Related papers: Photometric Supernova Classification With Machine …
Upcoming photometric surveys will discover tens of thousands of Type Ia supernovae (SNe Ia), vastly outpacing the capacity of our spectroscopic resources. In order to maximize the science return of these observations in the absence of…
The identification of light sources represents a task of utmost importance for the development of multiple photonic technologies. Over the last decades, the identification of light sources as diverse as sunlight, laser radiation and…
Classifying transients based on multi band light curves is a challenging but crucial problem in the era of GAIA and LSST since the sheer volume of transients will make spectroscopic classification unfeasible. Here we present a nonparametric…
We investigate the performance of some common machine learning techniques in identifying BHB stars from photometric data. To train the machine learning algorithms, we use previously published spectroscopic identifications of BHB stars from…
The volume of data being collected in solar physics has exponentially increased over the past decade and with the introduction of the $\textit{Daniel K. Inouye Solar Telescope}$ (DKIST) we will be entering the age of petabyte solar data.…
In the era of large all-sky surveys, there will be a need for rapid, automatic classifications of newly discovered transient objects. Our focus here is the classification of supernovae (SNe). We consider random forest machine learning…
This paper presents a novel method for determining the probability that a supernova candidate belongs to a known supernova type (such as Ia, Ibc, IIL, \emph{etc.}), using its photometric information alone. It is validated with Monte Carlo,…
Vast amounts of astronomical photometric data are generated from various projects, requiring significant effort to identify variable stars and other object classes. In light of this, a general, widely applicable classification framework…
We have publicly released a blinded mix of simulated SNe, with types (Ia, Ib, Ic, II) selected in proportion to their expected rate. The simulation is realized in the griz filters of the Dark Energy Survey (DES) with realistic observing…
Time-domain astronomy is entering a new era as wide-field surveys with higher cadences allow for more discoveries than ever before. The field has seen an increased use of machine learning and deep learning for automated classification of…
Upcoming large-scale ground- and space- based supernova surveys will face a challenge identifying supernova candidates largely without the use of spectroscopy. Over the past several years, a number of supernova identification schemes have…
Photometric classification of Type Ia supernovae (SNe Ia) is critical for cosmological studies but remains difficult due to class imbalance and observational noise. While deep learning models have been explored, they are often…
In the field of transmission electron microscopy, data interpretation often lags behind acquisition methods, as image processing methods often have to be manually tailored to individual datasets. Machine learning offers a promising approach…
In this work we explore the possibility of applying machine learning methods designed for one-dimensional problems to the task of galaxy image classification. The algorithms used for image classification typically rely on multiple costly…
Breast cancer is one of the most serious types of cancer that can occur in women. The automatic diagnosis of breast cancer by analyzing histological images (HIs) is important for patients and their prognosis. The classification of HIs…
Deep metric learning aims to learn a function mapping image pixels to embedding feature vectors that model the similarity between images. Two major applications of metric learning are content-based image retrieval and face verification. For…
We report results from the Supernova Photometric Classification Challenge (SNPCC), a publicly released mix of simulated supernovae (SNe), with types (Ia, Ibc, and II) selected in proportion to their expected rate. The simulation was…
Large numbers of supernovae (SNe) have been discovered in recent years, and many more will be found in the near future. Once discovered, further study of a SN and its possible use as an astronomical tool (e.g., as a distance estimator)…
Controlling defects in semiconductor processes is important for maintaining yield, improving production cost, and preventing time-dependent critical component failures. Electron beam-based imaging has been used as a tool to survey wafers in…
The scientific value of the next generation of large continuum surveys would be greatly increased if the redshifts of the newly detected sources could be rapidly and reliably estimated. Given the observational expense of obtaining…