Related papers: Automatic classification of time-variable X-ray so…
We present results from the photometric and spectroscopic identification of 122 X-ray sources discovered by XMM-Newton in the 2-10 keV band (the HELLAS2XMM 1dF sample). One of the most interesting results (which is found also in deeper…
ABRIDGED. We present "The XMM-Newton Bright Serendipitous Survey", two flux-limited samples with flux limit fx ~7E-14 cgs in the 0.5-4.5 keV (BSS) and 4.5-7.5 keV (HBSS) energy band, respectively. After discussing the survey strategy, we…
Much more attention has been paid to unsupervised feature selection nowadays due to the emergence of massive unlabeled data. The distribution of samples and the latent effect of training a learning method using samples in more effective…
We search for transient sources in a sample of ULXs from the 3XMM-DR4 release of the XMM-Newton Serendipitous Source Catalogue in order to find candidate neutron star ULXs alternating between an accreting state and the propeller regime, in…
Combining machine learning with econometric analysis is becoming increasingly prevalent in both research and practice. A common empirical strategy involves the application of predictive modeling techniques to 'mine' variables of interest…
Unsupervised multi-domain adaptation plays a key role in transfer learning by leveraging acquired rich source information from multiple source domains to solve target task from an unlabeled target domain. However, multiple source domains…
In the latest data release from the Fermi $\gamma$-ray Space Telescope (the 4th Fermi LAT 14 yr Catalog, or 4FGL), more than 50% of the Galactic sources are yet to be identified. We observed 13 unidentified Fermi LAT sources with the…
Machine-learning (ML) algorithms will play a crucial role in studying the large datasets delivered by new facilities over the next decade and beyond. Here, we investigate the capabilities and limits of such methods in finding galaxies with…
The Fermi-LAT unassociated sources represent some of the most enigmatic gamma-ray sources in the sky. Observations with the Swift-XRT and -UVOT telescopes have identified hundreds of likely X-ray and UV/optical counterparts in the…
As technology advanced, collecting data via automatic collection devices become popular, thus we commonly face data sets with lengthy variables, especially when these data sets are collected without specific research goals beforehand. It…
Data sampling acts as a pivotal role in training deep learning models. However, an effective sampling schedule is difficult to learn due to the inherently high dimension of parameters in learning the sampling schedule. In this paper, we…
The All-Sky Automated Survey for Supernovae (ASAS-SN) is the first optical survey to monitor the entire sky, currently with a cadence of $\lesssim 24$ hours down to $g \lesssim 18.5$ mag. ASAS-SN has routinely operated since 2013,…
Machine learning has emerged as a powerful tool in the field of gamma-ray astrophysics. The algorithms can distinguish between different source types, such as blazars and pulsars, and help uncover new insights into the high-energy universe.…
Anomaly detection in connected autonomous vehicles (CAVs) is crucial for maintaining safe and reliable transportation networks, as CAVs can be susceptible to sensor malfunctions, cyber-attacks, and unexpected environmental disruptions. This…
The two currently largest all-sky photometric datasets, WISE and SuperCOSMOS, were cross-matched by Bilicki et al. (2016) (B16) to construct a novel photometric redshift catalogue on 70% of the sky. Galaxies were therein separated from…
We address the problem of learning self-supervised representations from unlabeled image collections. Unlike existing approaches that attempt to learn useful features by maximizing similarity between augmented versions of each input image or…
Ongoing or upcoming surveys such as Gaia, ZTF, or LSST will observe light-curves of billons or more astronomical sources. This presents new challenges for identifying interesting and important types of variability. Collecting a sufficient…
Classification of galaxy morphology is a challenging but meaningful task for the enormous amount of data produced by the next-generation telescope. By introducing the adaptive polar coordinate transformation, we develop a rotationally…
Several machine learning tasks require to represent the data using only a sparse set of interest points. An ideal detector is able to find the corresponding interest points even if the data undergo a transformation typical for a given…
Time series anomaly detection is instrumental in maintaining system availability in various domains. Current work in this research line mainly focuses on learning data normality deeply and comprehensively by devising advanced neural network…