Related papers: QUEST (Quasar Unsupervised Encoder and Synthesis T…
Quantum enhanced receivers are endowed with resources to achieve higher sensitivities than conventional technologies. For application in optical communications, they provide improved discriminatory capabilities for multiple non-orthogonal…
Obscuration in high-redshift quasi-stellar objects (QSO) has a profound impact on our understanding of the evolution of supermassive black holes across the cosmic time. An accurate quantification of its relevance is therefore mandatory. We…
We have imaged 45 quasars from the Sloan Digital Sky Survey (SDSS) with redshifts 1.85 < z < 4.26 in JHKs with the KPNO SQIID imager. By combining these data with optical magnitudes from the SDSS we have computed the restframe optical…
Quantitative susceptibility mapping (QSM) utilizes MRI signal phase to estimate local tissue susceptibility, which has been shown useful to provide novel image contrast and as biomarkers of abnormal tissue. QSM requires addressing a…
Quantum coherence and entanglement are fundamental resources in quantum technologies, yet their efficient estimation for unknown states by employing minimal resources in experimental settings remains challenging, particularly in…
Autonomous planetary exploration demands real-time, high-fidelity environmental perception. Standard deep learning models require massive computational resources. Conversely, space-qualified onboard computers operate under strict power,…
Ultra-violet (UV) radiation from accreting black holes ionizes the intergalactic gas around early quasars, carving out highly ionized bubbles in their surroundings. Any changes in a quasar's luminosity are therefore predicted to produce…
Purpose: To develop an automated machine-learning-based method for the discovery of rapid and quantitative chemical exchange saturation transfer (CEST) MR fingerprinting acquisition and reconstruction protocols. Methods: An MR physics…
Quantum Error Correction (QEC) is one of the fundamental problems in quantum computer systems, which aims to detect and correct errors in the data qubits within quantum computers. Due to the presence of unreliable data qubits in existing…
The brightest southern quasar above redshift $z=1$, HE 0515$-$4414, with its strong intervening metal absorption-line system at $z_{abs}=1.1508$, provides a unique opportunity to precisely measure or limit relative variations in the…
Super-resolution is a machine-learning technique in image processing which generates high-resolution images from low-resolution images. Inspired by this approach, we perform a numerical experiment of quantum machine learning, which takes…
Machine learning can provide powerful tools to detect patterns in multi-dimensional parameter space. We use K-means -a simple yet powerful unsupervised clustering algorithm which picks out structure in unlabeled data- to study a sample of…
Recently discovered quasar pairs at high redshifts ($z\gtrsim$5) are likely precursors to supermassive black hole mergers, providing a promising window to high redshift quasar growth mechanisms. However, the large uncertainties on their…
We present simulations of quasar colors, magnitudes, and numbers at redshifts 5<z<10 based on our discovery of ten new high-redshift quasars and the cloning of lower redshift Sloan Digital Sky Survey (SDSS) quasars. The ten quasars have…
We present 3D-HST, a near-infrared spectroscopic Treasury program with the Hubble Space Telescope for studying the processes that shape galaxies in the distant Universe. 3D-HST provides rest-frame optical spectra for a sample of ~7000…
Quasars behind the Galactic plane (GPQs) are important astrometric references and useful probes of Milky Way gas. However, the search for GPQs is difficult due to large extinctions and high source densities in the Galactic plane. Existing…
Quasinormal modes (QNMs) are usually characterized by their time dependence; oscillations at specific frequencies predicted by black hole (BH) perturbation theory. QNMs are routinely identified in the ringdown of numerical relativity…
The accurate recovery of constituent-level optical properties from integrating sphere measurements is a central analytical challenge in pharmaceutical analysis, food science, and biomedical diagnostics. Neural network autoencoders can…
We propose a quantum implicit neural representation (QINR)-based autoencoder (AE) and variational autoencoder (VAE) for image reconstruction and generation tasks. Our purpose is to demonstrate that the QINR in VAEs and AEs can transform…
The Karhunen-Loeve (KL) transform can compactly represent the information contained in large, complex datasets, cleanly eliminating noise from the data and identifying elements of the dataset with extreme or inconsistent characteristics. We…