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Future Cosmology: New Physics and Opportunity from the China Space Station Telescope (CSST)

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arxiv 2501.15023 v2 pith:LUJAJNCP submitted 2025-01-25 astro-ph.CO

Yan Gong , Haitao Miao , Xingchen Zhou , Qi Xiong , Yingxiao Song , Yuer Jiang , Minglin Wang , Junhui Yan
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Beichen Wu Furen Deng Xuelei Chen Zuhui Fan Yipeng Jing Xiaohu Yang Hu Zhan
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The China Space Station Telescope (CSST) is the next-generation Stage~IV survey telescope. It can simultaneously perform multi-band imaging and slitless spectroscopic wide- and deep-field surveys in ten years and an ultra-deep field (UDF) survey in two years, which are suitable for cosmological studies. Here we review several CSST cosmological probes, such as weak gravitational lensing, two-dimensional (2D) and three-dimensional (3D) galaxy clustering, galaxy cluster abundance, cosmic void, Type Ia supernovae (SNe Ia), and baryonic acoustic oscillations (BAO), and explore their capabilities and prospects in discovering new physics and opportunities in cosmology. We find that CSST will measure the matter distribution from small to large scales and the expansion history of the Universe with extremely high accuracy, which can provide percent-level stringent constraints on the properties of dark energy and dark matter and precisely test the theories of gravity.

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Forward citations

Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Hermes - Towards an Optimal High-Performance Algorithm for Cosmic Statistics of Large Data Sets

    astro-ph.CO 2026-07 conditional novelty 6.0 of 10

    Hermes/PyHermes reconstructs catalogues in a scaling-function basis and unifies CIC, 2PCF, 3PCF, marked, and operator-based cosmic statistics as reusable window operations with FFT/MPI/GPU scaling.

  2. CSST Cosmological Emulator II: Generalized Accurate Halo Mass Function Emulation

    astro-ph.CO 2025-06 conditional novelty 6.0 of 10

    A new emulator predicts cumulative dark matter halo mass functions for three mass definitions with claimed 2-10% accuracy from z=0 to 3, based on the Kun simulation suite.

  3. Supernova Classification using the Recurrent Neural Network in the CSST Ultra-Deep Field Survey

    astro-ph.CO 2025-11 conditional novelty 4.0 of 10

    In mock CSST-UDF data, an LSTM classifier plus JLA-like cuts yields a >99.5% pure Type Ia sample and recovers Ω_M and w to 14% and 18% in a flat wCDM model.

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