Related papers: Reverberation Mapping and Broad-Line Region Models
Black Hole Mass (M_BH) estimation in quasars, especially at high redshift, involves use of single epoch spectra with s/n and resolution that permit accurate measurement of the width of a broad line assumed to be a reliable virial estimator.…
Earlier, we suggested the "reload" concept of the polarimetric reverberation mapping of active galactic nuclei (AGN), proposed for the first time more than 10 years ago. We have successfully tested this approach of reverberation mapping of…
We review, for a general audience, a variety of recent experiments on extracting structure from machine-learning mathematical data that have been compiled over the years. Focusing on supervised machine-learning on labeled data from…
Large Language Models (LLMs) went from non-existent to ubiquitous in the machine learning discourse within a few years. Due to the fast pace of the field, it is difficult to identify the remaining challenges and already fruitful application…
Flux calibration of spectra in reverberation mapping (RM) is most often performed by assuming the flux constancy of some specified narrow emission lines, which stem from an extended region that is sometimes partially spatially resolved, in…
In this contribution we briefly review the reverberation mapping technique and its results for low and intermidiate luminosity AGNs. Then we present a monitoring campaign of high-luminosity high-redshift quasars which will extend these…
Unveiling the structure of the Broad Line Region (BLR) of AGNs is critical to understand the quasar phenomenon. Resolving a few BLRs by optical interferometry will bring decisive information to confront, complement and calibrate the…
The variable continuum emission of an active galactic nucleus (AGN) produces corresponding responses in the broad emission lines, which are modulated by light travel delays, and contain information on the physical properties, structure, and…
Linguistic representation learning in deep neural language models (LMs) has been studied for decades, for both practical and theoretical reasons. However, finding representations in LMs remains an unsolved problem, in part due to a dilemma…
Advances in machine learning have impacted myriad areas of materials science, ranging from the discovery of novel materials to the improvement of molecular simulations, with likely many more important developments to come. Given the rapid…
Recently, with the significant developments in deep learning techniques, solving underdetermined inverse problems has become one of the major concerns in the medical imaging domain. Typical examples include undersampled magnetic resonance…
Neural networks have been successfully used for classification tasks in a rapidly growing number of practical applications. Despite their popularity and widespread use, there are still many aspects of training and classification that are…
Machine learning can provide deep insights into data, allowing machines to make high-quality predictions and having been widely used in real-world applications, such as text mining, visual classification, and recommender systems. However,…
In this article we study fine regularity properties for mappings of finite distortion. Our main theorems yield strongly localized regularity results in the borderline case in the class of maps of exponentially integrable distortion.…
Healthcare sector is totally different from other industry. It is on high priority sector and people expect highest level of care and services regardless of cost. It did not achieve social expectation even though it consume huge percentage…
Nearly all spatial reasoning problems involve uncertainty of one sort or another. Uncertainty arises due to the inaccuracies of sensors used in measuring distances and angles. We refer to this as directional uncertainty. Uncertainty also…
The virial factor $f$ is critical for accurate supermassive black hole (SMBH) mass measurements using reverberation mapping (RM) and the radius--luminosity ($R$--$L$) relation, yet its value remains highly uncertain. While traditional…
Modern radar systems have high requirements in terms of accuracy, robustness and real-time capability when operating on increasingly complex electromagnetic environments. Traditional radar signal processing (RSP) methods have shown some…
This paper surveys the machine learning literature and presents in an optimization framework several commonly used machine learning approaches. Particularly, mathematical optimization models are presented for regression, classification,…
I address the need for deep blazar surveys by showing that our current understanding of blazars is based on a relatively small number of intrinsically luminous sources. I then review the on-going deeper surveys, addressing in particular…