Deep Attention-Based Supernovae Classification of Multi-Band Light-Curves

• URL: http://arxiv.org/abs/2201.08482v1
• Date: Thu, 20 Jan 2022 22:48:40 GMT
• Title: Deep Attention-Based Supernovae Classification of Multi-Band Light-Curves
• Authors: \'Oscar Pimentel, Pablo A. Est\'evez, Francisco F\"orster
• Abstract summary: supernovae (SNe) are relatively uncommon objects compared to other classes of variable events. processing of multi-band light-curves is a challenging task due to the highly irregular cadence, long time gaps, missing-values, low number of observations. We propose a Deep Attention model called TimeModAttn to classify multi-band light-curves of different SN types. Second, we propose a model for the synthetic generation of SN multi-band light-curves based on the Supernova Parametric Model (SPM)
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In astronomical surveys, such as the Zwicky Transient Facility (ZTF), supernovae (SNe) are relatively uncommon objects compared to other classes of variable events. Along with this scarcity, the processing of multi-band light-curves is a challenging task due to the highly irregular cadence, long time gaps, missing-values, low number of observations, etc. These issues are particularly detrimental for the analysis of transient events with SN-like light-curves. In this work, we offer three main contributions. First, based on temporal modulation and attention mechanisms, we propose a Deep Attention model called TimeModAttn to classify multi-band light-curves of different SN types, avoiding photometric or hand-crafted feature computations, missing-values assumptions, and explicit imputation and interpolation methods. Second, we propose a model for the synthetic generation of SN multi-band light-curves based on the Supernova Parametric Model (SPM). This allows us to increase the number of samples and the diversity of the cadence. The TimeModAttn model is first pre-trained using synthetic light-curves in a semi-supervised learning scheme. Then, a fine-tuning process is performed for domain adaptation. The proposed TimeModAttn model outperformed a Random Forest classifier, increasing the balanced-$F_1$score from $\approx.525$ to $\approx.596$. The TimeModAttn model also outperformed other Deep Learning models, based on Recurrent Neural Networks (RNNs), in two scenarios: late-classification and early-classification. Finally, we conduct interpretability experiments. High attention scores are obtained for observations earlier than and close to the SN brightness peaks, which are supported by an early and highly expressive learned temporal modulation.

Related papers

• Moirai-MoE: Empowering Time Series Foundation Models with Sparse Mixture of Experts [103.725112190618]
  This paper introduces Moirai-MoE, using a single input/output projection layer while delegating the modeling of diverse time series patterns to the sparse mixture of experts. Extensive experiments on 39 datasets demonstrate the superiority of Moirai-MoE over existing foundation models in both in-distribution and zero-shot scenarios.
  arXiv  Detail & Related papers  (2024-10-14T13:01:11Z)
• One More Step: A Versatile Plug-and-Play Module for Rectifying Diffusion Schedule Flaws and Enhancing Low-Frequency Controls [77.42510898755037]
  One More Step (OMS) is a compact network that incorporates an additional simple yet effective step during inference. OMS elevates image fidelity and harmonizes the dichotomy between training and inference, while preserving original model parameters. Once trained, various pre-trained diffusion models with the same latent domain can share the same OMS module.
  arXiv  Detail & Related papers  (2023-11-27T12:02:42Z)
• OpenSTL: A Comprehensive Benchmark of Spatio-Temporal Predictive Learning [67.07363529640784]
  We propose OpenSTL to categorize prevalent approaches into recurrent-based and recurrent-free models. We conduct standard evaluations on datasets across various domains, including synthetic moving object trajectory, human motion, driving scenes, traffic flow and forecasting weather. We find that recurrent-free models achieve a good balance between efficiency and performance than recurrent models.
  arXiv  Detail & Related papers  (2023-06-20T03:02:14Z)
• Grasping Core Rules of Time Series through Pure Models [6.849905754473385]
  PureTS is a network with three pure linear layers that achieved state-of-the-art in 80% of the long sequence prediction tasks. We discuss the potential of pure linear layers in both phenomena and essence.
  arXiv  Detail & Related papers  (2022-08-15T10:22:15Z)
• Supernova Light Curves Approximation based on Neural Network Models [53.180678723280145]
  Photometric data-driven classification of supernovae becomes a challenge due to the appearance of real-time processing of big data in astronomy. Recent studies have demonstrated the superior quality of solutions based on various machine learning models. We study the application of multilayer perceptron (MLP), bayesian neural network (BNN), and normalizing flows (NF) to approximate observations for a single light curve.
  arXiv  Detail & Related papers  (2022-06-27T13:46:51Z)
• TSEM: Temporally Weighted Spatiotemporal Explainable Neural Network for Multivariate Time Series [0.0]
  We present a model-agnostic, model-specific approach to time series deep learning. We show that TSEM outperforms XCM in terms of accuracy, while also satisfying a number of interpretability criteria.
  arXiv  Detail & Related papers  (2022-05-25T18:54:25Z)
• Real-time Detection of Anomalies in Multivariate Time Series of Astronomical Data [0.0]
  Astronomical transients are stellar objects that become temporarily brighter on various timescales. New astronomical sky surveys are observing unprecedented numbers of multi-wavelength transients. We present two novel methods that aim to quickly and automatically detect anomalous transient light curves in real-time.
  arXiv  Detail & Related papers  (2021-12-15T19:02:54Z)
• Real-time detection of anomalies in large-scale transient surveys [0.0]
  We present two novel methods of automatically detecting anomalous transient light curves in real-time. Both methods are based on the simple idea that if the light curves from a known population of transients can be accurately modelled, any deviations from model predictions are likely anomalies.
  arXiv  Detail & Related papers  (2021-10-29T18:29:25Z)
• ModeRNN: Harnessing Spatiotemporal Mode Collapse in Unsupervised Predictive Learning [75.2748374360642]
  We propose ModeRNN, which introduces a novel method to learn hidden structured representations between recurrent states. Across the entire dataset, different modes result in different responses on the mixtures of slots, which enhances the ability of ModeRNN to build structured representations.
  arXiv  Detail & Related papers  (2021-10-08T03:47:54Z)
• Kernel and Rich Regimes in Overparametrized Models [69.40899443842443]
  We show that gradient descent on overparametrized multilayer networks can induce rich implicit biases that are not RKHS norms. We also demonstrate this transition empirically for more complex matrix factorization models and multilayer non-linear networks.
  arXiv  Detail & Related papers  (2020-02-20T15:43:02Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.