相关论文: An autoencoder for compressing angle-resolved phot…

We study how time- and angle-resolved photoemission (tr-ARPES) reveals the dynamics of BCS-type, s-wave superconducting systems with time-varying order parameters. Approximate methods are discussed, based on previous approaches to either…

Deep learning has revolutionized computer vision, yet a major gap persists between complex, data-hungry deep learning models and the practical demands of state-of-the-art scientific measurements. To fundamentally bridge this gap, we propose…

In recent years, distinct machine learning (ML) models have been separately used for feature extraction and noise reduction from energy-momentum dispersion intensity maps obtained from raw angle-resolved photoemission spectroscopy (ARPES)…

Angle resolved photoemission spectroscopy (ARPES) has been commonly applied to evaluate the shape of Fermi surfaces by employing simple criteria for the determination of the Fermi vector k_F parallel to the surface such as maximum…

Recently, angle-resolved photoemission spectroscopy (ARPES) has revealed a dispersion anomaly at high binding energy near 0.3-0.5eV in various families of the high-temperature superconductors. For further studies of this anomaly we present…

We introduce an efficient first-principles framework for simulating angle-resolved photoemission spectroscopy (ARPES) based on the direct computation of photoelectron states as solutions of the Kohn-Sham equation with scattering boundary…

Multispectral transmission imaging provides strong benefits for early breast cancer screening. The frame accumulation method addresses the challenge of low grayscale and signal-to-noise ratio resulting from the strong absorption and…

This paper introduces Associative Compression Networks (ACNs), a new framework for variational autoencoding with neural networks. The system differs from existing variational autoencoders (VAEs) in that the prior distribution used to model…

Autoencoders allow to reconstruct a given input from a small set of parameters. However, the input size is often limited due to computational costs. We therefore propose a clustering and reassembling method for volumetric point clouds, in…

Recent years have witnessed the success of deep networks in compressed sensing (CS), which allows for a significant reduction in sampling cost and has gained growing attention since its inception. In this paper, we propose a new practical…

We develop a simulation procedure for angle-resolved photoemission spectroscopy (ARPES), where a photoelectron wave function is set to be an outgoing plane wave in a vacuum associated with the emitted photoelectron wave packet. ARPES…

In this article we review our angle- and time-resolved photoemission studies (ARPES and trARPES) on various ferropnictides.

A new low photon energy regime of angle resolved photoemission spectroscopy is accessed with lasers and used to study the superconductor Bi2Sr2CaCu2O8+delta. The low energy increases bulk sensitivity, reduces background, and improves…

Combining Angle resolved photoelectron spectroscopy (ARPES) and a $\mu$-focused Laser, we have performed scanning ARPES microscopy measurements of the domain population within the nematic phase of FeSe single crystals. We are able to…

In this paper, we build autoencoder based pipelines for extreme end-to-end image compression based on Ball\'e's approach, which is the state-of-the-art open source implementation in image compression using deep learning. We deepened the…

There has been a lot of recent interest in designing neural network models to estimate a distribution from a set of examples. We introduce a simple modification for autoencoder neural networks that yields powerful generative models. Our…

Detectors in next-generation high-energy physics experiments face several daunting requirements, such as high data rates, damaging radiation exposure, and stringent constraints on power, space, and latency. To address these challenges,…

The strong dependence of the momentum distribution of the photoelectrons on experimental conditions raises the question as to whether angle-resolved photoemission spectroscopy (ARPES) is able to provide an accurate reflection of the Fermi…

We present an application of autoencoders to the problem of noise reduction in single-shot astronomical images and explore its suitability for upcoming large-scale surveys. Autoencoders are a machine learning model that summarises an input…

Learned image compression methods have shown superior rate-distortion performance and remarkable potential compared to traditional compression methods. Most existing learned approaches use stacked convolution or window-based self-attention…