Related papers: Enhancing Ly{\alpha} Emitter Identification in HET…

We analyze a sample of $z$-dropout galaxies in the CANDELS GOODS South and UDS fields that have been targeted by a dedicated spectroscopic campaign aimed at detecting their Ly$\alpha$ line. Deep IRAC observations at 3.6 and 4.5 $\mu$m are…

Convolutional denoising autoencoders (DAEs) are powerful tools for image restoration. However, they inherit a key limitation of convolutional neural networks (CNNs): they tend to recover low-frequency features, such as smooth regions, more…

High-Resolution Transmission Electron Microscopy (HRTEM) enables atomic-scale observation of nucleation dynamics, which boosts the studies of advanced solid materials. Nonetheless, due to the millisecond-scale rapid change of nucleation, it…

We introduce the LEnSed laeS in the Eboss suRvey (LESSER) project, which aims to search for lensed Lyman-$\alpha$ Emitters (LAEs) in the Extended Baryon Oscillation Spectroscopic Survey (eBOSS). The final catalog contains 361 candidate…

Holographic displays have significant potential in virtual reality and augmented reality owing to their ability to provide all the depth cues. Deep learning-based methods play an important role in computer-generated holography (CGH). During…

The use of deep learning (DL) in medical image analysis has significantly improved the ability to predict lung cancer. In this study, we introduce a novel deep convolutional neural network (CNN) model, named ResNet+, which is based on the…

Lyman Alpha Emitters (LAEs) are galaxies that have been selected on the basis of a strong Ly{\alpha} emission line in their spectra. Observational campaigns have dramatically increased the sample of known LAEs, which now extends out to z=7.…

Fast and accurate fault detection and localization in fiber optic cables is extremely important to ensure the optical network survivability and reliability. Hence there exists a crucial need to develop an automatic and reliable algorithm…

Large-scale multimodal contrastive learning has recently achieved impressive success in learning rich and transferable representations, yet it remains fundamentally limited by the uniform treatment of feature dimensions and the neglect of…

Epilepsy is a neurological disorder and for its detection, encephalography (EEG) is a commonly used clinical approach. Manual inspection of EEG brain signals is a time-consuming and laborious process, which puts heavy burden on neurologists…

Deep Learning (DL) is a two-step classification model that consists feature learning, generating feature representations using unsupervised ways and the supervised learning stage at the last step of model using at least two hidden layers on…

We developed and trained a pipeline of three machine learning (ML) models than can predict which sources are more likely to be an AGN and to be detected in specific radio surveys. Also, it can estimate redshift values for predicted…

We present optical spectroscopy of two samples of GALEX grism selected Ly alpha emitters (LAEs): one at z=0.195-0.44 and the other at z=0.65-1.25. We have also observed a comparison sample of galaxies in the same redshift intervals with the…

Among applications of deep learning (DL) involving low cost sensors, remote image classification involves a physical channel that separates edge sensors and cloud classifiers. Traditional DL models must be divided between an encoder for the…

To understand the formation and evolution of massive cosmic structures, studying them at high redshift, in the epoch when they formed the majority of their mass is essential. The One-hundred-deg$^2$ DECam Imaging in Narrowbands (ODIN)…

We present a multiple emission lines study of $\sim$1300 H$\alpha$ emitters (HAEs) at $z \sim 2.3$ in the ZFOURGE survey. In contrast to the traditional spectroscopic method, our sample is selected based on the flux excess in the…

We present a spectroscopic survey of 318 faint $(R\sim 27$, $L\sim0.1L_*)$, Ly{\alpha}-emission-selected galaxies (LAEs) at 2.5<z<3. A sample of 32 LAEs with rest-frame optical spectra from Keck/MOSFIRE are used to interpret the LAE spectra…

Deep Convolutional Neural Networks (CNNs) have been widely used in various domains due to their impressive capabilities. These models are typically composed of a large number of 2D convolutional (Conv2D) layers with numerous trainable…

Hyperspectral image (HSI) classification faces critical challenges, including high spectral dimensionality, complex spectral-spatial correlations, and limited training samples with severe class imbalance. While CNNs excel at local feature…

In the context of large spectroscopic surveys of stars, data-driven methods are key in deducing physical parameters for millions of spectra in a short time. Convolutional neural networks (CNNs) enable us to connect observables (e.g.…