Type Ia Supernovae (SNe Ia) spectra are compared regarding the coefficient of the largest wavelet scale in their decomposition. Two distinct subgroups were identified and their occurrence is discussed in light of use of SNe Ia as cosmological probes. Apart from the group of normal SNe, another trend characterised by intrinsically redder colours is consisted of many different SN events that exhibit diverse properties, including the interaction with the circumstellar material, the existence of specific shell-structure in or surrounding the SN ejecta or super-Chandrasekhar mass progenitors. Compared with the normal objects, these SNe may violate the standard width-luminosity correction, which could influence the cosmological results if they were all calibrated equally, since their fraction among SNe Ia is not negligible when performing precision cosmology. Using largest wavelet scale coefficient in combination with long-baseline B-I colours, we show how to disentangle SN intrinsic colour from the part that corresponds to the reddening due to dust extinction in the host galaxy in the SALT2 colour parameter c, discussing how the intrinsic colour differences may explain the different reddening laws for two subsamples. There are wavelength intervals for which the measured largest scale coefficient is invariant to the additional extinction applied to a spectrum. Combination of wavelet coefficients measured in different wavelength intervals can be used to develop a technique that allows for estimation of extinction.
We present improved photometric supernovae classification using deep recurrent neural networks. The main improvements over previous work are (i) the introduction of a time gate in the recurrent cell that uses the observational time as an input; (ii) greatly increased data augmentation including time translation, addition of Gaussian noise and early truncation of the lightcurve. For post Supernovae Photometric Classification Challenge (SPCC) data, using a training fraction of $5.2\%$ (1103 supernovae) of a representational dataset, we obtain a type Ia vs. non type Ia classification accuracy of …
Deep Recurrent Neural Networks for Supernovae Classification
University of Nottingham（诺丁汉大学）
我们应用能够学习复杂序列信息的深度循环神经网络对超新星\footnote{Code available at \href{https://github.com/adammoss/supernovae}{https://github.com/adammoss/supernovae}}进行分类。观测时间和过滤通量被用作网络的输入，但由于输入是不可知的，因此也可以包括其他数据，如宿主星系信息。使用超新星光度分类挑战（SPCC）数据，我们发现深度网络能够学习光变曲线，但是网络的性能对训练数据量高度敏感。对于50%的代表性SPCC数据集（围绕$10^4$超新星）的训练大小，我们获得了94.7%的Ia型与非Ia型分类准确度，0.986的受试者操作特征曲线AUC下的面积和SPCC品质因数$F_1=0.64$。当仅使用由SPCC定义的早期挑战的数据时，我们实现了93.1\%的分类准确度，0.977的AUC和$F_1=0.58$，结果几乎与整个光变曲线一样好。采用双向神经网络对超新星I、II、III型进行分类，分类精度为90.4%，AUC为0.974。我们还应用了一个预先训练的模型来获得分类概率作为时间的函数，并表明它可以给出超新星类型的早期指示。我们的方法是有竞争力的，与现有的算法，并为未来的大规模测光调查的应用。
The Vera C. Rubin Observatory will increase the number of observed supernovae (SNe) by an order of magnitude; however, it is impossible to spectroscopically confirm the class for all the SNe discovered. Thus, photometric classification is crucial but its accuracy depends on the not-yet-finalized observing strategy of Rubin Observatory's Legacy Survey of Space and Time (LSST). We quantitatively analyze the impact of the LSST observing strategy on SNe classification using simulated multi-band light curves from the Photometric LSST Astronomical Time-Series Classification Challenge (PLAsTiCC). Fir…
We present DASH (Deep Automated Supernova and Host classifier), a novel software package that automates the classification of the type, age, redshift, and host galaxy of supernova spectra. DASH makes use of a new approach that does not rely on iterative template matching techniques like all previous software, but instead classifies based on the learned features of each supernova's type and age. It has achieved this by employing a deep convolutional neural network to train a matching algorithm. This approach has enabled DASH to be orders of magnitude faster than previous tools, being able to ac…
Measuring the Properties of Dark Energy with Photometrically Classified Pan-STARRS Supernovae. I. Systematic Uncertainty from Core-Collapse Supernova Contamination
The recent $\sim 4 \, σ$ Hubble constant, $H_{0}$, tension is observed between the value of $H_{0}$ from the Cosmic Microwave Background (CMB) and Type Ia Supernovae (SNe Ia). It is a decade since this tension is excruciating the modern astrophysical community. To shed light on this problem is key to consider probes at intermediate redshifts between SNe Ia and CMB and reduce the uncertainty on $H_0$. Toward these goals, we fill the redshift gap by employing Gamma-Ray Bursts (GRBs) and Quasars (QSOs), reaching $z=9.4$ and $z=7.6$, respectively, combined with Baryonic Acoustic Oscillations (BAO)…
Supernova (SN) classification and redshift estimation using photometric data only have become very important for the Large Synoptic Survey Telescope (LSST), given the large number of SNe that LSST will observe and the impossibility of spectroscopically following up all the SNe. We investigate the performance of a SN classifier that uses SN colors to classify LSST SNe with the Random Forest classification algorithm. Our classifier results in an AUC of 0.98 which represents excellent classification. We are able to obtain a photometric SN sample containing 99$\%$ SNe Ia by choosing a probability …
The efficient classification of different types of supernova is one of the most important problems for observational cosmology. However, spectroscopic confirmation of most objects in upcoming photometric surveys, such as the The Rubin Observatory Legacy Survey of Space and Time (LSST), will be unfeasible. The development of automated classification processes based on photometry has thus become crucial. In this paper we investigate the performance of machine learning (ML) classification on the final cosmological constraints using simulated lightcurves from The Supernova Photometric Classificati…
We present a novel method of classifying Type Ia supernovae using convolutional neural networks, a neural network framework typically used for image recognition. Our model is trained on photometric information only, eliminating the need for accurate redshift data. Photometric data is pre-processed via 2D Gaussian process regression into two-dimensional images created from flux values at each location in wavelength-time space. These "flux heatmaps" of each supernova detection, along with "uncertainty heatmaps" of the Gaussian process uncertainty, constitute the dataset for our model. This prepr…
Université Paris-Saclay（巴黎萨克雷大学）；CNRS/IN2P3（法国国家科学研究中心/核物理与粒子物理研究所）；Université Clermont Auvergne（克莱蒙奥弗涅大学）；Swinburne University of Technology（斯威本科技大学）
We describe how the Fink broker early supernova Ia classifier optimizes its ML classifications by employing an active learning (AL) strategy. We demonstrate the feasibility of implementation of such strategies in the current Zwicky Transient Facility (ZTF) public alert data stream. We compare the performance of two AL strategies: uncertainty sampling and random sampling. Our pipeline consists of 3 stages: feature extraction, classification and learning strategy. Starting from an initial sample of 10 alerts (5 SN Ia and 5 non-Ia), we let the algorithm identify which alert should be added to the…
As part of the cosmology analysis using Type Ia Supernovae (SN Ia) in the Dark Energy Survey (DES), we present photometrically identified SN Ia samples using multi-band light-curves and host galaxy redshifts. For this analysis, we use the photometric classification framework SuperNNova (SNN; Möller et al. 2019) trained on realistic DES-like simulations. For reliable classification, we process the DES SN programme (DES-SN) data and introduce improvements to the classifier architecture, obtaining classification accuracies of more than 98 per cent on simulations. This is the first SN classificati…
We present a spatio-temporal AI framework that concurrently exploits both the spatial and time-variable features of gravitationally lensed supernovae in optical images to ultimately aid in future discoveries of such exotic transients in wide-field surveys. Our spatio-temporal engine is designed using recurrent convolutional layers, while drawing from recent advances in variational inference to quantify approximate Bayesian uncertainties via a confidence score. Using simulated Young Supernova Experiment (YSE) images of lensed and non-lensed supernovae as a showcase, we find that the use of time…
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 increasing as objects with new features are observed, and the next generation of large-surveys will only bring more variety to our attention. We apply the machine learning technique of multi-label classification to the spectra of supernovae. By measuring the probabilities of specific features or `tags' in the supernova spectra, we can compress the information fro…
Photometric classification of supernovae detected by the Zwicky Transient Facility using noise augmentation
Humboldt-Universität zu Berlin（柏林洪堡大学）；Deutsches Elektronen-Synchrotron（德国电子同步辐射加速器中心）；Leibniz-Institut für Astrophysik Potsdam (AIP)（波茨坦阿基米德天体物理研究所）；European Southern Observatory（欧洲南方天文台）；University of Washington（华盛顿大学）；Trinity College Dublin, The University of Dublin（都柏林三一学院）；California Institute of Technology（加州理工学院）；National Central University（中央大学）；Lancaster University（兰卡斯特大学）；Institute of Space Sciences (ICE, CSIC)（空间科学研究所）；Institut d’Estudis Espacials de Catalunya (IEEC)（加泰罗尼亚空间研究院）
