Semi-supervised Graph Neural Networks for Pileup Noise Removal

The high instantaneous luminosity of the CERN Large Hadron Collider leads to multiple proton-proton interactions in the same or nearby bunch crossings (pileup). Advanced pileup mitigation algorithms are designed to remove this noise from pileup particles and improve the performance of crucial physics observables. This study implements a semi-supervised graph neural network for particle-level pileup noise removal, by identifying individual particles produced from pileup. The graph neural network is firstly trained on charged particles with known labels, which can be obtained from detector measurements on data or simulation, and then inferred on neutral particles for which such labels are missing. This semi-supervised approach does not depend on the ground truth information from simulation and thus allows us to perform training directly on experimental data. The performance of this approach is found to be consistently better than widely-used domain algorithms and comparable to the fully-supervised training using simulation truth information. The study serves as the first attempt at applying semi-supervised learning techniques to pileup mitigation, and opens up a new direction of fully data-driven machine learning pileup mitigation studies

Efficient ab initio many-body calculations based on sparse modeling of Matsubara Green's function

This lecture note reviews recently proposed sparse-modeling approaches for efficient ab initio many-body calculations based on the data compression of Green's functions. The sparse-modeling techniques are based on a compact orthogonal basis representation, intermediate representation (IR) basis functions, for imaginary-time and Matsubara Green's functions. A sparse sampling method based on the IR basis enables solving diagrammatic equations efficiently. We describe the basic properties of the IR basis, the sparse sampling method and its applications to ab initio calculations based on the GW approximation and the Migdal-Eliashberg theory. We also describe a numerical library for the IR basis and the sparse sampling method, irbasis, and provide its sample codes. This lecture note follows the Japanese review article [H. Shinaoka et al., Solid State Physics 56(6), 301 (2021)].Comment: 26 pages, 10 figure

Deep air learning: Interpolation, prediction, and feature analysis of fine-grained air quality

The interpolation, prediction, and feature analysis of fine-gained air quality are three important topics in the area of urban air computing. The solutions to these topics can provide extremely useful information to support air pollution control, and consequently generate great societal and technical impacts. Most of the existing work solves the three problems separately by different models. In this paper, we propose a general and effective approach to solve the three problems in one model called the Deep Air Learning (DAL). The main idea of DAL lies in embedding feature selection and semi-supervised learning in different layers of the deep learning network. The proposed approach utilizes the information pertaining to the unlabeled spatio-temporal data to improve the performance of the interpolation and the prediction, and performs feature selection and association analysis to reveal the main relevant features to the variation of the air quality. We evaluate our approach with extensive experiments based on real data sources obtained in Beijing, China. Experiments show that DAL is superior to the peer models from the recent literature when solving the topics of interpolation, prediction, and feature analysis of fine-gained air quality

Investigation of Reducing Interface State Density in 4H-SiC by Increasing Oxidation Rate

Detailed investigations of the pre-oxidation phosphorus implantation process are required to increase the oxidation rate in 4H-SiC metal-oxide-semiconductor (MOS) capacitors. This study focuses on the SiO2/SiC interface characteristics of pre-oxidation using phosphorus implantation methods. The inversion channel mobility of a metal-oxide-semiconductor field effect transistor (MOSFET) was decreased via a high interface state density and the coulomb-scattering mechanisms of the carriers. High-resolution transmission electron microscopy (HRTEM) and scanning transmission electron microscopy (STEM) were used to evaluate the SiO2/SiC interface’s morphology. According to the energy-dispersive X-ray spectrometry (EDS) results, it was found that phosphorus implantation reduced the accumulation of carbon at the SiO2/SiC interface. Moreover, phosphorus distributed on the SiO2/SiC interface exhibited a Gaussian profile, and the nitrogen concentration at the SiO2/SiC interface may be correlated with the content of phosphorus. This research presents a new approach for increasing the oxidation rate of SiC and reducing the interface state density

Thermal Pyrolysis Behavior and Decomposition Mechanism of Lignin Revealed by Stochastic Cluster Dynamics Simulations

It is crucial to comprehensively understand the thermal pyrolysis behavior of lignin and the underlying mechanisms to effectively convert lignin into high-value-added chemical compounds. However, the high complexity and heterogeneity of lignin make it challenging to comprehend its pyrolysis behavior by using conventional experimental methods. Here, we report the development of a computational model that integrates stochastic cluster dynamics to simulate lignin pyrolysis under different temperatures and heating rates. The lignin model molecules were created by leveraging experimental data to accurately represent the chemical structure and composition of lignin, which were used to further predict and validate the distribution of products formed during the fast and slow pyrolysis processes, respectively. Fast pyrolysis was found to be particularly favorable for the yield of liquid products leading to extensive depolymerization and fragmentation of the lignin macromolecules. During this process, the short residence time can promote the formation of phenols through the cracking of carboxylic acid and aldehyde and particularly inhibit the coupling reaction of free radicals into dimer compounds. In addition, the constitute bond breaking of functional groups on the benzene rings further promote the transformation between different varieties of high-value phenolic derivatives. Our investigation provides a comprehensive understanding of lignin pyrolysis and shed new lights on the development of effective strategies for biomass degradation

Comparative Investigation on the Performance of Modified System Poles and Traditional System Poles Obtained from PDC Data for Diagnosing the Ageing Condition of Transformer Polymer Insulation Materials

The life expectancy of a transformer is largely depended on the service life of transformer polymer insulation materials. Nowadays, several papers have reported that the traditional system poles obtained from polarization and depolarization current (PDC) data can be used to assess the condition of transformer insulation systems. However, the traditional system poles technique only provides limited ageing information for transformer polymer insulation. In this paper, the modified system poles obtained from PDC data are proposed to assess the ageing condition of transformer polymer insulation. The aim of the work is to focus on reporting a comparative investigation on the performance of modified system poles and traditional system poles for assessing the ageing condition of a transformer polymer insulation system. In the present work, a series of experiments have been performed under controlled laboratory conditions. The PDC measurement data, degree of polymerization (DP) and moisture content of the oil-immersed polymer pressboard specimens were carefully monitored. It is observed that, compared to the relationships between traditional system poles and DP values, there are better correlations between the modified system poles and DP values, because the modified system poles can obtain much more ageing information on transformer polymer insulation. Therefore, the modified system poles proposed in the paper are more suitable for the diagnosis of the ageing condition of transformer polymer insulation

Rational design of Two-dimensional Binary Polymers from Hetero Triangulenes for Photocatalytic Water Splitting

Based on first principles calculations, we report the design of three two-dimensional (2D) binary honeycomb-kagome polymers composed of B- and N-centered heterotriangulenes with a periodically alternate arrangement as in hexagonal boron nitride. The 2D binary polymers with donor-acceptor characteristics, are semiconductors with a direct band gap of 1.98-2.28 eV. The enhanced in-plane electron conjugation contributes to high charge carrier mobilities for both electrons and holes, about 6.70 and 0.24 × 103 cm2 V-1 s-1, respectively, for the 2D binary polymer with carbonyl bridges (2D CTPAB). With appropriate band edge alignment to match the water redox potentials and pronounced light adsorption for the ultraviolet and visible range of spectra, 2D CTPAB is predicted to be an effective photocatalyst/photoelectrocatalyst to promote overall water splitting