相关论文: An iterative method to deblend AGN-Host contributi…

NASA's Kepler, K2 and TESS missions employ Simple Aperture Photometry (SAP) to derive time-series photometry, where an aperture is estimated for each star, and pixels containing each star are summed to create a single light curve. This…

We explore novel methods of recovering the original spectral line profiles from data obtained by instruments that sample those profiles with an extended or multipeaked spectral transmission profile. The techniques are tested on data…

We have explored the interplay of star formation and AGN activity in soft X-rays (0.5-2 keV) in two samples of Seyfert 2 galaxies (Sy2s). Using a combination of low resolution CCD spectra from Chandra and XMM-Newton, we modeled the soft…

In terms of the variable nature of normal active galaxy nuclei (AGN) and luminous quasars, a so-called flux variation gradient (FVG) method has been widely utilized to estimate the underlying non-variable host galaxy fluxes. The FVG method…

Near-future large galaxy surveys will encounter blended galaxy images at a fraction of up to 50% in the densest regions of the universe. Current deblending techniques may segment the foreground galaxy while leaving missing pixel intensities…

We present a new algorithm, named Autonomous Gaussian Decomposition (AGD), for automatically decomposing spectra into Gaussian components. AGD uses derivative spectroscopy and machine learning to provide optimized guesses for the number of…

We use a new technique to extract the spectrum of a supernova from that of the contaminating background of its host galaxy, and apply it to the specific case of high-redshift Type Ia supernova (SN Ia) spectroscopy. The algorithm is based on…

In order to assess the accuracy with which we can determine the morphologies of AGN host galaxies, we have simulated more than 50,000 ACS images of galaxies with z < 1.25, using image and noise properties appropriate for the GOODS survey.…

The INT Photometric H-Alpha Survey (IPHAS) is particular effective in tracing asymptotic giant branch (AGB) stars across the Galactic disk due to its use of broad-band r' and i' filters, and the clear separation of the dwarf and giant…

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…

Measuring the star-forming properties of AGN hosts is key to our understanding of galaxy formation and evolution. However, this topic remains debated, partly due to the difficulties in separating the infrared (i.e. 1--1000 $\mu$m) emission…

Removing optical and atmospheric blur from galaxy images significantly improves galaxy shape measurements for weak gravitational lensing and galaxy evolution studies. This ill-posed linear inverse problem is usually solved with…

The ionizing fluxes from quasars and other active galactic nuclei (AGN) are critical for interpreting their emission-line spectra and for photoionizing and heating the intergalactic medium (IGM). Using far-ultraviolet spectra from the…

Active galactic nuclei (AGN) play a key role in galaxy evolution but are challenging to identify due to their varied observational signatures. Furthermore, understanding their impact requires quantifying their strength relative to their…

Ground-based astronomical observations will continue to produce resolution-limited images due to atmospheric seeing. Deconvolution reverses such effects and thus can benefit extracted science in multifaceted ways. We apply the Scaled…

Aims. As a new approach to the study of resolved stellar populations in nearby galaxies, our goal is to demonstrate in NGC300 that integral field spectroscopy with high spatial resolution and excellent seeing conditions reaches an…

We separate the extragalactic radio source population above ~50 uJy into active galactic nuclei (AGN) and star-forming sources. The primary method of our approach is to fit the infrared spectral energy distributions (SEDs), constructed…

This letter presents a novel hybrid method that leverages deep learning to exploit the multi-resolution analysis capability of the wavelets, in order to denoise a photoplethysmography (PPG) signal. Under the proposed method, a noisy PPG…

Deconvolution of large survey images with millions of galaxies requires to develop a new generation of methods which can take into account a space variant Point Spread Function (PSF) and have to be at the same time accurate and fast. We…

We present a novel deep learning method to separately extract the two-dimensional flux information of the foreground galaxy (deflector) and background system (source) of Galaxy-Galaxy Strong Lensing events using U-Net (GGSL-Unet for short).…