Efficiently emulating distribution functions in gigaparsec volumes for varying cosmological parameters | Paper Citation Record

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This paper cites Optuna: A Next-generation Hyperparameter Optimization Framework.

Efficiently emulating distribution functions in gigaparsec volumes for varying cosmological parameters Optuna: A Next-generation Hyperparameter Optimization Framework

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This paper cites 2025, title The BIG SOBOL SEQUENCE: How many simulations do we need for simulation-based inference in cosmology? , , 703, A301, 10.1051/0004-6361/202554602.

Efficiently emulating distribution functions in gigaparsec volumes for varying cosmological parameters 2025, title The BIG SOBOL SEQUENCE: How many simulations do we need for simulation-based inference in cosmology? , , 703, A301, 10.1051/0004-6361/202554602

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This paper cites and Steinhardt, Paul J.

Efficiently emulating distribution functions in gigaparsec volumes for varying cosmological parameters and Steinhardt, Paul J

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This paper cites J., et al., 2017, @doi [MNRAS] 10.1093/mnras/stx1647 , 471.

Efficiently emulating distribution functions in gigaparsec volumes for varying cosmological parameters J., et al., 2017, @doi [MNRAS] 10.1093/mnras/stx1647 , 471

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This paper cites J., Ho M., Wandelt B.

Efficiently emulating distribution functions in gigaparsec volumes for varying cosmological parameters J., Ho M., Wandelt B

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This paper cites J., 2016, @doi [ ] 10.1093/mnras/stv2657 , https://ui.adsabs.harvard.edu/abs/2016MNRAS.456.2361B 456.

Efficiently emulating distribution functions in gigaparsec volumes for varying cosmological parameters J., 2016, @doi [ ] 10.1093/mnras/stv2657 , https://ui.adsabs.harvard.edu/abs/2016MNRAS.456.2361B 456

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Efficiently emulating distribution functions in gigaparsec volumes for varying cosmological parameters Unresolved cited work

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This paper cites Bower, R.

Efficiently emulating distribution functions in gigaparsec volumes for varying cosmological parameters Bower, R

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This paper cites doi:10.1093/mnras/staa3473 , archiveprefix =.

Efficiently emulating distribution functions in gigaparsec volumes for varying cosmological parameters doi:10.1093/mnras/staa3473 , archiveprefix =

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This paper cites China Phys.

Efficiently emulating distribution functions in gigaparsec volumes for varying cosmological parameters China Phys

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This paper cites a nen , E. and Kermiche , S. and Kiessling , A. and Kilbinger , M. and Kohley , R. and Kubik , B. and K \.

Efficiently emulating distribution functions in gigaparsec volumes for varying cosmological parameters a nen , E. and Kermiche , S. and Kiessling , A. and Kilbinger , M. and Kohley , R. and Kubik , B. and K \

Reference 11

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This paper cites R., 2009, @doi [ ] 10.1111/j.1365-2966.2009.15167.x , http://adsabs.harvard.edu/abs/2009MNRAS.398..607V 398, 607.

Efficiently emulating distribution functions in gigaparsec volumes for varying cosmological parameters R., 2009, @doi [ ] 10.1111/j.1365-2966.2009.15167.x , http://adsabs.harvard.edu/abs/2009MNRAS.398..607V 398, 607

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This paper cites R., 2009, @doi [ ] 10.1111/j.1365-2966.2009.15167.x , http://adsabs.harvard.edu/abs/2009MNRAS.398..607V 398, 607.

Efficiently emulating distribution functions in gigaparsec volumes for varying cosmological parameters R., 2009, @doi [ ] 10.1111/j.1365-2966.2009.15167.x , http://adsabs.harvard.edu/abs/2009MNRAS.398..607V 398, 607

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This paper cites The effect of active galactic nuclei feedback on the halo mass function.

Efficiently emulating distribution functions in gigaparsec volumes for varying cosmological parameters The effect of active galactic nuclei feedback on the halo mass function

Reference 14

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This paper cites S., & White, S.

Efficiently emulating distribution functions in gigaparsec volumes for varying cosmological parameters S., & White, S

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Efficiently emulating distribution functions in gigaparsec volumes for varying cosmological parameters Unresolved cited work

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This paper cites R., Cole S., Frenk C.

Efficiently emulating distribution functions in gigaparsec volumes for varying cosmological parameters R., Cole S., Frenk C

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This paper cites <i>Euclid</i> preparation.

Efficiently emulating distribution functions in gigaparsec volumes for varying cosmological parameters <i>Euclid</i> preparation

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This paper cites Overview of the Euclid mission.

Efficiently emulating distribution functions in gigaparsec volumes for varying cosmological parameters Overview of the Euclid mission

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Efficiently emulating distribution functions in gigaparsec volumes for varying cosmological parameters Real-time Bayesian inference at extreme scale: A digital twin for tsunami early warning applied to the Cascadia subduction zone

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Efficiently emulating distribution functions in gigaparsec volumes for varying cosmological parameters Unresolved cited work

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This paper cites and Shibahashi , H.

Efficiently emulating distribution functions in gigaparsec volumes for varying cosmological parameters and Shibahashi , H

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Efficiently emulating distribution functions in gigaparsec volumes for varying cosmological parameters Gebhardt, D

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Efficiently emulating distribution functions in gigaparsec volumes for varying cosmological parameters Unresolved cited work

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Efficiently emulating distribution functions in gigaparsec volumes for varying cosmological parameters Unresolved cited work

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This paper cites FIRE-2 Simulations: Physics versus Numerics in Galaxy Formation.

Efficiently emulating distribution functions in gigaparsec volumes for varying cosmological parameters FIRE-2 Simulations: Physics versus Numerics in Galaxy Formation

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This paper cites A., et al.

Efficiently emulating distribution functions in gigaparsec volumes for varying cosmological parameters A., et al

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This paper cites LSST: From Science Drivers to Reference Design and Anticipated Data Products , journal =.

Efficiently emulating distribution functions in gigaparsec volumes for varying cosmological parameters LSST: From Science Drivers to Reference Design and Anticipated Data Products , journal =

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Efficiently emulating distribution functions in gigaparsec volumes for varying cosmological parameters G., et al., 2025, @doi [ApJ] 10.3847/1538-4357/ae0334 , 994, 174

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Efficiently emulating distribution functions in gigaparsec volumes for varying cosmological parameters I., 2001, @doi [ ] 10.1046/j.1365-8711.2001.04416.x , http://adsabs.harvard.edu/abs/2001MNRAS.325..231O 325, 231

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Efficiently emulating distribution functions in gigaparsec volumes for varying cosmological parameters , keywords =

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This paper cites P., Ba J., 2014, in 3rd International Conference for Learning Representations.

Efficiently emulating distribution functions in gigaparsec volumes for varying cosmological parameters P., Ba J., 2014, in 3rd International Conference for Learning Representations

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Efficiently emulating distribution functions in gigaparsec volumes for varying cosmological parameters E., et al., 2024, @doi [ApJ] 10.3847/1538-4357/ad3d4a , 968, 11

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Efficiently emulating distribution functions in gigaparsec volumes for varying cosmological parameters The Astrophysical Journal , year = 2000, month = aug, volume = 538, number = 2, pages =

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This paper cites Environmental dependence of high-redshift galaxy evolution.

Efficiently emulating distribution functions in gigaparsec volumes for varying cosmological parameters Environmental dependence of high-redshift galaxy evolution

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Efficiently emulating distribution functions in gigaparsec volumes for varying cosmological parameters keywords =

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Efficiently emulating distribution functions in gigaparsec volumes for varying cosmological parameters A Hierarchy of Normalizing Flows for Modelling the Galaxy-Halo Relationship

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Efficiently emulating distribution functions in gigaparsec volumes for varying cosmological parameters C., et al., 2024, @doi [arXiv] 10.48550/arXiv.2411.13960

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Efficiently emulating distribution functions in gigaparsec volumes for varying cosmological parameters Unresolved cited work

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Efficiently emulating distribution functions in gigaparsec volumes for varying cosmological parameters G., Schaye, J., Bird, S., & Le Brun, A

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Efficiently emulating distribution functions in gigaparsec volumes for varying cosmological parameters Unresolved cited work

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Efficiently emulating distribution functions in gigaparsec volumes for varying cosmological parameters 2023, ApJ, 959, 136, doi: 10.3847/1538-4357/ad022a Oñorbe, J., Boylan-Kolchin, M., Bullock, J

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Efficiently emulating distribution functions in gigaparsec volumes for varying cosmological parameters Unresolved cited work

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Efficiently emulating distribution functions in gigaparsec volumes for varying cosmological parameters Teaching dark matter simulations to speak the halo language

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Efficiently emulating distribution functions in gigaparsec volumes for varying cosmological parameters Unresolved cited work

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Efficiently emulating distribution functions in gigaparsec volumes for varying cosmological parameters Simulation-based cosmological inference from optically-selected galaxy clusters with $\texttt{Capish}$

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Efficiently emulating distribution functions in gigaparsec volumes for varying cosmological parameters Unresolved cited work

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Efficiently emulating distribution functions in gigaparsec volumes for varying cosmological parameters year = 1974, month = feb, volume =

Reference 50

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Observation ed08f036-d86b-4d3f-b09c-90a4116313f9 · outbound

This paper cites Galaxy cluster count cosmology with simulation-based inference.

Efficiently emulating distribution functions in gigaparsec volumes for varying cosmological parameters Galaxy cluster count cosmology with simulation-based inference

Reference 51

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Efficiently emulating distribution functions in gigaparsec volumes for varying cosmological parameters Unresolved cited work

Reference 52

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Efficiently emulating distribution functions in gigaparsec volumes for varying cosmological parameters Unresolved cited work

Reference 53

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This paper cites S., Crain R.

Efficiently emulating distribution functions in gigaparsec volumes for varying cosmological parameters S., Crain R

Reference 54

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Observation 37dc42fc-b68c-457b-a66b-d25aac15b66a · outbound

This paper cites Somerville, R.

Efficiently emulating distribution functions in gigaparsec volumes for varying cosmological parameters Somerville, R

Reference 55

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Efficiently emulating distribution functions in gigaparsec volumes for varying cosmological parameters keywords =

Reference 56

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This paper cites H., Banerjee A., Collaboration t.

Efficiently emulating distribution functions in gigaparsec volumes for varying cosmological parameters H., Banerjee A., Collaboration t

Reference 57

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Efficiently emulating distribution functions in gigaparsec volumes for varying cosmological parameters Unresolved cited work

Reference 58

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Efficiently emulating distribution functions in gigaparsec volumes for varying cosmological parameters A., okas E

Reference 59

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Efficiently emulating distribution functions in gigaparsec volumes for varying cosmological parameters keywords =

Reference 60

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Observation 2097795f-eed0-4371-b537-151dfb597ec9 · outbound

This paper cites Toward a halo mass function for precision cosmology: the limits of universality.

Efficiently emulating distribution functions in gigaparsec volumes for varying cosmological parameters Toward a halo mass function for precision cosmology: the limits of universality

Reference 61

Resolution: verified exact; doi, observed 2026-05-10T04:25:06.282576Z

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Observation 334911d4-5c26-46ea-acef-ec26e1cfd254 · outbound

This paper cites R., White S.

Efficiently emulating distribution functions in gigaparsec volumes for varying cosmological parameters R., White S

Reference 62

Resolution: verified exact; doi, observed 2026-05-10T04:25:06.280386Z

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Efficiently emulating distribution functions in gigaparsec volumes for varying cosmological parameters keywords =

Reference 63

Resolution: verified exact; doi, observed 2026-05-10T04:25:06.278525Z

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Observation ebac557c-de43-4ba9-9aa2-89685f65ffd2 · outbound

This paper cites Villaescusa-Navarro, C.

Efficiently emulating distribution functions in gigaparsec volumes for varying cosmological parameters Villaescusa-Navarro, C

Reference 64

Resolution: verified exact; doi, observed 2026-05-10T04:25:06.285997Z

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This paper cites 2021, ApJ, 915, 71, doi:10.3847/1538-4357/abf7ba.

Efficiently emulating distribution functions in gigaparsec volumes for varying cosmological parameters 2021, ApJ, 915, 71, doi:10.3847/1538-4357/abf7ba

Reference 65

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This paper cites 2022, title The CAMELS Multifield Data Set: Learning the Universe's Fundamental Parameters with Artificial Intelligence , , 259, 61, 10.3847/1538-4365/ac5ab0.

Efficiently emulating distribution functions in gigaparsec volumes for varying cosmological parameters 2022, title The CAMELS Multifield Data Set: Learning the Universe's Fundamental Parameters with Artificial Intelligence , , 259, 61, 10.3847/1538-4365/ac5ab0

Reference 66

Resolution: verified exact; doi, observed 2026-05-10T04:25:06.259820Z

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Efficiently emulating distribution functions in gigaparsec volumes for varying cosmological parameters , keywords =

Reference 67

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This paper cites 2020, Nature Reviews Physics, 2, 42, 10.1038/s42254-019-0127-2.

Efficiently emulating distribution functions in gigaparsec volumes for varying cosmological parameters 2020, Nature Reviews Physics, 2, 42, 10.1038/s42254-019-0127-2

Reference 68

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This paper cites Precision determination of the mass function of dark matter halos.

Efficiently emulating distribution functions in gigaparsec volumes for varying cosmological parameters Precision determination of the mass function of dark matter halos

Reference 69

Resolution: metadata mismatch; doi, observed 2026-05-10T04:25:06.231806Z

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Efficiently emulating distribution functions in gigaparsec volumes for varying cosmological parameters The Connection between Galaxies and their Dark Matter Halos

Reference 70

Resolution: verified exact; doi, observed 2026-05-10T04:25:06.265670Z

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Efficiently emulating distribution functions in gigaparsec volumes for varying cosmological parameters M., et al., 2023, @doi [MNRAS] 10.1093/mnras/stac3280 , 519

Reference 71

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Efficiently emulating distribution functions in gigaparsec volumes for varying cosmological parameters keywords =

Reference 72

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Efficiently emulating distribution functions in gigaparsec volumes for varying cosmological parameters Unresolved cited work

Reference 73

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Efficiently emulating distribution functions in gigaparsec volumes for varying cosmological parameters , keywords =

Reference 74

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