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

We present cosmological ($30-400$ Mpc) distributions of neutral hydrogen (HI) in the inter-galactic medium (IGM) traced by Ly$\alpha$ Emitters (LAEs) and QSOs at $z=2.1-2.5$, selected with the data of the on-going Hobby-Eberly Telescope…

In this paper, we propose a novel Convolutional Neural Network (CNN) approach for the classification of raw dry-EEG signals without any data pre-processing. To illustrate the effectiveness of our approach, we utilise the Steady State Visual…

We present cosmological constraints from the sample of Type Ia supernovae (SN Ia) discovered during the full five years of the Dark Energy Survey (DES) Supernova Program. In contrast to most previous cosmological samples, in which SN are…

We present a spatially resolved stacked analysis of 287 LAEs at $z>4$ observed with JWST/NIRSpec prism spectroscopy. By constructing a two-dimensional stack from public surveys (CAPERS, CEERS, JADES, and RUBIES), we probe the average…

We present a spectroscopically confirmed sample of Lyman alpha emitting galaxies (LAEs) at z ~ 4.5 in the Extended Chandra Deep Field South (ECDFS), which we combine with a sample of z ~ 4.5 LAEs from the Large Area Lyman Alpha (LALA)…

We investigate the feasibility of using deep learning techniques, in the form of a one-dimensional convolutional neural network (1D-CNN), for the extraction of signals from the raw waveforms produced by the individual channels of liquid…

In the proposed SEHybridSN model, a dense block was used to reuse shallow feature and aimed at better exploiting hierarchical spatial spectral feature. Subsequent depth separable convolutional layers were used to discriminate the spatial…

We present a `CLAssifier-DECoder' architecture (\emph{ClaDec}) which facilitates the comprehension of the output of an arbitrary layer in a neural network (NN). It uses a decoder to transform the non-interpretable representation of the…

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,…

In coherent optical orthogonal frequency-division multiplexing (CO-OFDM) fiber communications, a novel end-to-end learning framework to mitigate Laser Phase Noise (LPN) impairments is proposed in this paper. Inspired by Autoencoder (AE)…

Lymph node (LN) assessment is a critical, indispensable yet very challenging task in the routine clinical workflow of radiology and oncology. Accurate LN analysis is essential for cancer diagnosis, staging, and treatment planning. Finding…

We measure the Ly$\alpha$ escape fraction of 935 [OIII]-emitting galaxies between $1.9 < z < 2.35$ by comparing stacked spectra from the Hubble Space Telescope/WFC3's near-IR grism to corresponding stacks from the Hobby Eberly Telescope…

Context. Investigating the diagnostic power of H$\alpha$/H$\beta$ Charge-Exchange (CE) emission as Low-Energy Galactic Cosmic Rays(LECRs) tracer in diffuse regions. Aims. In this work, we define and test a spectroscopic indicator of CE…

Using wide field narrow-band surveys, we provide a new measurement of the $z=6.6$ Lyman-$\alpha$ Emitter (LAE) luminosity function (LF), which constraints the bright end for the first time. We use a combination of archival narrow-band NB921…

Hepatocellular carcinoma (HCC) is a common type of liver cancer whose early-stage diagnosis is a common challenge, mainly due to the manual assessment of hematoxylin and eosin-stained whole slide images, which is a time-consuming process…

Despite significant advances in deep learning-based sleep stage classification, the clinical adoption of automatic classification models remains slow. One key challenge is the lack of explainability, as many models function as black boxes…

Dual-phase time projection chamber (TPC) that employs a multi-ton-scale liquid xenon (LXe) target mass is a pioneering detector technology to search for dark matter. Beyond its advantage in dark matter direct detection efforts, the natural…

We study the Evolutionary Deep Neural Network (EDNN) framework for accelerating numerical solvers of time-dependent partial differential equations (PDEs). We introduce a Low-Rank Evolutionary Deep Neural Network (LR-EDNN), which constrains…

We performed a search for strong lens galaxy-scale systems in the first data release of the Dark Energy Survey (DES), from a color-selected parent sample of 18~745~029 Luminous Red Galaxies (LRGs). Our search was based on a Convolutional…

The information recoverable from galaxy spectra depends fundamentally on spectral resolution, yet assembling large samples at high resolution remains observationally expensive. We present a deep-learning framework for spectral…