Related papers: Using cGANs for Anomaly Detection: Identifying Ast…
Local low metallicity galaxies with signatures of possible accretion activity are ideal laboratories in which to search for the lowest mass black holes and study their impact on the host galaxy. Here we present the first JWST NIRSpec IFS…
The rise of automation and machine learning (ML) in electron microscopy has the potential to revolutionize materials research through autonomous data collection and processing. A significant challenge lies in developing ML models that…
We present a multi-wavelength analysis of the properties of Extremely Red Galaxy (ERG) populations, selected in the GOODS-South/Chandra Deep Field South field. By using all the photometric and spectroscopic information available on large…
The James Webb Space Telescope (JWST) NIRSpec instrument will allow rest-frame ultraviolet/optical spectroscopy of galaxies in the epoch of reionization (EoR). Some galaxies may exhibit significant leakage of hydrogen-ionizing photons into…
We present the first James Webb Space Telescope/NIRCam-led determination of $7<z<9$ galaxy properties based on broadband imaging from 0.8 to 5~$\mathrm{\mu m}$ as part of the GLASS-JWST Early Release Science program. This is the deepest…
We calculate the angular correlation function for a sample of 170,000 AGN extracted from the Wide-field Infrared Survey Explorer (WISE) catalog, selected to have red mid-IR colors (W1 - W2 > 0.8) and 4.6 micron flux densities brighter than…
When formulated as an unsupervised learning problem, anomaly detection often requires a model to learn the distribution of normal data. Previous works apply Generative Adversarial Networks (GANs) to anomaly detection tasks and show good…
The JWST MIRI instrument will revolutionize extragalactic astronomy with unprecedented sensitivity and angular resolution in mid-IR. Here, we assess the potential of MIRI photometry to constrain galaxy properties in the Cosmic Evolution…
Deep generative models including generative adversarial networks (GANs) are powerful unsupervised tools in learning the distributions of data sets. Building a simple GAN architecture in PyTorch and training on the CANDELS data set, we…
We present deep Subaru/FOCAS spectra for two extreme emission line galaxies (EELGs) at $z\sim 1$ with strong {\sc[Oiii]}$\lambda$5007 emission lines, exhibiting equivalent widths (EWs) of $2905^{+946}_{-578}$ \AA\ and $2000^{+188}_{-159}$…
In many anomaly detection tasks, where anomalous data rarely appear and are difficult to collect, training using only normal data is important. Although it is possible to manually create anomalous data using prior knowledge, they may be…
Several dihedral angles prediction methods were developed for protein structure prediction and their other applications. However, distribution of predicted angles would not be similar to that of real angles. To address this we employed…
Developing efficient time series anomaly detection techniques is important to maintain service quality and provide early alarms. Generative neural network methods are one class of the unsupervised approaches that are achieving increasing…
This paper highlights initial photometric analyses of JWST NIRCam imaging data in the sightline of SMACS0723, aiming to identify galaxies at redshift $z>7$. By applying a conservative Lyman-break selection followed by photometric redshift…
Unsupervised learning of anomaly detection in high-dimensional data, such as images, is a challenging problem recently subject to intense research. Through careful modelling of the data distribution of normal samples, it is possible to…
This paper presents a deep learning-based approach to emotion detection using Conditional Generative Adversarial Networks (cGANs). Unlike traditional unimodal techniques that rely on a single data type, we explore a multimodal framework…
The WFC3 Infrared Spectroscopic Parallel Survey (WISP) uses the Hubble Space Telescope (HST) infrared grism capabilities to obtain slitless spectra of thousands of galaxies over a wide redshift range including the peak of star formation…
Observations of astrophysical objects such as galaxies are limited by various sources of random and systematic noise from the sky background, the optical system of the telescope and the detector used to record the data. Conventional…
We take advantage of the NIRCam photometric observations available as part of the Cosmic Evolution Early Release Science survey (CEERS) to identify and analyse very red sources in an effort to discover very dusty star forming galaxies. We…
Generative Adversarial Networks (GANs) have been impactful on many problems and applications but suffer from unstable training. The Wasserstein GAN (WGAN) leverages the Wasserstein distance to avoid the caveats in the minmax two-player…