End-to-end optimisation of HEP triggers

Kavli Affiliate: David W. Miller | Summary:High-energy physics experiments face extreme data rates, requiring real-time trigger systems to reduce event throughput while preserving sensitivity to rare processes. Trigger systems are typically constructed as modular chains of sequentially optimised algorithms, including machine learning models. Each algorithm is optimised for a specific local objective with no guarantee […]


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Correlation between nuclear isospin asymmetry and $α$-particle preformation probability for superheavy nuclei from a Bayesian inference

Kavli Affiliate: Wei Gao| Summary:In the study of $α$ decay within the superheavy nuclear region ($Z geq 90$ and $N geq 140$), the $α$-particle preformation probability $P_α$ serves as a crucial physical quantity linking nuclear structure to decay observables. We introduce a phenomenological model incorporating the decay energy $Q_α$, mass number $A$, orbital angular momentum […]


Continue.. Correlation between nuclear isospin asymmetry and $α$-particle preformation probability for superheavy nuclei from a Bayesian inference

Correlation between nuclear isospin asymmetry and $α$-particle preformation probability for superheavy nuclei from a Bayesian inference

Kavli Affiliate: Wei Gao| Summary:In the study of $α$ decay within the superheavy nuclear region ($Z geq 90$ and $N geq 140$), the $α$-particle preformation probability $P_α$ serves as a crucial physical quantity linking nuclear structure to decay observables. We introduce a phenomenological model incorporating the decay energy $Q_α$, mass number $A$, orbital angular momentum […]


Continue.. Correlation between nuclear isospin asymmetry and $α$-particle preformation probability for superheavy nuclei from a Bayesian inference

Bayesian neural network with autoencoder for model-based description of $α$-particle preformation factor

Kavli Affiliate: Wei Gao| Summary:$α$ decay is an important probe for studying the structure of heavy and superheavy nuclei, in which the $α$-particle preformation ($P_α$) is a key physical quantity for describing decay half-lives. This work develops a hybrid framework that integrates Bayesian neural networks with autoencoder (BNN-Auto), combined with the cosh potential (CPT), to […]


Continue.. Bayesian neural network with autoencoder for model-based description of $α$-particle preformation factor

Centrifugal-corrected harmonic oscillator model for spherical proton emitters

Kavli Affiliate: Wei Gao| Summary:In the present work, we propose an improved harmonic oscillator model to systematically evaluate the proton radioactivity half-lives in spherical nuclei, incorporating centrifugal potential effects. By fitting the experimental data, the centrifugal parameter $d = 0.143$ for the correction term $dl(l+1)$ and nuclear potential depth $V_0 = 62.4$ MeV are obtained. […]


Continue.. Centrifugal-corrected harmonic oscillator model for spherical proton emitters

Centrifugal-corrected harmonic oscillator model for spherical proton emitters

Kavli Affiliate: Wei Gao| Summary:In the present work, we propose an improved harmonic oscillator model to systematically evaluate the proton radioactivity half-lives in spherical nuclei, incorporating centrifugal potential effects. By fitting the experimental data, the centrifugal parameter $d = 0.143$ for the correction term $dl(l+1)$ and nuclear potential depth $V_0 = 62.4$ MeV are obtained. […]


Continue.. Centrifugal-corrected harmonic oscillator model for spherical proton emitters

Centrifugal-corrected harmonic oscillator model for spherical proton emitters

Kavli Affiliate: Wei Gao| Summary:In the present work, we propose an improved harmonic oscillator model to systematically evaluate the proton radioactivity half-lives in spherical nuclei, incorporating centrifugal potential effects. By fitting the experimental data, the centrifugal parameter $d = 0.143$ for the correction term $dl(l+1)$ and nuclear potential depth $V_0 = 62.4$ MeV are obtained. […]


Continue.. Centrifugal-corrected harmonic oscillator model for spherical proton emitters

DeepConf: Machine Learning Conformer Reconstruction of Biomolecules from Scanning Tunneling Microscopy Images

Kavli Affiliate: Stephan Rauschenbach | Summary:Improving the detailed understanding of the underlying properties and functions of biomolecules has recently attracted growing interest, enabled by the possibility of real-space imaging of single, intact macromolecules using Scanning Tunneling Microscopy (STM) in combination with electrospray ion beam deposition and soft landing. This combination provides key insights into biomolecular […]


Continue.. DeepConf: Machine Learning Conformer Reconstruction of Biomolecules from Scanning Tunneling Microscopy Images

Reprogramming of neuronal genome function and phenotype by astrocytes

Kavli Affiliate: Michael Beer | Authors: Boxun Li, Kevin Hagy, Alexias Safi, Michael A. Beer, Alejandro Barrera, Sara Geraghty, Ruhi Rai, Alyssa N. Pederson, Samuel J. Reisman, Michael I. Love, Patrick F. Sullivan, Cagla Eroglu, Gregory E. Crawford and Charles A. Gersbach | Summary: Heterotypic cell-cell interactions are critical to governing cellular physiology, disease progression, […]


Continue.. Reprogramming of neuronal genome function and phenotype by astrocytes

To What Extent Are Star Cluster Ages Encoded in Their Environments? Exploring the Spatial Distribution of Age-Related Information with PHANGS-HST Imaging and Convolutional Neural Networks

Kavli Affiliate: Andrew Vanderburg | Summary:The environments around star clusters evolve as stellar feedback reshapes the interstellar medium and dynamical processes reorganize the structure of the surrounding stellar field. As approximately single-age populations, star clusters can serve as clocks to trace these environmental changes. In this exploratory study, we test whether convolutional neural networks (CNNs) […]


Continue.. To What Extent Are Star Cluster Ages Encoded in Their Environments? Exploring the Spatial Distribution of Age-Related Information with PHANGS-HST Imaging and Convolutional Neural Networks