Related papers: A Convolutional Neural Network Approach to Superno…
Organic scintillators are important in advancing nuclear detection and particle physics experiments. Achieving a high signal-to-noise ratio necessitates efficient pulse shape discrimination techniques to accurately distinguish between…
Future time-domain surveys for transient events in the near- and mid-infrared bands will significantly extend our understanding about the physics of the early Universe. In this paper we study the implications of a deep (~27 mag), long-term…
Various convolutional neural networks (CNNs) were developed recently that achieved accuracy comparable with that of human beings in computer vision tasks such as image recognition, object detection and tracking, etc. Most of these networks,…
In this work, six convolutional neural networks (CNNs) have been trained based on %different feature images and arrays from the database including 15,638 superflare candidates on solar-type stars, which are collected from the three-years…
The main observational properties and resulting classification of supernovae (SNe) are briefly reviewed. Then we discuss the progress in modeling of two basic types of SNe - the thermonuclear and core-collapse ones, with special emphasis…
We present a systematic search for wide-separation (Einstein radius >1.5"), galaxy-scale strong lenses in the 30 000 sq.deg of the Pan-STARRS 3pi survey on the Northern sky. With long time delays of a few days to weeks, such systems are…
Convolutional Neural Networks (CNNs) are a class of artificial neural networks whose computational blocks use convolution, together with other linear and non-linear operations, to perform classification or regression. This paper explores…
The remarkable uniformity of Type Ia supernovae (SNe Ia) has allowed astronomers to use them as distance indicators to measure the properties and expansion history of the Universe. However, SNe Ia exhibit intrinsic variation in both their…
With the rapid growth in the semiconductor industry, it is becoming critical to detect and classify increasingly smaller patterned defects. Recently machine learning, including deep learning, has come to aid in this endeavor in a big way.…
Strongly lensed type Ia supernovae (SNe Ia) provide a unique cosmological probe to address the Hubble tension problem in cosmology. In addition to the sensitivity of the time delays to the value of the Hubble constant, the transient and…
Supernovae (SNe) could be powerful probes of the properties of stars and galaxies at high redshifts in future surveys. Wide fields and longer exposure times are required to offset diminishing star formation rates and lower fluxes to detect…
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…
Determining the material category of a surface from an image is a demanding task in perception that is drawing increasing attention. Following the recent remarkable results achieved for image classification and object detection utilising…
This work is focused on the morphological classification of galaxies following the Hubble sequence in which the different classes are arranged in a hierarchy. The proposed method, BCNN, is composed of two main modules. First, a…
In this work we describe a Convolutional Neural Network (CNN) to accurately predict the scene illumination. Taking image patches as input, the CNN works in the spatial domain without using hand-crafted features that are employed by most…
Convolutional neural networks for computer vision are fairly intuitive. In a typical CNN used in image classification, the first layers learn edges, and the following layers learn some filters that can identify an object. But CNNs for…
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…
The classification of stripped-envelope supernovae (SE-SNe) is revisited using modern data-sets. Spectra are analysed using an empirical method to "blindly" categorise SNe according to spectral feature strength and appearance. This method…
Deep Convolutional Neural Networks (CNNs) have demonstrated excellent performance in image classification, but still show room for improvement in object-detection tasks with many categories, in particular for cluttered scenes and occlusion.…
Empirically, Type Ia supernovae are the most useful, precise, and mature tools for determining astronomical distances. Acting as calibrated candles they revealed the presence of dark energy and are being used to measure its properties.…