13 papers
Deep Learning for Astrophysics: An Open Textbook from the NASA Cosmic Origins AI/ML Science and Technology Interest Group
Yuan-Sen Ting, Digvijay Wadekar, Phill Cargile +21
Recent community assessments identify education as a principal barrier to adopting modern machine learning in astronomy. We present Deep Learning for Astrophysics, a freely availab…
On the potential for inhomogeneities to mimic an evolving dark energy
Hayley J. Macpherson, Georgios Valogiannis
In this work we explore the ability of inhomogeneities to result in an apparent dynamical evolution of dark energy. The idea that inhomogeneities may alter the expansion history of…
Large-Scale Structure Probes of the Post-Inflationary Axiverse
Marco Gorghetto, Sokratis Trifinopoulos, Georgios Valogiannis
We study the cosmology of axions in the post-inflationary scenario, where random initial conditions and the ensuing string-domain-wall network generate an isocurvature power spectr…
Probing Physics Beyond the Standard Model through Combined Analyses of Next-Generation Type Ia Supernova, CMB, and BAO Surveys
Srinivasan Raghunathan, Ayan Mitra, Nikolina Å arÄeviÄ +11
Observations of Type Ia supernovae (\sne), which probe the late Universe, together with baryon acoustic oscillations (BAO) and the cosmic microwave background (CMB), which probe th…
Setting SAIL: Leveraging Scientist-AI-Loops for Rigorous Visualization Tools
Nico Schuster, Andrés N. Salcedo, Simon Bouchard +8
Scientists across all disciplines share a common challenge: the divide between their theoretical knowledge and the specialized skills and time needed to build interactive tools to…
Constraining Power of Wavelet vs. Power Spectrum Statistics for CMB Lensing and Weak Lensing with Learned Binning
Kyle Boone, Georgios Valogiannis, Marco Gatti +1
We present forecasts for constraints on the matter density () and the amplitude of matter density fluctuations at 8hMpc () from CMB lensing convergence maps and…