The 3rd Shanghai Assembly on Cosmology and Structure Formation

Session

Poster

Nov 17, 2025, 3:55 PM

Tsung-Dao Lee Institute

65. Cosmological gains from the multi-tracer analysis between RSD and kSZ effects of next generation surveys

Zhuoyang Li (Tsinghua University)

Poster

We study the synergy of redshift space distortion (RSD) and kinetic Sunyaev Zel’dovich (kSZ) effects from the next stage cosmological surveys in this paper. The next generation of galaxy and CMB surveys are considered, and we compare the behaviors of the traditional slit-based spectroscopic galaxy survey (e.g. DESI) and the slitless spectrosocpic
galaxy survey (CSST as an example). For a more...

14. Dark Matter Halo Shapes as Probes of Microphysical Models: Insights from AIDA-TNG Simulations

Carlo Giocoli (INAF - OAS Bologna)

Poster

Precision cosmology is increasingly constrained by tensions in structure formation and the nature of dark matter. In this context, the internal structure of dark matter haloes offers a sensitive probe of non-standard dark matter physics. I will present results from the AIDA-TNG project, a suite of cosmological hydrodynamical simulations based on the IllustrisTNG model and extended to...

37. Does General Relativity hold in galaxy scales? A test at a z ~0.3 elliptical lens galaxy

Carlos Carneiro (Universidade Federal do Rio Grande do Sul)

Poster

General Relativity (GR) has been extensively tested at Solar System scales; in recent decades, galaxy-scale tests have become popular. In particular, recent works have focused on the so-called gravitational slip parameter $\eta_\text{PPN}$, which quantifies the spatial curvature generated per unit mass. Under GR, and assuming a vanishing anisotropic stress tensor, $\eta_\text{PPN} = 1$. In...

19. Mapping Dark Matter to the Lyman-$\alpha$ Forest with Neural Networks

Dr Bhaskar Arya (IIT Kanpur)

Poster

We present a machine learning framework that uses neural networks to predict Lyman-alpha forest spectra from dark matter density fields. Trained on simulations with varying cosmological parameters, our network learns the complex, non-linear transformation from the underlying matter distribution and velocity fields to transmitted flux. We demonstrate that the network accurately reconstructs the...