9 papers
Full Nonlinear Velocity Reconstruction With Transformer and Ensemble Tree Machine Learning
Yulin Gong, Rachel Bean
Accurate reconstruction of peculiar velocities from galaxy positions is important for probing the motion and evolution of large scale structure. They are sensitive to the cosmologi…
Detection of the Pairwise Kinematic Sunyaev-Zel'dovich Effect and Pairwise Velocity with DESI DR1 Galaxies and ACT DR6 and Planck CMB Data
Yulin Gong, Patricio A. Gallardo, Rachel Bean +50
We present a 9.3-sigma detection of the pairwise kinematic Sunyaev-Zeldovich (kSZ) effect by combining a sample of 913,286 Luminous Red Galaxies (LRGs) from the Dark Energy Spectro…
Measurements of the Thermal Sunyaev-Zel'dovich Effect with ACT and DESI Luminous Red Galaxies
R. Henry Liu, Simone Ferraro, Emmanuel Schaan +54
Cosmic Microwave Background (CMB) photons scatter off the free-electron gas in galaxies and clusters, allowing us to use the CMB as a backlight to probe the gas in and around low-r…
Iskay2: Signal Extraction of the Kinematic Sunyaev-Zel'dovich Effect Through The Pairwise Estimator. Pipeline and Validation
Patricio A. Gallardo, Yulin Gong, Boryana Hadzhiyska +1
The peculiar motions of massive halos probe the distribution of matter in the universe, the gravitational potential, and the history of cosmic structure growth. The kinematic Sunya…
Probing cosmic velocities with the pairwise kinematic Sunyaev-Zel'dovich signal in DESI Bright Galaxy Sample DR1 and ACT DR6
B. Hadzhiyska, Y. Gong, Y. Hsu +55
We present a measurement of the pairwise kinematic Sunyaev-Zel'dovich (kSZ) signal using the Dark Energy Spectroscopic Instrument (DESI) Bright Galaxy Sample (BGS) Data Release 1 (…
Cluster optical depth and pairwise velocity estimation using machine learning
Yulin Gong, Rachel Bean
We apply two machine learning methods, a CNN deep-leaning model and a gradient-boosting decision tree, to estimate individual cluster optical depths from observed properties derive…