Related papers: QUEST (Quasar Unsupervised Encoder and Synthesis T…
Broad absorption line (BAL) quasars serve as critical probes for understanding active galactic nucleus (AGN) outflows, black hole accretion, and cosmic evolution. To address the limitations of manual classification in large-scale…
Quantum machine learning methods often rely on fixed, hand-crafted quantum encodings that may not capture optimal features for downstream tasks. In this work, we study the power of quantum autoencoders in learning data-driven quantum…
MLPQNA stands for Multi Layer Perceptron with Quasi Newton Algorithm and it is a machine learning method which can be used to cope with regression and classification problems on complex and massive data sets. In this paper we give the…
Aims. Quasar catalogues from narrow-band photometric data are used in a variety of applications, including targeting for spectroscopic follow-up, measurements of supermassive black hole masses, or Baryon Acoustic Oscillations. Here, we…
The FIRST Radio Survey provides a new resource for constructing a large quasar sample. With source positions accurate to better than 1\arcsec\ and a point source sensitivity limit of 1 mJy, it reaches 50 times deeper than previous radio…
We describe a technique for simultaneously classifying and estimating the redshift of quasars. It can separate quasars from stars in arbitrary redshift ranges, estimate full posterior distribution functions for the redshift, and naturally…
Quantum Machine Learning investigates the possibility of quantum computers enhancing Machine Learning algorithms. Anomaly segmentation is a fundamental task in various domains to identify irregularities at sample level and can be addressed…
We propose a Multimodal Machine Learning method for estimating the Photometric Redshifts of quasars (PhotoRedshift-MML for short), which has long been the subject of many investigations. Our method includes two main models, i.e. the feature…
This paper investigates a phenomenon where query-based object detectors mispredict at the last decoding stage while predicting correctly at an intermediate stage. We review the training process and attribute the overlooked phenomenon to two…
The DESI survey will measure large-scale structure using quasars as direct tracers of dark matter in the redshift range $0.9<z<2.1$ and using quasar Ly-$\alpha$ forests at $z>2.1$. We present two methods to select candidate quasars for DESI…
Over the last two decades, around 300 quasars have been discovered at $z\gtrsim6$, yet only one has identified as being strongly gravitationally lensed. We explore a new approach -- enlarging the permitted spectral parameter space, while…
Quasars accreting at very high rates are believed to be prime movers of galactic evolution because of their high radiative and mechanical output. The study presented in this paper investigates a sample of six highly accreting quasars at…
We introduce the Convolutional Set Transformer (CST), a novel neural architecture designed to process image sets of arbitrary cardinality that are visually heterogeneous yet share high-level semantics - such as a common category, scene, or…
We present a novel intelligent quasar continuum neural network (iQNet), predicting the intrinsic continuum of any quasar in the rest-frame wavelength range 1020 Angstroms $\leq \lambda \leq$ 1600 Angstroms. We train this network using…
In this paper we study the universality of the results of SQUEzE, a software package to classify quasar spectra and estimate their redshifts. The code is presented in \cite{Perez-Rafols+2019}. We test the results against changes on…
Super-resolution and denoising are ill-posed yet fundamental image restoration tasks. In blind settings, the degradation kernel or the noise level are unknown. This makes restoration even more challenging, notably for learning-based…
The main purpose of this work is to improve the existing knowledge about the most powerful engines in the Universe - quasars. Although a lot is already known, we still have only a vague idea how these engines work exactly, why they behave…
Supermassive black holes (SMBHs) of $\sim 10^9\, M_\odot$ are generally believed to be the central engines of the luminous quasars observed at $z\gtrsim6$, but their astrophysical origin remains elusive. The $z\gtrsim$ quasars reside in…
This article presents an argument for why quantum computers could unlock new methods for machine learning. We argue that spectral methods, in particular those that learn, regularise, or otherwise manipulate the Fourier spectrum of a machine…
One of the fundamental questions about quasars is related to their central supermassive black holes. The reason for the existence of these black holes with such a huge mass is still unclear and various models have been proposed to explain…