Related papers: Deeply Learning Deep Inelastic Scattering Kinemati…
The first measurements of the cross sections for neutral and charged current deep inelastic scattering in e+p collisions with longitudinally polarised positron beams are presented. The total cross section for e+p charged current deep…
We present the development of a digital twin-based reconstruction framework for dielectric laser acceleration (DLA) based on machine-learning-assisted inversion of single-shot electron energy spectra. DLA as a promising candidate for…
The existing data obtained in deep-inelastic scattering (DIS) experiments in the kinematical region $x_B \approxle 10^{-2}$ and $Q^2 \ge 5 {GeV}^2$ are examined to gain further insight into the dynamics of interacting gluons. It is shown…
Three recent breakthroughs due to AI in arts and science serve as motivation: An award winning digital image, protein folding, fast matrix multiplication. Many recent developments in artificial neural networks, particularly deep learning…
Deep learning, as a highly efficient method for metasurface inverse design, commonly use simulation data to train deep neural networks (DNNs) that can map desired functionalities to proper metasurface designs. However, the assumptions and…
This paper presents a deep learning-based method for dynamic gear measurement and uncertainty estimation. A twin-system proposed on the Unity platform is utilized to flexibly generate diverse simulated datasets. This effectively addresses…
Traditional learning approaches proposed for controlling quadrotors or helicopters have focused on improving performance for specific trajectories by iteratively improving upon a nominal controller, for example learning from demonstrations,…
Since its commissioning in 1991, the ep collider HERA has been running successfully for almost a decade without stopping improving its performance. In this report, the inclusive cross section and structure function measurements for the deep…
We propose an end-to-end deep learning framework that comprehensively solves the inverse wave scattering problem across all length scales. Our framework consists of the newly introduced wide-band butterfly network coupled with a simple…
High energy scattering processes of charged particles are accompanied by radiation of hard photons. Emission collinear to the incident particles, which leads to a reduction of the effective beam energy, and the possibility to directly…
We present track reconstruction algorithms based on deep learning, tailored to overcome specific central challenges in the field of hadron physics. Two approaches are used: (i) deep learning (DL) model known as fully-connected neural…
Recent attempts to use deep learning for super-resolution reconstruction of turbulent flows have used supervised learning, which requires paired data for training. This limitation hinders more practical applications of super-resolution…
Real time calculation of inverse kinematics (IK) with dynamically stable configuration is of high necessity in humanoid robots as they are highly susceptible to lose balance. This paper proposes a methodology to generate joint-space…
We present measurements of the structure function \Ft\ in $e^+p$ scattering at HERA in the range $3.5\;\Gevsq < \qsd < 5000\;\Gevsq$. A new reconstruction method has allowed a significant improvement in the resolution of the kinematic…
The purpose of this study is to develop a computationally efficient deep learning based control framework for high degree of freedom exoskeleton robots to address the real time computational limitations associated with conventional model…
We investigate recurrent neural networks with asymmetric interactions and demonstrate that the inclusion of self-couplings or sparse excitatory inter-module connections leads to the emergence of a densely connected manifold of dynamically…
Accurate observation of two or more particles within a very narrow time window has always been a challenge in modern physics. It creates the possibility of correlation experiments, such as the ground-breaking Hanbury Brown-Twiss experiment,…
We investigate possible search strategies for QCD-instanton induced processes at HERA in the deep-inelastic scattering (DIS) regime. Our study is based on the Monte Carlo generator QCDINS for instanton-induced events and the standard…
In this article, we use artificial intelligence algorithms to show how to enhance the resolution of the elementary particle track fitting in inhomogeneous dense detectors, such as plastic scintillators. We use deep learning to replace more…
Applying artificial intelligence to scientific problems (namely AI for science) is currently under hot debate. However, the scientific problems differ much from the conventional ones with images, texts, and etc., where new challenges…