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Register a new userModels and Simulations for the Photometric LSST Astronomical Time Series Classification Challenge (PLAsTiCC)
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We describe the simulated data sample for the Photometric LSST Astronomical Time Series Classification Challenge (PLAsTiCC), a publicly available challenge to classify transient and variable events that will be observed by the Large Synoptic Survey Telescope (LSST), a new facility expected to start in the early 2020s. The challenge was hosted by Kaggle, ran from 2018 September 28 to 2018 December 17, and included 1,094 teams competing for prizes. Here we provide details of the 18 transient and variable source models, which were not revealed until after the challenge, and release the model libraries at https://doi.org/10.5281/zenodo.2612896. We describe the LSST Operations Simulator used to predict realistic observing conditions, and we describe the publicly available SNANA simulation code used to transform the models into observed fluxes and uncertainties in the LSST passbands (ugrizy). Although PLAsTiCC has finished, the publicly available models and simulation tools are being used within the astronomy community to further improve classification, and to study contamination in photometrically identified samples of type Ia supernova used to measure properties of dark energy. Our simulation framework will continue serving as a platform to improve the PLAsTiCC models, and to develop new models.
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Next-generation surveys like the Legacy Survey of Space and Time (LSST) on the Vera C. Rubin Observatory will generate orders of magnitude more discoveries of transients and variable stars than previous surveys. To prepare for this data deluge, we developed the Photometric LSST Astronomical Time-series Classification Challenge (PLAsTiCC), a competition which aimed to catalyze the development of robust classifiers under LSST-like conditions of a non-representative training set for a large photometric test set of imbalanced classes. Over 1,000 teams participated in PLAsTiCC, which was hosted in the Kaggle data science competition platform between Sep 28, 2018 and Dec 17, 2018, ultimately identifying three winners in February 2019. Participants produced classifiers employing a diverse set of machine learning techniques including hybrid combinations and ensemble averages of a range of approaches, among them boosted decision trees, neural networks, and multi-layer perceptrons. The strong performance of the top three classifiers on Type Ia supernovae and kilonovae represent a major improvement over the current state-of-the-art within astronomy. This paper summarizes the most promising methods and evaluates their results in detail, highlighting future directions both for classifier development and simulation needs for a next generation PLAsTiCC data set.
Classification of transient and variable light curves is an essential step in using astronomical observations to develop an understanding of their underlying physical processes. However, upcoming deep photometric surveys, including the Large Synoptic Survey Telescope (LSST), will produce a deluge of low signal-to-noise data for which traditional labeling procedures are inappropriate. Probabilistic classification is more appropriate for the data but are incompatible with the traditional metrics used on deterministic classifications. Furthermore, large survey collaborations intend to use these classification probabilities for diverse science objectives, indicating a need for a metric that balances a variety of goals. We describe the process used to develop an optimal performance metric for an open classification challenge that seeks probabilistic classifications and must serve many scientific interests. The Photometric LSST Astronomical Time-series Classification Challenge (PLAsTiCC) is an open competition aiming to identify promising techniques for obtaining classification probabilities of transient and variable objects by engaging a broader community both within and outside astronomy. Using mock classification probability submissions emulating archetypes of those anticipated of PLAsTiCC, we compare the sensitivity of metrics of classification probabilities under various weighting schemes, finding that they yield qualitatively consistent results. We choose as a metric for PLAsTiCC a weighted modification of the cross-entropy because it can be meaningfully interpreted. Finally, we propose extensions of our methodology to ever more complex challenge goals and suggest some guiding principles for approaching the choice of a metric of probabilistic classifications.
Future surveys such as the Legacy Survey of Space and Time (LSST) of the Vera C. Rubin Observatory will observe an order of magnitude more astrophysical transient events than any previous survey before. With this deluge of photometric data, it will be impossible for all such events to be classified by humans alone. Recent efforts have sought to leverage machine learning methods to tackle the challenge of astronomical transient classification, with ever improving success. Transformers are a recently developed deep learning architecture, first proposed for natural language processing, that have shown a great deal of recent success. In this work we develop a new transformer architecture, which uses multi-head self attention at its core, for general multi-variate time-series data. Furthermore, the proposed time-series transformer architecture supports the inclusion of an arbitrary number of additional features, while also offering interpretability. We apply the time-series transformer to the task of photometric classification, minimising the reliance of expert domain knowledge for feature selection, while achieving results comparable to state-of-the-art photometric classification methods. We achieve a weighted logarithmic-loss of 0.507 on imbalanced data in a representative setting using data from the Photometric LSST Astronomical Time-Series Classification Challenge (PLAsTiCC). Moreover, we achieve a micro-averaged receiver operating characteristic area under curve of 0.98 and micro-averaged precision-recall area under curve of 0.87.
Blazars can be divided into two subtypes, flat spectrum radio quasars (FSRQs) and BL Lac objects, which have been distinguished phenomenologically by the strength of their optical emission lines, while their physical nature and relationship are still not fully understood. In this paper, we focus on the differences in their variability. We characterize the blazar variability using the Ornstein-Uhlenbeck (OU) process, and investigate the features that are discriminative for the two subtypes. We used optical photometric and polarimetric data obtained with the 1.5-m Kanata telescope for 2008-2014. We found that four features, namely the variation amplitude, characteristic timescale, and non-stationarity of the variability obtained from the light curves and the median of the degree of polarization (PD), are essential for distinguishing between FSRQs and BL Lac objects. FSRQs are characterized by rare and large flares, while the variability of BL Lac objects can be reproduced with a stationary OU process with relatively small amplitudes. The characteristics of the variability are governed not by the differences in the jet structure between the subtypes, but by the peak frequency of the synchrotron emission. This implies that the nature of the variation in the jets is common in FSRQs and BL Lac objects. We found that BL Lac objects tend to have high PD medians, which suggests that they have a stable polarization component. FSRQs have no such component, possibly because of a strong Compton cooling effect in sub-pc scale jets.
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 threshold. We estimate the photometric redshifts (photo-z) of SNe in our sample by fitting the SN light curves using the SALT2 model with nested sampling. We obtain a mean bias ($left<z_mathrm{phot}-z_mathrm{spec}right>$) of 0.012 with $sigmaleft( frac{z_mathrm{phot}-z_mathrm{spec}}{1+z_mathrm{spec}}right) = 0.0294$ without using a host-galaxy photo-z prior, and a mean bias ($left<z_mathrm{phot}-z_mathrm{spec}right>$) of 0.0017 with $sigmaleft( frac{z_mathrm{phot}-z_mathrm{spec}}{1+z_mathrm{spec}}right) = 0.0116$ using a host-galaxy photo-z prior. Assuming a flat $Lambda CDM$ model with $Omega_m=0.3$, we obtain $Omega_m$ of $0.305pm0.008$ (statistical errors only), using the simulated LSST sample of photometric SNe Ia (with intrinsic scatter $sigma_mathrm{int}=0.11$) derived using our methodology without using host-galaxy photo-z prior. Our method will help boost the power of SNe from the LSST as cosmological probes.
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