SCONE: Supernova Classification with a Convolutional Neural Network

Qu, Helen; Sako, Masao; Möller, Anais; Doux, Cyrille

doi:10.48550/arxiv.2106.04370

Cited by 2 publications

(2 citation statements)

References 23 publications

(27 reference statements)

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“…We use the open-source software george (Foreman-Mackey et al 2014) to interpolate the data points using multivariate Gaussian process regression (MGPR) (Pruzhinskaya et al 2019;Qu et al 2021) in a normalized wavelength space and temporal phase space. Our MGPR uses a mean function η and a covariance function K (also called the kernel).…”

Section: Data Pre-processingmentioning

confidence: 99%

Searching for Anomalies in the ZTF Catalog of Periodic Variable Stars

Chan,

Villar,

Cheung

et al. 2021

Preprint

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Periodic variables illuminate the physical processes of stars throughout their lifetime. Wide-field surveys continue to increase our discovery rates of periodic variable stars. Automated approaches are essential to identify interesting periodic variable stars for multi-wavelength and spectroscopic followup. Here, we present a novel unsupervised machine learning approach to hunt for anomalous periodic variables using phase-folded light curves presented in the Zwicky Transient Facility Catalogue of Periodic Variable Stars by Chen et al. (2020). We use a convolutional variational autoencoder to learn a low dimensional latent representation, and we search for anomalies within this latent dimension via an isolation forest. We identify anomalies with irregular variability. Most of the top anomalies are likely highly variable Red Giants or Asymptotic Giant Branch stars concentrated in the Milky Way galactic disk; a fraction of the identified anomalies are more consistent with Young Stellar Objects. Detailed spectroscopic follow-up observations are encouraged to reveal the nature of these anomalies.

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Section: Data Pre-processingmentioning

confidence: 99%

Searching for Anomalies in the ZTF Catalog of Periodic Variable Stars

Chan,

Villar,

Cheung

et al. 2021

Preprint

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“…In that case, the multivariate probability distribution associated with the uncertainties σ x i and σ y i mean that each data point i is "spread out" both in the horizontal (rows, x) as well as the vertical (columns, y) directions. A CNN where a 2D data set (including uncertainties) is represented by images was used recently to classify supernovas (Qu et al, 2021).…”

Section: Cnn2d Imagesmentioning

confidence: 99%

The information of attribute uncertainties: what convolutional neural networks can learn about errors in input data

Rodrigues,

Abramo,

Hirata

2021

Preprint

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Errors in measurements are key to weighting the value of data, but are often neglected in Machine Learning (ML). We show how Convolutional Neural Networks (CNNs) are able to learn about the context and patterns of signal and noise, leading to improvements in the performance of classification methods. We construct a model whereby two classes of objects follow an underlying Gaussian distribution, and where the features (the input data) have varying, but known, levels of noise. This model mimics the nature of scientific data sets, where the noises arise as realizations of some random processes whose underlying distributions are known. The classification of these objects can then be performed using standard statistical techniques (e.g., least-squares minimization or Markov-Chain Monte Carlo), as well as ML techniques. This allows us to take advantage of a maximum likelihood approach to object classification, and to measure the amount by which the ML methods are incorporating the information in the input data uncertainties. We show that, when each data point is subject to different levels of noise (i.e., noises with different distribution functions), that information can be learned by the CNNs, raising the ML performance to at least the same level of the least-squares method -and sometimes even surpassing it. Furthermore, we show that, with varying noise levels, the confidence of the ML classifiers serves as a proxy for the underlying cumulative distribution function, but only if the information about specific input data uncertainties is provided to the CNNs.

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SCONE: Supernova Classification with a Convolutional Neural Network

Cited by 2 publications

References 23 publications

Searching for Anomalies in the ZTF Catalog of Periodic Variable Stars

Searching for Anomalies in the ZTF Catalog of Periodic Variable Stars

The information of attribute uncertainties: what convolutional neural networks can learn about errors in input data

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