Related papers: Periodicity detection in AGN with the boosted tree…
Aims. The aim of this paper is to analyse the previously discovered discontinuity of the periodicity of the GeV $\gamma$-ray emission of the radio-loud X-ray binary LS I +61{\deg}303 and to determine its physical origin. Methods. We used…
Fast Radio Bursts (FRBs) are bright radio transients with millisecond durations which typically occur at extragalactic distances. The association of FRB 20200428 with the Galactic magnetar SGR J1935+2154 strongly indicates that they could…
We present AGNBoost, a machine learning framework utilizing XGBoostLSS to identify AGN and estimate redshifts from JWST NIRCam and MIRI photometry. AGNBoost constructs 66 input features from 7 NIRCam and 4 MIRI bands to predict the fraction…
X-ray quasi-periodic oscillations (QPOs) in AGN allow us to probe and understand the nature of accretion in highly curved space-time, yet the most robust form of detection (i.e. repeat detections over multiple observations) has been limited…
Theory predicts that a supermassive black hole binary (SMBHB) could be observed as a luminous active galactic nucleus (AGN) that periodically varies on the order of its orbital timescale. In X-rays, periodic variations could be caused by…
Fast radio bursts (FRBs) are luminous, dispersed millisecond-duration radio bursts whose origin is poorly known. Recent observations suggest that some FRBs may reside in binary systems, even though conclusive evidence remains elusive. Here…
We searched for long period variation in V-band, Ic-band and RXTE X-ray light curves of the High Mass X-ray Binaries (HMXBs) LS 1698 / RX J1037.5-5647, HD 110432 / 1H 1249-637 and HD 161103 / RX J1744.7-2713 in an attempt to discover…
Merging satellite products and ground-based measurements is often required for obtaining precipitation datasets that simultaneously cover large regions with high density and are more accurate than pure satellite precipitation products.…
Astronomy is presently experiencing profound growth in the deployment of machine learning to explore large datasets. However, transient quasi-periodic oscillations (QPOs) which appear in power density spectra of many X-ray binary system…
The ability to identify stock market trends has obvious advantages for investors. Buying stock on an upward trend (as well as selling it in case of downward movement) results in profit. Accordingly, the start and end-points of the trend are…
Boosting is an ensemble method that combines base models in a sequential manner to achieve high predictive accuracy. A popular learning algorithm based on this ensemble method is eXtreme Gradient Boosting (XGB). We present an adaptation of…
We introduce a new periodicity detection algorithm for binary time series of event onsets, the Gaussian Mixture Periodicity Detection Algorithm (GMPDA). The algorithm approaches the periodicity detection problem to infer the parameters of a…
We present time series analyses of three-decade long radio observations of the BL Lacertae object AO 0235+164 made at the University of Michigan Radio Astronomical Observatory operating at three central frequencies of 4.8 GHz, 8.0 GHz and…
Classifying catalog objects as stars, galaxies, or AGN is a crucial part of any statistical study of galaxies. We describe our pipeline for binary (star/galaxy) and multiclass (star/galaxy/Type I AGN/Type II AGN) classification developed…
We use nine years of gamma-ray data provided by the Fermi Large Area Telescope (LAT) to systematically study the light curves of more than two thousand active galactic nuclei (AGN) included in recent Fermi-LAT catalogs. Ten different…
Predicting Sea Surface Temperature (SST) in the Great Barrier Reef (GBR) region is crucial for the effective management of its fragile ecosystems. This study provides a rigorous comparative analysis of several machine learning techniques to…
Machine learning based approaches are emerging as very powerful tools for many applications including source classification in astrophysics research due to the availability of huge high quality data from different surveys in observational…
Despite the rise to dominance of deep learning in unstructured data domains, tree-based methods such as Random Forests (RF) and Gradient Boosted Decision Trees (GBDT) are still the workhorses for handling discriminative tasks on tabular…
The faint radio-source population includes sources dominated both by star formation and active galactic nuclei (AGN), encoding the evolution of activity in the Universe. To investigate its nature, we probabilistically classified 4,471 radio…
This paper summarises two recently developed techniques in power spectral analysis and their application to a sample of X-ray light curves of accreting collapsed objects in active galactic nuclei and X-ray binaries. The first technique is…