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This paper investigates an important problem in stream mining, i.e., classification under streaming emerging new classes or SENC. The common approach is to treat it as a classification problem and solve it using either a supervised learner…
Noting the erroneous proclivity of information-theoretic approaches, like the Akaike information criterion (AIC), to select simpler models while performing model selection with a small sample size, we address the problem of new physics…
We present a new survey strategy to discover and study high redshift Type Ia supernovae (SNe Ia) using the Hubble Space Telescope (HST). By targeting massive galaxy clusters at 0.9<z<1.5, we obtain a twofold improvement in the efficiency of…
Observations of high redshift type Ia supernovae (SNe) will enable us to probe the structure of galaxy halos and the composition of dark matter. The future prospects for this field are briefly discussed here. First the ability of SN…
This paper introduces an algorithm for the detection of change-points and the identification of the corresponding subsequences in transient multivariate time-series data (MTSD). The analysis of such data has become more and more important…
Superluminous supernovae (SLSNe) are one of the most luminous stellar explosions known, yet they remain poorly understood. Because they are intrinsically rare, efficiently identifying them in the large alert streams produced by modern…
Machine-learning based classifiers have become indispensable in the field of astrophysics, allowing separation of astronomical sources into various classes, with computational efficiency suitable for application to the enormous data volumes…
The POINT-AGAPE collaboration surveyed M31 with the primary goal of optical detection of microlensing events, yet its data catalogue is also a prime source of lightcurves of variable and transient objects, including classical novae (CNe). A…
Motivated by the fact that calibrated light curves of Type Ia supernovae (SNe Ia) have become a major tool to determine the expansion history of the Universe, considerable attention has been given to, both, observations and models of these…
A novel machine learning approach is used to provide further insight into atomic nuclei and to detect orderly patterns amidst a vast data of large-scale calculations. The method utilizes a neural network that is trained on ab initio results…
We determine the viability of exploiting lensing time delays to observe strongly gravitationally lensed supernovae (gLSNe) from first light. Assuming a plausible discovery strategy, the Legacy Survey of Space and Time (LSST) and the Zwicky…
The development of synoptic sky surveys has led to a massive amount of data for which resources needed for analysis are beyond human capabilities. To process this information and to extract all possible knowledge, machine learning…
The importance of using fast and automatic methods to classify variable stars for large amounts of data is undeniable. There have been many attempts to classify variable stars by traditional algorithms like Random Forest. In recent years,…
Correctly interpreting observations of sources such as type Ia supernovae (SNe Ia) require knowledge of the power spectrum of matter on AU scales - which is very hard to model accurately. Because under-dense regions account for much of the…
The scientific study of the Solar System's minor bodies ultimately starts with a search for those bodies. This chapter presents a review of the use of machine learning techniques to find moving objects, both natural and artificial, in…
In recent years the amount of publicly available astronomical data has increased exponentially, with a remarkable example being large scale multiepoch photometric surveys. This wealth of data poses challenges to the classical methodologies…
The detection and reconstruction of gravitational waves from core-collapse supernovae (CCSN) present significant challenges due to the highly stochastic nature of the signals and the complexity of detector noise. In this work, we introduce…
Supernova surveys can be used to study a variety of subjects such as: (i) cosmology through type Ia supernovae (SNe), (ii) star-formation rates through core-collapse SNe, and (iii) supernova properties and their connection to host galaxy…
When performing data classification over a stream of continuously occurring instances, a key challenge is to develop an open-world classifier that anticipates instances from an unknown class. Studies addressing this problem, typically…
New time-domain surveys, such as the Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST), will observe millions of transient alerts each night, making standard approaches of visually identifying new and interesting transients…