Related papers: A Deep Learning Approach to Quasar Continuum Predi…

A deep neural network (DNN) model consisting of two hidden layers was proposed for predicting the immediate environments of specific atoms based on X-ray absorption near-edge spectra (XANES). The output layer of the DNN can be adjusted to…

The transition from monolithic to distributed multi-chip quantum architectures has fundamentally altered the circuit compilation landscape, introducing challenges in managing temporal noise variations and minimizing expensive inter-chip…

We explore the use of Deep Learning to infer physical quantities from the observable transmitted flux in the Lyman-alpha forest. We train a Neural Network using redshift z=3 outputs from cosmological hydrodynamic simulations and mock…

The X-ray luminosity function of distant (3<z<5.1) unabsorbed quasars has been measured. A sample of distant high-luminosity quasars ($10^{45} \leq L_{{\rm X},2-10} < 7.5 \times 10^{45}$ erg/s in the 2--10 keV energy band) from the catalog…

Long-term climate projections require running global Earth system models on timescales of hundreds of years and have relatively coarse resolution (from 40 to 160 km in the horizontal) due to their high computational costs. Unresolved…

Although weight and activation quantization is an effective approach for Deep Neural Network (DNN) compression and has a lot of potentials to increase inference speed leveraging bit-operations, there is still a noticeable gap in terms of…

Interpreting scattered acoustic and electromagnetic wave patterns is a computational task that enables remote imaging in a number of important applications, including medical imaging, geophysical exploration, sonar and radar detection, and…

Strongly lensed Type Ia supernovae (LSNe Ia) are a promising probe to measure the Hubble constant ($H_0$) directly. To use LSNe Ia for cosmography, a time-delay measurement between the multiple images, a lens-mass model, and a mass…

This paper has proposed a new baseline deep learning model of more benefits for image classification. Different from the convolutional neural network(CNN) practice where filters are trained by back propagation to represent different…

Accurate amine property prediction is essential for optimizing CO2 capture efficiency in post-combustion processes. Quantum machine learning (QML) can enhance predictive modeling by leveraging superposition, entanglement, and interference…

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…

We present {\tt deepSIP} (deep learning of Supernova Ia Parameters), a software package for measuring the phase and -- for the first time using deep learning -- the light-curve shape of a Type Ia supernova (SN~Ia) from an optical spectrum.…

Optical spectra of galaxies and quasars from large cosmological surveys are used to measure redshifts and infer distances. They are also rich with information on the intrinsic properties of these astronomical objects. However, their…

We aim to select quasar candidates based on the two large survey databases, Pan-STARRS and AllWISE. Exploring the distribution of quasars and stars in the color spaces, we find that the combination of infrared and optical photometry is more…

Currently, the $\Lambda$ Cold Dark Matter model, which relies on the existence of cold dark matter and a cosmological constant $\Lambda$, best describes the Universe. However, we lack information in the high-redshift ($z$) region between…

We have designed the Extremely Luminous Quasar Survey (ELQS) to provide a highly complete census of unobscured UV-bright quasars during the cosmic noon, $z=2.8-5.0$. Here we report the discovery of 70 new quasars in the ELQS South Galactic…

We present a new catalogue of ~2,400 optically selected quasars with spectroscopic redshifts and X-ray observations from either Chandra or XMM-Newton. The sample can be used to investigate the non-linear relation between the UV and X-ray…

Quantum neural network architectures that have little-to-no inductive biases are known to face trainability and generalization issues. Inspired by a similar problem, recent breakthroughs in machine learning address this challenge by…

We investigate the high-redshift quasar luminosity function (QLF) down to an apparent magnitude of I(AB) = 25 in the Cosmic Evolution Survey (COSMOS). Careful analysis of the extensive COSMOS photometry and imaging data allows us to…

The Hubble constant ($H_0$) is one of the fundamental parameters in cosmology, but there is a heated debate around the $>$4$\sigma$ tension between the local Cepheid distance ladder and the early Universe measurements. Strongly lensed Type…