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Astronomical archives contain vast quantities of unexplored data that potentially harbour rare and scientifically valuable cosmic phenomena. We leverage new semi-supervised methods to extract such objects from the Hubble Legacy Archive. We…
We present a new spectrum extraction technique which employs optimal weights for the spectral extraction, deblends the overlapping spectra, determines the precise sky background, and takes into account correlations between errors correctly…
The Sloan Digital Sky Survey (SDSS) and Two Micron All Sky Survey (2MASS) are rich resources for studying stellar astrophysics and the structure and formation history of the Galaxy. As new surveys and instruments adopt similar filter sets,…
Outlier detection has received special attention in various fields, mainly for those dealing with machine learning and artificial intelligence. As strong outliers, anomalies are divided into the point, contextual and collective outliers.…
This study addresses an important gap in time series outlier detection by proposing a novel problem setting: long-term outlier prediction. Conventional methods primarily focus on immediate detection by identifying deviations from normal…
We present a systematic search for two types of very unusual astronomical objects: Galactic stars and spiral galaxies with double radio lobes, i.e. radio emission on opposite sides of the optical object, suggesting the ejection of jets from…
Outlier detection is an important problem occurring in a wide range of areas. Outliers are the outcome of fraudulent behaviour, mechanical faults, human error, or simply natural deviations. Many data mining applications perform outlier…
In this paper we present results of a pilot study to use imaging data from the Sloan Digital Sky Survey (SDSS) to search for low-surface-brightness (LSB) galaxies. For our pilot study we use a test sample of 92 galaxies from the catalog of…
In recent decades, large-scale sky surveys such as Sloan Digital Sky Survey (SDSS) have resulted in generation of tremendous amount of data. The classification of this enormous amount of data by astronomers is time consuming. To simplify…
We present a new algorithm to search for distant clusters of galaxies on catalogues deriving from imaging data, as those of the ESO Imaging Survey. Our algorithm is a matched filter one, similar to that adopted by Postman et al. (1996),…
Outlier detection aims to identify unusual data instances that deviate from expected patterns. The outlier detection is particularly challenging when outliers are context dependent and when they are defined by unusual combinations of…
We present the observational results of our search for emission-line galaxies (ELG) towards nearby voids. In order to find ELG, we started a survey using the IIIa-J objective prism plates from the Hamburg QSO Survey. The plates are…
Large scale imaging surveys will increase the number of galaxy-scale strong lensing candidates by maybe three orders of magnitudes beyond the number known today. Finding these rare objects will require picking them out of at least tens of…
We present an algorithm capable of detecting diffuse, dim sources of any size in an astronomical image. These sources often defeat traditional methods for source finding, which expand regions around points of high intensity. Extended…
An outlier is an observation or a data point that is far from rest of the data points in a given dataset or we can be said that an outlier is away from the center of mass of observations. Presence of outliers can skew statistical measures…
There exist multiple methods to detect outliers in multivariate data in the literature, but most of them require to estimate the covariance matrix. The higher the dimension, the more complex the estimation of the matrix becoming impossible…
We have improved upon our previous search technique of systematically searching QSO spectra for narrow galactic H-alpha emission, which indicates a foreground galaxy within the Sloan Digital Sky Survey (SDSS) spectral fiber. We now search…
Spatial perception is the backbone of many robotics applications, and spans a broad range of research problems, including localization and mapping, point cloud alignment, and relative pose estimation from camera images. Robust spatial…
Outlier Detection is a critical and cardinal research task due its array of applications in variety of domains ranging from data mining, clustering, statistical analysis, fraud detection, network intrusion detection and diagnosis of…
Anomaly, or out-of-distribution, detection is a promising tool for aiding discoveries of new particles or processes in particle physics. In this work, we identify and address two overlooked opportunities to improve anomaly detection for…