SPT: Spectral Transformer for Red Giant Stars Age and Mass Estimation

• URL: http://arxiv.org/abs/2401.04900v1
• Date: Wed, 10 Jan 2024 03:03:12 GMT
• Title: SPT: Spectral Transformer for Red Giant Stars Age and Mass Estimation
• Authors: Mengmeng Zhang, Fan Wu, Yude Bu, Shanshan Li, Zhenping Yi, Meng Liu, and Xiaoming Kong
• Abstract summary: We develop a novel framework, Spectral Transformer, to predict the age and mass of red giants aligned with asteroseismology from their spectra. Trained and tested on 3,880 red giant spectra from LAMOST, the SPT achieved remarkable age and mass estimations with average percentage errors of 17.64% and 6.61%, respectively.
• Score: 10.222849465628174
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The age and mass of red giants are essential for understanding the structure and evolution of the Milky Way. Traditional isochrone methods for these estimations are inherently limited due to overlapping isochrones in the Hertzsprung-Russell diagram, while asteroseismology, though more precise, requires high-precision, long-term observations. In response to these challenges, we developed a novel framework, Spectral Transformer (SPT), to predict the age and mass of red giants aligned with asteroseismology from their spectra. A key component of SPT, the Multi-head Hadamard Self-Attention mechanism, designed specifically for spectra, can capture complex relationships across different wavelength. Further, we introduced a Mahalanobis distance-based loss function to address scale imbalance and interaction mode loss, and incorporated Monte Carlo dropout for quantitative analysis of prediction uncertainty.Trained and tested on 3,880 red giant spectra from LAMOST, the SPT achieved remarkable age and mass estimations with average percentage errors of 17.64% and 6.61%, respectively, and provided uncertainties for each corresponding prediction. The results significantly outperform those of traditional machine learning algorithms and demonstrate a high level of consistency with asteroseismology methods and isochrone fitting techniques. In the future, our work will leverage datasets from the Chinese Space Station Telescope and the Large Synoptic Survey Telescope to enhance the precision of the model and broaden its applicability in the field of astronomy and astrophysics.

Related papers

• Anticipating Decoherence: a Predictive Framework for Enhancing Coherence in Quantum Emitters [96.41185946460115]
  We develop an anticipatory framework for forecasting and decoherence engineering in remote quantum emitters.<n>We show that a machine learning model trained on limited data can accurately forecast unseen spectral behavior.<n>These results pave the way for real-time decoherence engineering in scalable quantum systems.
  arXiv  Detail & Related papers  (2025-08-04T17:23:14Z)
• SpectrumFM: A New Paradigm for Spectrum Cognition [65.65474629224558]
  We propose a spectrum foundation model, termed SpectrumFM, which provides a new paradigm for spectrum cognition.<n>An innovative spectrum encoder that exploits the convolutional neural networks is proposed to effectively capture both fine-grained local signal structures and high-level global dependencies in the spectrum data.<n>Two novel self-supervised learning tasks, namely masked reconstruction and next-slot signal prediction, are developed for pre-training SpectrumFM, enabling the model to learn rich and transferable representations.
  arXiv  Detail & Related papers  (2025-08-02T14:40:50Z)
• SpecCLIP: Aligning and Translating Spectroscopic Measurements for Stars [1.4217538206528657]
  We present SpecCLIP, a foundation model framework that extends LLM-inspired methodologies to stellar spectral analysis.<n>By training foundation models on large-scale spectral datasets, our goal is to learn robust and informative embeddings that support diverse downstream applications.<n>We demonstrate that fine-tuning these models on moderate-sized labeled datasets improves adaptability to tasks such as stellar- parameter estimation and chemical-abundance determination.
  arXiv  Detail & Related papers  (2025-07-02T17:49:52Z)
• Latent-space Field Tension for Astrophysical Component Detection An application to X-ray imaging [0.0]
  We introduce a novel multi-frequency Bayesian model of the sky emission field that leverages latent-space tension as an indicator of model misspecification.<n>We demonstrate the effectiveness of this method on synthetic multi-frequency imaging data and apply it to observational X-ray data from the eROSITA Early Data Release (EDR) of the SN1987A region in the Large Magellanic Cloud (LMC)<n>Our results highlight the method's capability to reconstruct astrophysical components with high accuracy, achieving sub-pixel localization of point sources, robust separation of extended emission, and detailed uncertainty quantification.
  arXiv  Detail & Related papers  (2025-06-25T18:45:18Z)
• Variational diffusion transformers for conditional sampling of supernovae spectra [49.1574468325115]
  We introduce DiTSNe-Ia, a variational diffusion-based generative model conditioned on light curve observations and trained to reproduce the observed spectral diversity of SNe Ia.<n>In experiments with realistic light curves and spectra from radiative transfer simulations, DiTSNe-Ia achieves significantly more accurate reconstructions than the widely used SALT3 templates.
  arXiv  Detail & Related papers  (2025-05-05T22:52:32Z)
• CARL: Camera-Agnostic Representation Learning for Spectral Image Analysis [75.25966323298003]
  Spectral imaging offers promising applications across diverse domains, including medicine and urban scene understanding. variability in channel dimensionality and captured wavelengths among spectral cameras impede the development of AI-driven methodologies. We introduce $textbfCARL$, a model for $textbfC$amera-$textbfA$gnostic $textbfR$esupervised $textbfL$ across RGB, multispectral, and hyperspectral imaging modalities.
  arXiv  Detail & Related papers  (2025-04-27T13:06:40Z)
• Point-Calibrated Spectral Neural Operators [54.13671100638092]
  We introduce Point-Calibrated Spectral Transform, which learns operator mappings by approximating functions with the point-level adaptive spectral basis. Point-Calibrated Spectral Neural Operators learn operator mappings by approximating functions with the point-level adaptive spectral basis.
  arXiv  Detail & Related papers  (2024-10-15T08:19:39Z)
• Real-time gravitational-wave inference for binary neutron stars using machine learning [71.29593576787549]
  We present a machine learning framework that performs complete BNS inference in just one second without making any approximations. Our approach enhances multi-messenger observations by providing (i) accurate localization even before the merger; (ii) improved localization precision by $sim30%$ compared to approximate low-latency methods; and (iii) detailed information on luminosity distance, inclination, and masses.
  arXiv  Detail & Related papers  (2024-07-12T18:00:02Z)
• Flow-Based Generative Emulation of Grids of Stellar Evolutionary Models [4.713280433864737]
  We present a flow-based generative approach to emulate grids of stellar evolutionary models. We demonstrate their ability to emulate a variety of evolutionary tracks and isochrones across a continuous range of input parameters.
  arXiv  Detail & Related papers  (2024-07-12T16:54:17Z)
• Long-Term Prediction Accuracy Improvement of Data-Driven Medium-Range Global Weather Forecast [5.284452133959932]
  A universal neural operator called the Spherical Harmonic Neural Operator (SHNO) is introduced to improve long-term iterative forecasts. SHNO uses the spherical harmonic basis to mitigate distortions for spherical data and uses gated residual spectral attention (GRSA) to correct spectral bias caused by spurious correlations across different scales. Our findings highlight the benefits and potential of SHNO to improve the accuracy of long-term prediction.
  arXiv  Detail & Related papers  (2024-06-26T02:06:27Z)
• cecilia: A Machine Learning-Based Pipeline for Measuring Metal Abundances of Helium-rich Polluted White Dwarfs [0.0]
  Cecilia is the first Machine Learning-powered spectral modeling code designed to measure the metal abundances of intermediate-temperature white dwarfs. Cecilia combines state-of-the-art atmosphere models, powerful artificial intelligence tools, and robust statistical techniques. Cecilia's performance has the potential to unlock large-scale studies of extrasolar geochemistry.
  arXiv  Detail & Related papers  (2024-02-07T19:00:02Z)
• deep-REMAP: Parameterization of Stellar Spectra Using Regularized Multi-Task Learning [0.0]
  Deep-Regularized Ensemble-based Multi-task Learning with Asymmetric Loss for Probabilistic Inference ($rmdeep-REMAP$) We develop a novel framework that utilizes the rich synthetic spectra from the PHOENIX library and observational data from the MARVELS survey to accurately predict stellar atmospheric parameters.
  arXiv  Detail & Related papers  (2023-11-07T05:41:48Z)
• End-To-End Latent Variational Diffusion Models for Inverse Problems in High Energy Physics [61.44793171735013]
  We introduce a novel unified architecture, termed latent variation models, which combines the latent learning of cutting-edge generative art approaches with an end-to-end variational framework. Our unified approach achieves a distribution-free distance to the truth of over 20 times less than non-latent state-of-the-art baseline.
  arXiv  Detail & Related papers  (2023-05-17T17:43:10Z)
• Neural Importance Sampling for Rapid and Reliable Gravitational-Wave Inference [59.040209568168436]
  We first generate a rapid proposal for the Bayesian posterior using neural networks, and then attach importance weights based on the underlying likelihood and prior. This provides (1) a corrected posterior free from network inaccuracies, (2) a performance diagnostic (the sample efficiency) for assessing the proposal and identifying failure cases, and (3) an unbiased estimate of the Bayesian evidence. We carry out a large study analyzing 42 binary black hole mergers observed by LIGO and Virgo with the SEOBNRv4PHM and IMRPhenomHMXP waveform models.
  arXiv  Detail & Related papers  (2022-10-11T18:00:02Z)
• Deep Learning Models of the Discrete Component of the Galactic Interstellar Gamma-Ray Emission [61.26321023273399]
  A significant point-like component from the small scale (or discrete) structure in the H2 interstellar gas might be present in the Fermi-LAT data. We show that deep learning may be effectively employed to model the gamma-ray emission traced by these rare H2 proxies within statistical significance in data-rich regions.
  arXiv  Detail & Related papers  (2022-06-06T18:00:07Z)
• Unsupervised Spectral Unmixing For Telluric Correction Using A Neural Network Autoencoder [58.720142291102135]
  We present a neural network autoencoder approach for extracting a telluric transmission spectrum from a large set of high-precision observed solar spectra from the HARPS-N radial velocity spectrograph.
  arXiv  Detail & Related papers  (2021-11-17T12:54:48Z)
• Gaussian Processes with Skewed Laplace Spectral Mixture Kernels for Long-term Forecasting [11.729971911409637]
  Long-term forecasting involves predicting a horizon that is far ahead of the last observation. We propose to model spectral densities using a skewed Laplace spectral mixture (SLSM) due to the skewness of its peaks, sparsity, non-smoothness, and heavy tail characteristics. In addition, we adapt the lottery ticket method, originally developed to prune weights of a neural network, to GPs in order to automatically select the number of kernel components.
  arXiv  Detail & Related papers  (2020-11-08T13:03:59Z)

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.