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Inferring Cosmological Parameters on SDSS via Domain-Generalized Neural Networks and Lightcone Simulations

JY Lee, J Kim, M Jung, BK Oh, Y Jo, S Park… - arXiv preprint arXiv …, 2024 - arxiv.org
Astrophysics paper astro-ph.CO Suggest

… ABSTRACT We present a proof-of-concept simulation-based inference on Ωm and σ8 from the SDSS BOSS LOWZ NGC catalog using neural networks and domain …

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@article{2409.02256v1,
Author        = {Jun-Young Lee and Ji-hoon Kim and Minyong Jung and Boon Kiat Oh and Yongseok Jo and Songyoun Park and Jaehyun Lee and Yuan-Sen Ting and Ho Seong Hwang},
Title         = {Inferring Cosmological Parameters on SDSS via Domain-Generalized Neural
  Networks and Lightcone Simulations},
Eprint        = {2409.02256v1},
DOI           = {10.3847/1538-4357/ad73d4},
ArchivePrefix = {arXiv},
PrimaryClass  = {astro-ph.CO},
Abstract      = {We present a proof-of-concept simulation-based inference on $\Omega_{\rm m}$
and $\sigma_{8}$ from the SDSS BOSS LOWZ NGC catalog using neural networks and
domain generalization techniques without the need of summary statistics. Using
rapid lightcone simulations, ${\rm L{\scriptsize -PICOLA}}$, mock galaxy
catalogs are produced that fully incorporate the observational effects. The
collection of galaxies is fed as input to a point cloud-based network,
${\texttt{Minkowski-PointNet}}$. We also add relatively more accurate ${\rm
G{\scriptsize ADGET}}$ mocks to obtain robust and generalizable neural
networks. By explicitly learning the representations which reduces the
discrepancies between the two different datasets via the semantic alignment
loss term, we show that the latent space configuration aligns into a single
plane in which the two cosmological parameters form clear axes. Consequently,
during inference, the SDSS BOSS LOWZ NGC catalog maps onto the plane,
demonstrating effective generalization and improving prediction accuracy
compared to non-generalized models. Results from the ensemble of 25
independently trained machines find $\Omega_{\rm m}=0.339 \pm 0.056$ and
$\sigma_{8}=0.801 \pm 0.061$, inferred only from the distribution of galaxies
in the lightcone slices without relying on any indirect summary statistics. A
single machine that best adapts to the ${\rm G{\scriptsize ADGET}}$ mocks
yields a tighter prediction of $\Omega_{\rm m}=0.282 \pm 0.014$ and
$\sigma_{8}=0.786 \pm 0.036$. We emphasize that adaptation across multiple
domains can enhance the robustness of the neural networks in observational
data.},
Year          = {2024},
Month         = {Sep},
Url           = {http://arxiv.org/abs/2409.02256v1},
File          = {2409.02256v1.pdf}
}

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