Related papers: Improved AGN light curve analysis with the z-trans…
Functional connectivity (FC) has been widely used to study brain network interactions underlying the emerging cognition and behavior of an individual. FC is usually defined as the correlation or partial correlation between brain regions.…
We propose an algorithm based on Zadoff-Chu (ZC) sequences and time-frequency images (TFI) to achieve drone remote identification (RID). Specifically, by analyzing the modulation parameters and frame structures of drone ratio-frequency (RF)…
Traffic flow forecasting is a highly challenging task due to the dynamic spatial-temporal road conditions. Graph neural networks (GNN) has been widely applied in this task. However, most of these GNNs ignore the effects of time-varying road…
The extremes of Active Galactic Nuclei (AGN) variability offer valuable new insights into the drivers and physics of AGN. We discuss some of the most extreme cases of AGN variability; the highest amplitudes, deep minima states, extreme…
Gamma-ray flares from Active Galactic Nuclei (AGN) show substantial variability on ultrafast timescales (i.e. shorter than the light crossing time of the AGN's supermassive black hole). We propose that ultrafast variability is a byproduct…
Unsupervised anomaly detection aims to identify anomalous samples from highly complex and unstructured data, which is pervasive in both fundamental research and industrial applications. However, most existing methods neglect the complex…
Active Galactic Nuclei (AGN) vary in their brightness across all wavelengths. Moreover, longer wavelength ultraviolet - optical continuum light curves appear to be delayed with respect to shorter wavelength light curves. A simple way to…
Classifier-free guidance (CFG) has emerged as a pivotal advancement in text-to-image latent diffusion models, establishing itself as a cornerstone technique for achieving high-quality image synthesis. However, under high guidance weights,…
Recent works have shown that exploiting multi-scale representations deeply learned via convolutional neural networks (CNN) is of tremendous importance for accurate contour detection. This paper presents a novel approach for predicting…
Asynchronous Time Series is a multivariate time series where all the channels are observed asynchronously-independently, making the time series extremely sparse when aligning them. We often observe this effect in applications with complex…
This work develops application techniques for stochastic modelling of Active Galactic Nuclei (AGN) variability as a probe of accretion disk physics. Stochastic models, specifically Continuous Auto-Regressive Moving Average (CARMA) models,…
Reverberation mapping is one of the main techniques used to study active galactic nuclei (AGN) accretion disks. Traditional continuum reverberation mapping uses short lags between variability in different wavelength AGN light curves on the…
Deployment of machine learning algorithms into real-world practice is still a difficult task. One of the challenges lies in the unpredictable variability of input data, which may differ significantly among individual users, institutions,…
We combine Lyman-break colour selection with ultradeep (> 200 ks) Chandra X-ray imaging over a survey area of ~0.35 deg^2 to select high redshift AGN. Applying careful corrections for both the optical and X-ray selection functions, the data…
Galaxy angular-power spectrum and autocorrelation functions (ACFs) provide information about the distribution of matter by using galaxy counts as a proxy. In this study, we are going to estimate autocorrelation angular power spectrum and…
As in Gonzalez-Nuevo et al. 2017 and Bonavera et al. 2019, the high-z sub-millimetre galaxies can be used as background sample for gravitational lensing studies thanks to their magnification bias. In particular, as in Bonavera et al. 2020…
Classifying variable stars is crucial for advancing our understanding of stellar evolution and dynamics. As large-scale surveys generate increasing volumes of light curve data, the demand for automated and reliable classification techniques…
This study presents a comprehensive analysis of the infrared (IR) luminosity functions (LF) of star-forming (SF) galaxies and active galactic nuclei (AGN) using data from the ZFOURGE survey. We employ CIGALE to decompose the spectral energy…
A damped random walk (DRW) process is often used to describe the temporal UV/optical continuum variability of active galactic nuclei (AGN). However, recent investigations have shown that this model fails to capture the full spectrum of AGN…
The production of artificial light curves with known statistical and variability properties is of great importance in astrophysics. Consolidating the confidence levels during cross-correlation studies, understanding the artefacts induced by…