scholarly journals On the cosmological performance of photometrically classified supernovae with machine learning

2020 ◽  
Vol 497 (3) ◽  
pp. 2974-2991
Author(s):  
Marcelo Vargas dos Santos ◽  
Miguel Quartin ◽  
Ribamar R R Reis
Keyword(s):  
Machine Learning ◽  
Mean Squared Error ◽  
Light Curves ◽  
Observational Cosmology ◽  
Automated Classification ◽  
Cosmological Constraints ◽  
Search Processes ◽  
Type Ia ◽  
Ia Supernovae

ABSTRACT The efficient classification of different types of supernovae 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, 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 light-curves from the Supernova Photometric Classification Challenge, released in 2010. We study the use of different feature sets for the light-curves and many different ML pipelines based on either decision-tree ensembles or automated search processes. To construct the final catalogues we propose a threshold selection method, by employing a bias-variance tradeoff. This is a very robust and efficient way to minimize the mean squared error. With this method, we were able to obtain very strong cosmological constraints, which allowed us to keep $\sim 75{{\ \rm per\ cent}}$ of the total information in the Type Ia supernovae when using the SALT2 feature set, and $\sim 33{{\ \rm per\ cent}}$ for the other cases (based either on the Newling model or on standard wavelet decomposition).

Machine Learning Approaches for Supernovae Classification

2017 ◽  
pp. 207-219 ◽  
Author(s):  
Surbhi Agrawal ◽  
Kakoli Bora ◽  
Swati Routh
Keyword(s):  
Machine Learning ◽  
Machine Learning Techniques ◽  
Type Ia Supernovae ◽  
Learning Approaches ◽  
Data Set ◽  
Learning Techniques ◽  
Type Ia ◽  
Ia Supernovae ◽  
The Universe

In this chapter, authors have discussed few machine learning techniques and their application to perform the supernovae classification. Supernovae has various types, mainly categorized into two important types. Here, focus is given on the classification of Type-Ia supernova. Astronomers use Type-Ia supernovae as “standard candles” to measure distances in the Universe. Classification of supernovae is mainly a matter of concern for the astronomers in the absence of spectra. Through the application of different machine learning techniques on the data set authors have tried to check how well classification of supernovae can be performed using these techniques. Data set used is available at Riess et al. (2007) (astro-ph/0611572).

scholarly journals ParSNIP: Generative Models of Transient Light Curves with Physics-enabled Deep Learning

2021 ◽  
Vol 162 (6) ◽  
pp. 275
Author(s):  
Kyle Boone
Keyword(s):  
Three Dimensional ◽  
Generative Models ◽  
Light Curves ◽  
Type Ia Supernovae ◽  
Data Sets ◽  
Data Set ◽  
Out Of Sample ◽  
Type Ia ◽  
Ia Supernovae

Abstract We present a novel method to produce empirical generative models of all kinds of astronomical transients from data sets of unlabeled light curves. Our hybrid model, which we call ParSNIP, uses a neural network to model the unknown intrinsic diversity of different transients and an explicit physics-based model of how light from the transient propagates through the universe and is observed. The ParSNIP model predicts the time-varying spectra of transients despite only being trained on photometric observations. With a three-dimensional intrinsic model, we are able to fit out-of-sample multiband light curves of many different kinds of transients with model uncertainties of 0.04–0.06 mag. The representation learned by the ParSNIP model is invariant to redshift, so it can be used to perform photometric classification of transients even with heavily biased training sets. Our classification techniques significantly outperform state-of-the-art methods on both simulated (PLAsTiCC) and real (PS1) data sets with 2.3× and 2× less contamination, respectively, for classification of Type Ia supernovae. We demonstrate how our model can identify previously unobserved kinds of transients and produce a sample that is 90% pure. The ParSNIP model can also estimate distances to Type Ia supernovae in the PS1 data set with an rms of 0.150 ± 0.007 mag compared to 0.155 ± 0.008 mag for the SALT2 model on the same sample. We discuss how our model could be used to produce distance estimates for supernova cosmology without the need for explicit classification.

Machine Learning Approaches for Supernovae Classification

2020 ◽  
pp. 294-306
Author(s):  
Surbhi Agrawal ◽  
Kakoli Bora ◽  
Swati Routh
Keyword(s):  
Machine Learning ◽  
Machine Learning Techniques ◽  
Type Ia Supernovae ◽  
Learning Approaches ◽  
Data Set ◽  
Learning Techniques ◽  
Type Ia ◽  
Ia Supernovae ◽  
The Universe

In this chapter, authors have discussed few machine learning techniques and their application to perform the supernovae classification. Supernovae has various types, mainly categorized into two important types. Here, focus is given on the classification of Type-Ia supernova. Astronomers use Type-Ia supernovae as “standard candles” to measure distances in the Universe. Classification of supernovae is mainly a matter of concern for the astronomers in the absence of spectra. Through the application of different machine learning techniques on the data set authors have tried to check how well classification of supernovae can be performed using these techniques. Data set used is available at Riess et al. (2007) (astro-ph/0611572).

scholarly journals Gravitational Collapse versus Thermonuclear Explosion of Degenerate Stellar Cores

1994 ◽  
Vol 147 ◽  
pp. 186-213
Author(s):  
J. Isern ◽  
R. Canal
Keyword(s):  
Neutron Star ◽  
White Dwarfs ◽  
Light Curves ◽  
Type Ia Supernovae ◽  
Radioactive Elements ◽  
Border Line ◽  
Thermonuclear Explosion ◽  
Type Ia ◽  
Γ Rays ◽  
Ia Supernovae

AbstractIn this paper we review the behavior of growing stellar degenerate cores. It is shown that ONeMg white dwarfs and cold CO white dwarfs can collapse to form a neutron star. This collapse is completely silent since the total amount of radioactive elements that are expelled is very small and a burst of γ-rays is never produced. In the case of an explosion (always carbonoxygen cores), the outcome fits quite well the observed properties of Type Ia supernovae. Nevertheless, the light curves and the velocities measured at maximum are very homogeneous and the diversity introduced by igniting at different densities is not enough to account for the most extreme cases observed. It is also shown that a promising way out of this problem could be the He-induced detonation of white dwarfs with different masses. Finally, we outline that the location of the border line which separetes explosion from collapse strongly depends on the input physics adopted.

scholarly journals Classification of multiwavelength transients with Machine Learning

2020 ◽  
Author(s):  
K Sooknunan ◽  
M Lochner ◽  
Bruce A Bassett ◽  
H V Peiris ◽  
R Fender ◽  
...  
Keyword(s):  
Machine Learning ◽  
Small Sample ◽  
Light Curves ◽  
Machine Learning Techniques ◽  
Optical Data ◽  
Test Time ◽  
Test Accuracy ◽  
Training Set ◽  
The Impact

Abstract With the advent of powerful telescopes such as the Square Kilometer Array and the Vera C. Rubin Observatory, we are entering an era of multiwavelength transient astronomy that will lead to a dramatic increase in data volume. Machine learning techniques are well suited to address this data challenge and rapidly classify newly detected transients. We present a multiwavelength classification algorithm consisting of three steps: (1) interpolation and augmentation of the data using Gaussian processes; (2) feature extraction using wavelets; (3) classification with random forests. Augmentation provides improved performance at test time by balancing the classes and adding diversity into the training set. In the first application of machine learning to the classification of real radio transient data, we apply our technique to the Green Bank Interferometer and other radio light curves. We find we are able to accurately classify most of the eleven classes of radio variables and transients after just eight hours of observations, achieving an overall test accuracy of 78%. We fully investigate the impact of the small sample size of 82 publicly available light curves and use data augmentation techniques to mitigate the effect. We also show that on a significantly larger simulated representative training set that the algorithm achieves an overall accuracy of 97%, illustrating that the method is likely to provide excellent performance on future surveys. Finally, we demonstrate the effectiveness of simultaneous multiwavelength observations by showing how incorporating just one optical data point into the analysis improves the accuracy of the worst performing class by 19%.

scholarly journals Constraints on the density distribution of type Ia supernovae ejecta inferred from late-time light-curve flattening

2020 ◽  
Vol 493 (4) ◽  
pp. 5617-5624
Author(s):  
Doron Kushnir ◽  
Eli Waxman
Keyword(s):  
Light Curve ◽  
Weighted Average ◽  
Far Infrared ◽  
Average Density ◽  
Light Curves ◽  
Type Ia Supernovae ◽  
Late Time ◽  
Simple Method ◽  
Type Ia ◽  
Ia Supernovae

ABSTRACT The finite time, τdep, over which positrons from β+ decays of 56Co deposit energy in type Ia supernovae ejecta lead, in case the positrons are trapped, to a slower decay of the bolometric luminosity compared to an exponential decline. Significant light-curve flattening is obtained when the ejecta density drops below the value for which τdep equals the 56Co lifetime. We provide a simple method to accurately describe this ‘delayed deposition’ effect, which is straightforward to use for analysis of observed light curves. We find that the ejecta heating is dominated by delayed deposition typically from 600 to 1200 d, and only later by longer lived isotopes 57Co and 55Fe decay (assuming solar abundance). For the relatively narrow 56Ni velocity distributions of commonly studied explosion models, the modification of the light curve depends mainly on the 56Ni mass-weighted average density, 〈ρ〉t3. Accurate late-time bolometric light curves, which may be obtained with JWST far-infrared (far-IR) measurements, will thus enable to discriminate between explosion models by determining 〈ρ〉t3 (and the 57Co and 55Fe abundances). The flattening of light curves inferred from recent observations, which is uncertain due to the lack of far-IR data, is readily explained by delayed deposition in models with $\langle \rho \rangle t^{3} \approx 0.2\, \mathrm{M}_{\odot }\, (10^{4}\, \textrm{km}\, \textrm{s}^{-1})^{-3}$, and does not imply supersolar 57Co and 55Fe abundances.

Cosmological constraints on the variable modified Chaplygin gas model by using MCMC approach

2015 ◽  
Vol 24 (08) ◽  
pp. 1550059 ◽  
Author(s):  
Jian-bin Chen ◽  
Zhen-qi Liu ◽  
Lili Xing
Keyword(s):  
Apparent Horizon ◽  
Chaplygin Gas ◽  
Modified Chaplygin Gas ◽  
Acoustic Oscillations ◽  
Microwave Background ◽  
Mean Values ◽  
Cosmological Constraints ◽  
Variable Modified Chaplygin Gas ◽  
Type Ia ◽  
Ia Supernovae

We investigate the cosmological constraints on the variable modified Chaplygin gas (VMCG) model from the latest observational data: Union2 dataset of Type Ia supernovae (SNIa), the observational Hubble data (OHD), the baryon acoustic oscillations (BAO) and the cosmic microwave background (CMB) data. By using the Markov chain Monte Carlo (MCMC) method, we obtain the mean values of parameters in the flat model: [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text] and [Formula: see text]. Furthermore, we investigate the thermodynamical properties of VMCG model at apparent horizon, event horizon and particle horizon respectively.

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