Neutral vs Ion Linewidths in Barnard 5: Evidence for Penetration by MHD Waves

Kavli Affiliate: Sarah E. Church | First 5 Authors: Jaime E. Pineda, Anika Schmiedeke, Paola Caselli, Steven W. Stahler, David T. Frayer | Summary: Dense cores are the final place where turbulence is dissipated. It has been proposed from theoretical arguments that the non-thermal velocity dispersion should be narrower both for molecular ions (compared to […]


Continue.. Neutral vs Ion Linewidths in Barnard 5: Evidence for Penetration by MHD Waves

Composition of Terrestrial Exoplanet Atmospheres from Meteorite Outgassing Experiments

Kavli Affiliate: Laura Schaefer | First 5 Authors: Maggie A. Thompson, Myriam Telus, Laura Schaefer, Jonathan J. Fortney, Toyanath Joshi | Summary: Terrestrial exoplanets likely form initial atmospheres through outgassing during and after accretion, although there is currently no first-principles understanding of how to connect a planet’s bulk composition to its early atmospheric properties. Important […]


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Fast and Accurate Emulation of the SDO/HMI Stokes Inversion with Uncertainty Quantification

Kavli Affiliate: J. Todd Hoeksema | Summary:The Helioseismic and Magnetic Imager (HMI) onboard NASA’s Solar Dynamics Observatory (SDO) produces estimates of the photospheric magnetic field which are a critical input to many space weather modelling and forecasting systems. The magnetogram products produced by HMI and its analysis pipeline are the result of a per-pixel optimization […]


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The HST See Change Program: I. Survey Design, Pipeline, and Supernova Discoveries

Kavli Affiliate: Risa Wechsler | First 5 Authors: Brian Hayden, David Rubin, Kyle Boone, Greg Aldering, Jakob Nordin | Summary: The See Change survey was designed to make $z>1$ cosmological measurements by efficiently discovering high-redshift Type Ia supernovae (SNe Ia) and improving cluster mass measurements through weak lensing. This survey observed twelve galaxy clusters with […]


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Search for anisotropic gravitational-wave backgrounds using data from Advanced LIGO and Advanced Virgo’s first three observing runs

Kavli Affiliate: Chao-Lin Kuo | First 5 Authors: The LIGO Scientific Collaboration, the Virgo Collaboration, the KAGRA Collaboration, R. Abbott, T. D. Abbott | Summary: We report results from searches for anisotropic stochastic gravitational-wave backgrounds using data from the first three observing runs of the Advanced LIGO and Advanced Virgo detectors. For the first time, […]


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Effects of overlapping sources on cosmic shear estimation: Statistical sensitivity and pixel-noise bias

Kavli Affiliate: Patricia R. Burchat | First 5 Authors: Javier Sanchez, Ismael Mendoza, David P. Kirkby, Patricia R. Burchat, | Summary: In Stage-IV imaging surveys, a significant amount of the cosmologically useful information is due to sources whose images overlap with those of other sources on the sky. The cosmic shear signal is primarily encoded […]


Continue.. Effects of overlapping sources on cosmic shear estimation: Statistical sensitivity and pixel-noise bias

Effects of overlapping sources on cosmic shear estimation: Statistical sensitivity and pixel-noise bias

Kavli Affiliate: Patricia Burchat | Summary:In Stage-IV imaging surveys, a significant amount of the cosmologically useful information is due to sources whose images overlap with those of other sources on the sky. The cosmic shear signal is primarily encoded in the estimated shapes of observed galaxies and thus directly impacted by overlaps. We introduce a […]


Continue.. Effects of overlapping sources on cosmic shear estimation: Statistical sensitivity and pixel-noise bias

DeepSZ: Identification of Sunyaev-Zel’dovich Galaxy Clusters using Deep Learning

Kavli Affiliate: W. L. Kimmy Wu | Summary:Galaxy clusters identified from the Sunyaev Zel’dovich (SZ) effect are a key ingredient in multi-wavelength cluster-based cosmology. We present a comparison between two methods of cluster identification: the standard Matched Filter (MF) method in SZ cluster finding and a method using Convolutional Neural Networks (CNN). We further implement […]


Continue.. DeepSZ: Identification of Sunyaev-Zel’dovich Galaxy Clusters using Deep Learning