New Perspectives 2024

Session

Cosmology

9 Jul 2024, 13:15

One West (Fermi National Accelerator Laboratory)

Conveners

Cosmology

• Gray Putnam ()

Contribution list Timetable

111. CMB in 10 Minutes

Lauren Saunders

09/07/2024, 13:15

The Cosmic Microwave Background (CMB) is one of our most powerful tools for studying fundamental physics. By observing the CMB, we can learn about the energy scale of inflation, probe the possibilities of particles beyond the Standard Model, and understand more about dark matter and dark energy. This talk will provide an overview of the exciting work being done by the CMB group at Fermilab to...

83. Population-level Dark Energy Constraints from Strong Gravitational Lensing using Simulation-Based Inference

Sreevani Jarugula (Fermilab)

09/07/2024, 13:30

In this work, we present a scalable approach for inferring the dark energy equation-of-state parameter ($w$) from a population of strong gravitational lens images using Simulation-Based Inference (SBI). Strong gravitational lensing offers crucial insights into cosmology, but traditional Monte Carlo methods for cosmological inference are computationally prohibitive and inadequate for processing...

89. Cosmic Millicharge Background and Reheating Probes

Yu-Dai Tsai (University of California, Irvine)

09/07/2024, 13:45

We demonstrate that the searches for dark sector particles can provide probes of reheating scenarios, focusing on the cosmic millicharge background produced in the early universe. We discuss two types of millicharge particles (mCPs): either with, or without, an accompanying dark photon. These two types of mCPs have distinct theoretical motivations and cosmological signatures. We discuss...

118. Studying Galaxy Mergers Using Domain Adaptation

Rohan Venkat

09/07/2024, 14:00

The ability to capture important features in imaging data through deep learning is of paramount importance in astronomy. Deep learning allows for the reduction of manual labor necessary in combing through thousands of astronomical images, however, small differences between astronomical datasets make the performance of deep learning models trained on one data set substantially different than on...