Gruen, D.
Harrison, I.
Herner, K.
Huff, E. M.
Jarvis, M.
Kuropatkin, N.
Leget, P. -F.
MacCrann, N.
McCullough, J.
Myles, J.
Navarro-Alsina, A.
Pandey, S.
Porredon, A.
Prat, J.
Raveri, M.
Rodriguez-Monroy, M.
Rollins, R. P.
Roodman, A.
Rykoff, E. S.
Sánchez, C.
Secco, L. F.
Sheldon, E.
Shin, T.
Troxel, M. A.
Tutusaus, I.
Varga, T. N.
Weaverdyck, N.
Wechsler, R. H.
Yanny, B.
Yin, B.
Zhang, Y.
Zuntz, J.
Aguena, M.
Allam, S.
Andrade-Oliveira, F.
Bacon, D.
Blazek, J.
Brooks, D.
Camilleri, R.
Carretero, J.
Cawthon, R.
da Costa, L. N.
Pereira, M. E. da Silva
Davis, T. M.
De Vicente, J.
Desai, S.
Doel, P.
García-Bellido, J.
Gutierrez, G.
Hinton, S. R.
Hollowood, D. L.
Honscheid, K.
James, D. J.
Kuehn, K.
Lahav, O.
Lee, S.
Marshall, J. L.
Mena-Fernández, J.
Menanteau, F.
Miquel, R.
Muir, J.
Ogando, R. L. C.
Malagón, A. A. Plazas
Sanchez, E.
Cid, D. Sanchez
Sevilla-Noarbe, I.
Smith, M.
Suchyta, E.
Swanson, M. E. C.
Thomas, D.
To, C.
Tucker, D. L.
contents Data-driven approaches using deep learning are emerging as powerful techniques to extract non-Gaussian information from cosmological large-scale structure. This work presents the first simulation-based inference (SBI) pipeline that combines weak lensing and galaxy clustering maps in a realistic Dark Energy Survey Year 3 (DES Y3) configuration and serves as preparation for a forthcoming analysis of the survey data. We develop a scalable forward model based on the CosmoGridV1 suite of N-body simulations to generate over one million self-consistent mock realizations of DES Y3 at the map level. Leveraging this large dataset, we train deep graph convolutional neural networks on the full survey footprint in spherical geometry to learn low-dimensional features that approximately maximize mutual information with target parameters. These learned compressions enable neural density estimation of the implicit likelihood via normalizing flows in a ten-dimensional parameter space spanning cosmological $w$CDM, intrinsic alignment, and linear galaxy bias parameters, while marginalizing over baryonic, photometric redshift, and shear bias nuisances. To ensure robustness, we extensively validate our inference pipeline using synthetic observations derived from both systematic contaminations in our forward model and independent Buzzard galaxy catalogs. Our forecasts yield significant improvements in cosmological parameter constraints, achieving $2-3\times$ higher figures of merit in the $Ω_m - S_8$ plane relative to our implementation of baseline two-point statistics and effectively breaking parameter degeneracies through probe combination. These results demonstrate the potential of SBI analyses powered by deep learning for upcoming Stage-IV wide-field imaging surveys.
format Preprint
id arxiv_https___arxiv_org_abs_2511_04681
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle Dark Energy Survey Year 3 results: Simulation-based $w$CDM inference from weak lensing and galaxy clustering maps with deep learning: Analysis design
Thomsen, A.
Bucko, J.
Kacprzak, T.
Ajani, V.
Fluri, J.
Refregier, A.
Anbajagane, D.
Castander, F. J.
Ferté, A.
Gatti, M.
Jeffrey, N.
Alarcon, A.
Amon, A.
Bechtol, K.
Becker, M. R.
Bernstein, G. M.
Campos, A.
Rosell, A. Carnero
Chang, C.
Chen, R.
Choi, A.
Crocce, M.
Davis, C.
DeRose, J.
Dodelson, S.
Doux, C.
Eckert, K.
Elvin-Poole, J.
Everett, S.
Fosalba, P.
Gruen, D.
Harrison, I.
Herner, K.
Huff, E. M.
Jarvis, M.
Kuropatkin, N.
Leget, P. -F.
MacCrann, N.
McCullough, J.
Myles, J.
Navarro-Alsina, A.
Pandey, S.
Porredon, A.
Prat, J.
Raveri, M.
Rodriguez-Monroy, M.
Rollins, R. P.
Roodman, A.
Rykoff, E. S.
Sánchez, C.
Secco, L. F.
Sheldon, E.
Shin, T.
Troxel, M. A.
Tutusaus, I.
Varga, T. N.
Weaverdyck, N.
Wechsler, R. H.
Yanny, B.
Yin, B.
Zhang, Y.
Zuntz, J.
Aguena, M.
Allam, S.
Andrade-Oliveira, F.
Bacon, D.
Blazek, J.
Brooks, D.
Camilleri, R.
Carretero, J.
Cawthon, R.
da Costa, L. N.
Pereira, M. E. da Silva
Davis, T. M.
De Vicente, J.
Desai, S.
Doel, P.
García-Bellido, J.
Gutierrez, G.
Hinton, S. R.
Hollowood, D. L.
Honscheid, K.
James, D. J.
Kuehn, K.
Lahav, O.
Lee, S.
Marshall, J. L.
Mena-Fernández, J.
Menanteau, F.
Miquel, R.
Muir, J.
Ogando, R. L. C.
Malagón, A. A. Plazas
Sanchez, E.
Cid, D. Sanchez
Sevilla-Noarbe, I.
Smith, M.
Suchyta, E.
Swanson, M. E. C.
Thomas, D.
To, C.
Tucker, D. L.
Cosmology and Nongalactic Astrophysics
Machine Learning
Data-driven approaches using deep learning are emerging as powerful techniques to extract non-Gaussian information from cosmological large-scale structure. This work presents the first simulation-based inference (SBI) pipeline that combines weak lensing and galaxy clustering maps in a realistic Dark Energy Survey Year 3 (DES Y3) configuration and serves as preparation for a forthcoming analysis of the survey data. We develop a scalable forward model based on the CosmoGridV1 suite of N-body simulations to generate over one million self-consistent mock realizations of DES Y3 at the map level. Leveraging this large dataset, we train deep graph convolutional neural networks on the full survey footprint in spherical geometry to learn low-dimensional features that approximately maximize mutual information with target parameters. These learned compressions enable neural density estimation of the implicit likelihood via normalizing flows in a ten-dimensional parameter space spanning cosmological $w$CDM, intrinsic alignment, and linear galaxy bias parameters, while marginalizing over baryonic, photometric redshift, and shear bias nuisances. To ensure robustness, we extensively validate our inference pipeline using synthetic observations derived from both systematic contaminations in our forward model and independent Buzzard galaxy catalogs. Our forecasts yield significant improvements in cosmological parameter constraints, achieving $2-3\times$ higher figures of merit in the $Ω_m - S_8$ plane relative to our implementation of baseline two-point statistics and effectively breaking parameter degeneracies through probe combination. These results demonstrate the potential of SBI analyses powered by deep learning for upcoming Stage-IV wide-field imaging surveys.
title Dark Energy Survey Year 3 results: Simulation-based $w$CDM inference from weak lensing and galaxy clustering maps with deep learning: Analysis design
topic Cosmology and Nongalactic Astrophysics
Machine Learning
url https://arxiv.org/abs/2511.04681

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