Deep Learning and LLM-based Methods Applied to Stellar Lightcurve Classification
- URL: http://arxiv.org/abs/2404.10757v1
- Date: Tue, 16 Apr 2024 17:35:25 GMT
- Title: Deep Learning and LLM-based Methods Applied to Stellar Lightcurve Classification
- Authors: Yu-Yang Li, Yu Bai, Cunshi Wang, Mengwei Qu, Ziteng Lu, Roberto Soria, Jifeng Liu,
- Abstract summary: We present a comprehensive evaluation of deep-learning and large language model (LLM) based models for the automatic classification of variable star light curves.
Special emphasis is placed on Cepheids, RR Lyrae, and eclipsing binaries, examining the influence of observational cadence and phase distribution on classification precision.
We unveil StarWhisper LightCurve (LC), an innovative Series comprising three LLM-based models: LLM, multimodal large language model (MLLM), and Large Audio Language Model (LALM)
- Score: 7.592813175419603
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Light curves serve as a valuable source of information on stellar formation and evolution. With the rapid advancement of machine learning techniques, it can be effectively processed to extract astronomical patterns and information. In this study, we present a comprehensive evaluation of deep-learning and large language model (LLM) based models for the automatic classification of variable star light curves, based on large datasets from the Kepler and K2 missions. Special emphasis is placed on Cepheids, RR Lyrae, and eclipsing binaries, examining the influence of observational cadence and phase distribution on classification precision. Employing AutoDL optimization, we achieve striking performance with the 1D-Convolution+BiLSTM architecture and the Swin Transformer, hitting accuracies of 94\% and 99\% correspondingly, with the latter demonstrating a notable 83\% accuracy in discerning the elusive Type II Cepheids-comprising merely 0.02\% of the total dataset.We unveil StarWhisper LightCurve (LC), an innovative Series comprising three LLM-based models: LLM, multimodal large language model (MLLM), and Large Audio Language Model (LALM). Each model is fine-tuned with strategic prompt engineering and customized training methods to explore the emergent abilities of these models for astronomical data. Remarkably, StarWhisper LC Series exhibit high accuracies around 90\%, significantly reducing the need for explicit feature engineering, thereby paving the way for streamlined parallel data processing and the progression of multifaceted multimodal models in astronomical applications. The study furnishes two detailed catalogs illustrating the impacts of phase and sampling intervals on deep learning classification accuracy, showing that a substantial decrease of up to 14\% in observation duration and 21\% in sampling points can be realized without compromising accuracy by more than 10\%.
Related papers
- AstroM$^3$: A self-supervised multimodal model for astronomy [0.0]
We propose AstroM$3$, a self-supervised pre-training approach that enables a model to learn from multiple modalities simultaneously.
Specifically, we extend the CLIP (Contrastive Language-Image Pretraining) model to a trimodal setting, allowing the integration of time-series photometry data, spectra, and astrophysical metadata.
Results demonstrate that CLIP pre-training improves classification performance for time-series photometry, where accuracy increases from 84.6% to 91.5%.
arXiv Detail & Related papers (2024-11-13T18:20:29Z) - LLAVADI: What Matters For Multimodal Large Language Models Distillation [77.73964744238519]
In this work, we do not propose a new efficient model structure or train small-scale MLLMs from scratch.
Our studies involve training strategies, model choices, and distillation algorithms in the knowledge distillation process.
By evaluating different benchmarks and proper strategy, even a 2.7B small-scale model can perform on par with larger models with 7B or 13B parameters.
arXiv Detail & Related papers (2024-07-28T06:10:47Z) - Detecting and Classifying Flares in High-Resolution Solar Spectra with Supervised Machine Learning [0.0]
We present a standardized procedure to classify solar flares with the aid of supervised machine learning.
Using flare data from the RHESSI mission and solar spectra from the HARPS-N instrument, we trained several supervised machine learning models.
The best-trained model achieves an average aggregate accuracy score of 0.65, and categorical accuracy scores of over 0.70 for the no-flare and weak-flare classes.
arXiv Detail & Related papers (2024-06-21T18:52:03Z) - The Scaling Law in Stellar Light Curves [3.090476527764192]
We investigate the scaling law properties that emerge when learning from astronomical time series data using self-supervised techniques.
A self-supervised Transformer model achieves 3-10 times the sample efficiency compared to the state-of-the-art supervised learning model.
Our research lays the groundwork for analyzing stellar light curves by examining them through large-scale auto-regressive generative models.
arXiv Detail & Related papers (2024-05-27T13:31:03Z) - MiniCPM: Unveiling the Potential of Small Language Models with Scalable Training Strategies [85.57899012821211]
Small Language Models (SLMs) are a resource-efficient alternative to Large Language Models (LLMs)
We introduce MiniCPM, specifically the 1.2B and 2.4B non-embedding parameter variants.
We also introduce MiniCPM family, including MiniCPM-DPO, MiniCPM-MoE and MiniCPM-128K.
arXiv Detail & Related papers (2024-04-09T15:36:50Z) - Identifying Light-curve Signals with a Deep Learning Based Object
Detection Algorithm. II. A General Light Curve Classification Framework [0.0]
We present a novel deep learning framework for classifying light curves using a weakly supervised object detection model.
Our framework identifies the optimal windows for both light curves and power spectra automatically, and zooms in on their corresponding data.
We train our model on datasets obtained from both space-based and ground-based multi-band observations of variable stars and transients.
arXiv Detail & Related papers (2023-11-14T11:08:34Z) - The Languini Kitchen: Enabling Language Modelling Research at Different
Scales of Compute [66.84421705029624]
We introduce an experimental protocol that enables model comparisons based on equivalent compute, measured in accelerator hours.
We pre-process an existing large, diverse, and high-quality dataset of books that surpasses existing academic benchmarks in quality, diversity, and document length.
This work also provides two baseline models: a feed-forward model derived from the GPT-2 architecture and a recurrent model in the form of a novel LSTM with ten-fold throughput.
arXiv Detail & Related papers (2023-09-20T10:31:17Z) - From Quantity to Quality: Boosting LLM Performance with Self-Guided Data Selection for Instruction Tuning [52.257422715393574]
We introduce a self-guided methodology for Large Language Models (LLMs) to autonomously discern and select cherry samples from open-source datasets.
Our key innovation, the Instruction-Following Difficulty (IFD) metric, emerges as a pivotal metric to identify discrepancies between a model's expected responses and its intrinsic generation capability.
arXiv Detail & Related papers (2023-08-23T09:45:29Z) - To Repeat or Not To Repeat: Insights from Scaling LLM under Token-Crisis [50.31589712761807]
Large language models (LLMs) are notoriously token-hungry during pre-training, and high-quality text data on the web is approaching its scaling limit for LLMs.
We investigate the consequences of repeating pre-training data, revealing that the model is susceptible to overfitting.
Second, we examine the key factors contributing to multi-epoch degradation, finding that significant factors include dataset size, model parameters, and training objectives.
arXiv Detail & Related papers (2023-05-22T17:02:15Z) - 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)
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.