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Ignasi Pérez-Ráfols

Publications and source records attributed to Ignasi Pérez-Ráfols.

3 recordsLinked to original sources

Quiescent Host Galaxies of Extended Quasars Revealed by Spectrophotometric Decomposition

Previous works of low-redshift quasar host galaxies have focused on compact quasars and found that their host galaxies are mainly star-forming galaxies. Here we present a study of host galaxies for quasars with extended morphologies in ground-based optical images. We select a sample of more than 1000 type 1 quasars at redshift $0.1<z<1$ that are classified as extended objects by DESI. Combining high-resolution spectra from DESI and high-quality images from Subaru HSC, we develop a spectrophotometric decomposition technique to iteratively decompose each quasar into an AGN component and its host galaxy. The technique can effectively break the degeneracy between the AGN and host components and capture the host spectral features. Our results show that the host galaxies of most quasars have low star-formation rates (SFRs) and low specific SFRs, indicating that they are quiescent galaxies. Many of them exhibit prominent post-starburst features with the existence of significant old stellar populations. These properties are quite different from the nature of compact quasars with star-forming host galaxies. In addition, the relation between the black hole mass and stellar mass for our sample is broadly consistent with the canonical local relations. This work is complementary to the previous studies and suggests that the host galaxies of low-redshift quasars are more diverse than what was thought.

astro-ph.GA↗

Automated quasar continuum estimation using neural networks: a comparative study of deep-learning architectures

Context. Ongoing and upcoming large spectroscopic surveys are drastically increasing the number of observed quasar spectra, requiring the development of fast and accurate automated methods to estimate spectral continua. Aims. This study evaluates the performance of three neural networks (NN) - an autoencoder, a convolutional NN (CNN), and a U-Net - in predicting quasar continua within the rest-frame wavelength range of $1020~\textÅ$ to $2000~\textÅ$. The ability to generalize and predict galaxy continua within the range of $3500~\textÅ$ to $5500~\textÅ$ is also tested. Methods. The performance of these architectures is evaluated using the absolute fractional flux error (AFFE) on a library of mock quasar spectra for the WEAVE survey, and on real data from the Early Data Release observations of the Dark Energy Spectroscopic Instrument (DESI) and the VIMOS Public Extragalactic Redshift Survey (VIPERS). Results. The autoencoder outperforms the U-Net, achieving a median AFFE of 0.009 for quasars. The best model also effectively recovers the Ly$α$ optical depth evolution in DESI quasar spectra. With minimal optimization, the same architectures can be generalized to the galaxy case, with the autoencoder reaching a median AFFE of 0.014 and reproducing the D4000n break in DESI and VIPERS galaxies.

astro-ph.GA↗

The Completed SDSS-IV extended Baryon Oscillation Spectroscopic Survey: The Damped Lyman-$α$ systems Catalog

We present the characteristics of the Damped Lyman-$α$ (DLA) systems found in the data release DR16 of the extended Baryon Oscillation Spectroscopic Survey (eBOSS) of the Sloan Digital Sky Survey (SDSS). DLAs were identified using the convolutional neural network (CNN) of~\cite{Parks2018}. A total of 117,458 absorber candidates were found with $2 \leq \zdla \leq 5.5$ and $19.7 \leq \lognhi \leq 22$, including 57,136 DLA candidates with $\lognhi \geq 20.3$. Mock quasar spectra were used to estimate DLA detection efficiency and the purity of the resulting catalog. Restricting the quasar sample to bright forests, i.e. those with mean forest fluxes $\meanflux>2\times\fluxunit$, the completeness and purity are greater than 90\% for DLAs with column densities in the range $20.1\leq \lognhi \leq 22$.

astro-ph.CO↗