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

In this study, we explore the potential of machine learning for modeling molecular electronic spectral intensities as a continuous function in a given wavelength range. Since presently available chemical space datasets provide excitation…

We take a sample of 94 ultraluminous, optical quasars from the search of over 14,486 deg^2 by Onken et al. 2022 in the range 4.4<redshift<5.2 and match them against the Rapid ASKAP Continuum Survey (RACS) observed on the Australian Square…

Deep learning has been shown to be able to recognize data patterns better than humans in specific circumstances or contexts. In parallel, quantum computing has demonstrated to be able to output complex wave functions with a few number of…

In this paper, we propose a deep learning architecture that produces accurate dense depth for the outdoor scene from a single color image and a sparse depth. Inspired by the indoor depth completion, our network estimates surface normals as…

We study the cosmic web at redshifts 1.0 <= z <= 1.8 using quasar systems based on quasar data from the SDSS DR7 QSO catalogue. Quasar systems were determined with a friend-of-friend (FoF) algorithm at a series of linking lengths. At the…

Continual learning can incrementally absorb new concepts without interfering with previously learned knowledge. Motivated by the characteristics of neural networks, in which information is stored in weights on connections, we investigated…

Uncertainty quantification (UQ) is important for reliability assessment and enhancement of machine learning models. In deep learning, uncertainties arise not only from data, but also from the training procedure that often injects…

We have conducted a spectroscopic survey to find faint quasars (-26.0 < M_{1450} < -22.0) at redshifts z=3.8-5.2 in order to measure the faint end of the quasar luminosity function at these early times. Using available optical imaging data…

Escalating cyber threats and the high-dimensional complexity of IoT traffic have outpaced classical anomaly detection methods. While deep learning offers improvements, computational bottlenecks limit real-time deployment at scale. We…

Gravitationally strongly lensed quasars (SL-QSO) offer invaluable insights into cosmological and astrophysical phenomena. With the data from ongoing and next-generation surveys, thousands of SL-QSO systems can be discovered expectedly,…

We have created a variety of composite quasar spectra using a homogeneous data set of over 2200 spectra from the Sloan Digital Sky Survey (SDSS). The quasar sample spans a redshift range of 0.044 <= z <= 4.789, and an absolute r' magnitude…

Deep learning has the potential to revolutionize quantum chemistry as it is ideally suited to learn representations for structured data and speed up the exploration of chemical space. While convolutional neural networks have proven to be…

We present deep radio observations of the most distant complete quasar sample drawn from the Sloan Digital Sky Survey. Combining our new data with those from literature we obtain a sample which is ~100 per cent complete down to S_1.4GHz =…

Time series prediction is essential for human activities in diverse areas. A common approach to this task is to harness Recurrent Neural Networks (RNNs). However, while their predictions are quite accurate, their learning process is complex…

The fast adaptation capability of deep neural networks in non-stationary environments is critical for online time series forecasting. Successful solutions require handling changes to new and recurring patterns. However, training deep neural…

We demonstrate the use of deep learning for fast spectral deconstruction of speckle patterns. The artificial neural network can be effectively trained using numerically constructed multispectral datasets taken from a measured spectral…

Air quality prediction is key to mitigating health impacts and guiding decisions, yet existing models tend to focus on temporal trends while overlooking spatial generalization. We propose AQ-Net, a spatiotemporal reanalysis model for both…

Exploring the possibility that fundamental constants of Nature might vary temporally or spatially constitutes one of the key science drivers for the European Southern Observatory's ESPRESSO spectrograph on the VLT and for the HIRES…

We present a novel method to identify candidate high redshift quasars (HzQs; ($z\gtrsim5.5$), which are unique probes of supermassive black hole growth in the early Universe, from large area optical/infrared photometric surveys. Using…

This paper presents a hybrid quantum-classical machine learning model for classification tasks, integrating a 4-qubit quantum circuit with a classical neural network. The quantum circuit is designed to encode the features of the Iris…