Related papers: A Convolutional Neural Network Approach to Superno…
Spiking neural networks (SNNs) have closer dynamics to the brain than current deep neural networks. Their low power consumption and sample efficiency make these networks interesting. Recently, several deep convolutional spiking neural…
Text classification is a fundamental task in natural language processing (NLP). Several recent studies show the success of deep learning on text processing. Convolutional neural network (CNN), as a popular deep learning model, has shown…
SN1991bg-like supernovae are a distinct subclass of thermonuclear supernovae (SNe Ia). Their spectral and photometric peculiarities indicate their progenitors and explosion mechanism differ from `normal' SNe Ia. One method of determining…
Narrow absorption features in nearby supernova (SN) spectra are a powerful diagnostic of the slow-moving material in the line of sight: they are extensively used to infer dust extinction from the host galaxies, and they can also serve in…
Supernovae classes have been defined phenomenologically, based on spectral features and time series data, since the specific details of the physics of the different explosions remain unrevealed. However, the number of these classes is…
Surface inspection systems are an important application domain for computer vision, as they are used for defect detection and classification in the manufacturing industry. Existing systems use hand-crafted features which require extensive…
We use a new technique to extract the spectrum of a supernova from that of the contaminating background of its host galaxy, and apply it to the specific case of high-redshift Type Ia supernova (SN Ia) spectroscopy. The algorithm is based on…
In response to a recently reported observation of evidence for two classes of Type Ia Supernovae (SNe Ia) distinguished by their brightness in the rest-frame near ultraviolet (NUV), we search for the phenomenon in publicly available…
Strong gravitational lensing of distant supernovae (SNe), particularly Type Ia's, has some exploitable properties not available when other sorts of cosmologically distant sources are lensed. One such property is that the ``standard candle''…
We propose a new sequential classification model for astronomical objects based on a recurrent convolutional neural network (RCNN) which uses sequences of images as inputs. This approach avoids the computation of light curves or difference…
Wide field surveys will soon be discovering Type Ia supernovae (SNe) at rates of several thousand per year. Spectroscopic follow-up can only scratch the surface for such enormous samples, so these extensive data sets will only be useful to…
Strong gravitational lensing and microlensing of supernovae (SNe) are emerging as a new probe of cosmology and astrophysics in recent years. We provide an overview of this nascent research field, starting with a summary of the first…
This study investigate the effectiveness of using Deep Learning (DL) for the classification of planetary nebulae (PNe). It focusses on distinguishing PNe from other types of objects, as well as their morphological classification. We adopted…
Determination of the explosion type of supernova remnants (SNRs) can be challenging, as SNRs are hundreds to thousands of years old and supernovae (SNe) are classified based on spectral properties days after explosion. Previous studies of…
In the era of large-scale photometric surveys, scalable and robust methods for classifying supernova (SN) populations are increasingly necessary. Often, spectroscopy is essential in addition to photometry to reliably classify SNe; however,…
Type Ia supernovae (SNe Ia) are one of the major tools to determine the cosmological parameters. Utilizing them as distance indicators, it is possible to geometrically survey the universe. To this end, the intrinsic scatter in the…
The current classification scheme for supernovae is presented. The main observational features of the supernova types are described and the physical implications briefly addressed. Differences between the homogeneous thermonuclear type Ia…
We present new diagnostic tools for distinguishing supernova remnants (SNRs) from HII regions. Up to now, sources with flux ratio [S II]/H$\rm{\alpha}$ higher than 0.4 have been considered as SNRs. Here, we present the combinations of three…
Supernovae (SNe) are stellar explosions driven by gravitational or thermonuclear energy, observed as electromagnetic radiation emitted over weeks or more. In all known SNe, this radiation comes from internal energy deposited in the…
The spectral energy distribution (SED) sequence for type Ia supernovae (SN Ia) is modeled by an artificial neural network. The SN Ia luminosity is characterized as a function of phase, wavelength, a color parameter and a decline rate…