Study of Exclusive $B to πe^+ ν_e$ Decays with Hadronic Full-event-interpretation Tagging in 189.3 fb$^{-1}$ of Belle II Data

Kavli Affiliate: T. Higuchi | First 5 Authors: Belle II Collaboration, F. Abudinén, I. Adachi, K. Adamczyk, L. Aggarwal | Summary: We present a reconstruction of the semileptonic decays $B^0 to pi^- e^+ nu_e$ and $B^+ to pi^0 e^+ nu_e$ in a sample corresponding to 189.3 fb$^{-1}$ of Belle II data, using events where the […]


Continue.. Study of Exclusive $B to πe^+ ν_e$ Decays with Hadronic Full-event-interpretation Tagging in 189.3 fb$^{-1}$ of Belle II Data

Study of Exclusive $B to πe^+ ν_e$ Decays with Hadronic Full-event-interpretation Tagging in 189.3 fb$^{-1}$ of Belle II Data

Kavli Affiliate: T. Higuchi | First 5 Authors: Belle II Collaboration, F. Abudinén, I. Adachi, K. Adamczyk, L. Aggarwal | Summary: We present a reconstruction of the semileptonic decays $B^0 to pi^- e^+ nu_e$ and $B^+ to pi^0 e^+ nu_e$ in a sample corresponding to 189.3 fb$^{-1}$ of Belle II data, using events where the […]


Continue.. Study of Exclusive $B to πe^+ ν_e$ Decays with Hadronic Full-event-interpretation Tagging in 189.3 fb$^{-1}$ of Belle II Data

Measurement of the branching fraction for the decay $B to K^{ast}(892)ell^+ell^-$ at Belle II

Kavli Affiliate: T. Higuchi | First 5 Authors: F. Abudinén, I. Adachi, R. Adak, K. Adamczyk, L. Aggarwal | Summary: We report a measurement of the branching fraction of $B to K^{ast}(892)ell^+ell^-$ decays, where $ell^+ell^- = mu^+mu^-$ or $e^+e^-$, using electron-positron collisions recorded at an energy at or near the $Upsilon(4S)$ mass and corresponding to […]


Continue.. Measurement of the branching fraction for the decay $B to K^{ast}(892)ell^+ell^-$ at Belle II

Impact of late-time neutrino emission on the Diffuse Supernova Neutrino Background

Kavli Affiliate: Shunsaku Horiuchi | First 5 Authors: Nick Ekanger, Shunsaku Horiuchi, Kei Kotake, Kohsuke Sumiyoshi, | Summary: In the absence of high-statistics supernova neutrino measurements, estimates of the diffuse supernova neutrino background (DSNB) hinge on the precision of simulations of core-collapse supernovae (CCSNe). Understanding the cooling phase of protoneutron star (PNS) evolution ($gtrsim1,{rm s}$ […]


Continue.. Impact of late-time neutrino emission on the Diffuse Supernova Neutrino Background

Impact of late-time neutrino emission on the diffuse supernova neutrino background

Kavli Affiliate: Shunsaku Horiuchi | First 5 Authors: Nick Ekanger, Shunsaku Horiuchi, Kei Kotake, Kohsuke Sumiyoshi, | Summary: In the absence of high-statistics supernova neutrino measurements, estimates of the diffuse supernova neutrino background (DSNB) hinge on the precision of simulations of core-collapse supernovae. Understanding the cooling phase of protoneutron star (PNS) evolution ($gtrsim1,{rm s}$ after […]


Continue.. Impact of late-time neutrino emission on the diffuse supernova neutrino background

EMPRESS. IX. Extremely Metal-Poor Galaxies are Very Gas-Rich Dispersion-Dominated Systems: Will JWST Witness Gaseous Turbulent High-z Primordial Galaxies?

Kavli Affiliate: Masahiro Kawasaki | First 5 Authors: Yuki Isobe, Masami Ouchi, Kimihiko Nakajima, Shinobu Ozaki, Nicolas Bouche | Summary: We present kinematics of 6 local extremely metal-poor galaxies (EMPGs) with low metallicities ($0.016-0.098 Z_{odot}$) and low stellar masses ($10^{4.7}-10^{7.6} M_{odot}$). Taking deep medium-high resolution ($Rsim7500$) integral-field spectra with 8.2-m Subaru, we resolve the small […]


Continue.. EMPRESS. IX. Extremely Metal-Poor Galaxies are Very Gas-Rich Dispersion-Dominated Systems: Will JWST Witness Gaseous Turbulent High-z Primordial Galaxies?

EMPRESS. IX. Extremely Metal-Poor Galaxies are Very Gas-Rich Dispersion-Dominated Systems: Will JWST Witness Gaseous Turbulent High-z Primordial Galaxies?

Kavli Affiliate: Masahiro Kawasaki | First 5 Authors: Yuki Isobe, Masami Ouchi, Kimihiko Nakajima, Shinobu Ozaki, Nicolas Bouche | Summary: We present kinematics of 6 local extremely metal-poor galaxies (EMPGs) with low metallicities ($0.016-0.098 Z_{odot}$) and low stellar masses ($10^{4.7}-10^{7.6} M_{odot}$). Taking deep medium-high resolution ($Rsim7500$) integral-field spectra with 8.2-m Subaru, we resolve the small […]


Continue.. EMPRESS. IX. Extremely Metal-Poor Galaxies are Very Gas-Rich Dispersion-Dominated Systems: Will JWST Witness Gaseous Turbulent High-z Primordial Galaxies?

Field Level Neural Network Emulator for Cosmological N-body Simulations

Kavli Affiliate: David N. Spergel | First 5 Authors: Drew Jamieson, Yin Li, Renan Alves de Oliveira, Francisco Villaescusa-Navarro, Shirley Ho | Summary: We build a field level emulator for cosmic structure formation that is accurate in the nonlinear regime. Our emulator consists of two convolutional neural networks trained to output the nonlinear displacements and […]


Continue.. Field Level Neural Network Emulator for Cosmological N-body Simulations

Simple lessons from complex learning: what a neural network model learns about cosmic structure formation

Kavli Affiliate: David N. Spergel | First 5 Authors: Drew Jamieson, Yin Li, Siyu He, Francisco Villaescusa-Navarro, Shirley Ho | Summary: We train a neural network model to predict the full phase space evolution of cosmological N-body simulations. Its success implies that the neural network model is accurately approximating the Green’s function expansion that relates […]


Continue.. Simple lessons from complex learning: what a neural network model learns about cosmic structure formation

Simple lessons from complex learning: what a neural network model learns about cosmic structure formation

Kavli Affiliate: David N. Spergel | First 5 Authors: Drew Jamieson, Yin Li, Siyu He, Francisco Villaescusa-Navarro, Shirley Ho | Summary: We train a neural network model to predict the full phase space evolution of cosmological N-body simulations. Its success implies that the neural network model is accurately approximating the Green’s function expansion that relates […]


Continue.. Simple lessons from complex learning: what a neural network model learns about cosmic structure formation