deep-REMAP: Parameterization of Stellar Spectra Using Regularized Multi-Task Learning

• URL: http://arxiv.org/abs/2311.03738v2
• Date: Tue, 21 Nov 2023 19:55:29 GMT
• Title: deep-REMAP: Parameterization of Stellar Spectra Using Regularized Multi-Task Learning
• Authors: Sankalp Gilda
• Abstract summary: 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.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Traditional spectral analysis methods are increasingly challenged by the exploding volumes of data produced by contemporary astronomical surveys. In response, we develop deep-Regularized Ensemble-based Multi-task Learning with Asymmetric Loss for Probabilistic Inference ($\rm{deep-REMAP}$), 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. By harnessing advanced machine learning techniques, including multi-task learning and an innovative asymmetric loss function, $\rm{deep-REMAP}$ demonstrates superior predictive capabilities in determining effective temperature, surface gravity, and metallicity from observed spectra. Our results reveal the framework's effectiveness in extending to other stellar libraries and properties, paving the way for more sophisticated and automated techniques in stellar characterization.

Related papers

• 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)
• Embedded feature selection in LSTM networks with multi-objective evolutionary ensemble learning for time series forecasting [49.1574468325115]
  We present a novel feature selection method embedded in Long Short-Term Memory networks. Our approach optimize the weights and biases of the LSTM in a partitioned manner. Experimental evaluations on air quality time series data from Italy and southeast Spain demonstrate that our method substantially improves the ability generalization of conventional LSTMs.
  arXiv  Detail & Related papers  (2023-12-29T08:42:10Z)
• Stellar Spectra Fitting with Amortized Neural Posterior Estimation and nbi [0.0]
  We train an ANPE model for the APOGEE survey and demonstrate its efficacy on both mock and real stellar spectra. We introduce an effective approach to handling the measurement noise properties inherent in spectral data. We discuss the utility of an ANPE "model zoo," where models are trained for specific instruments and distributed under the nbi framework.
  arXiv  Detail & Related papers  (2023-12-09T21:30:07Z)
• Searching for Novel Chemistry in Exoplanetary Atmospheres using Machine Learning for Anomaly Detection [1.8434042562191815]
  We advocate the application of machine learning (ML) techniques for anomaly (novelty) detection to exoplanet transit spectra. We demonstrate the feasibility of two popular anomaly detection methods on a large public database of synthetic spectra.
  arXiv  Detail & Related papers  (2023-08-15T07:19:54Z)
• Closing the loop: Autonomous experiments enabled by machine-learning-based online data analysis in synchrotron beamline environments [80.49514665620008]
  Machine learning can be used to enhance research involving large or rapidly generated datasets. In this study, we describe the incorporation of ML into a closed-loop workflow for X-ray reflectometry (XRR) We present solutions that provide an elementary data analysis in real time during the experiment without introducing the additional software dependencies in the beamline control software environment.
  arXiv  Detail & Related papers  (2023-06-20T21:21:19Z)
• Spectrum-BERT: Pre-training of Deep Bidirectional Transformers for Spectral Classification of Chinese Liquors [0.0]
  We propose a pre-training method of deep bidirectional transformers for spectral classification of Chinese liquors, abbreviated as Spectrum-BERT. We elaborately design two pre-training tasks, Next Curve Prediction (NCP) and Masked Curve Model (MCM), so that the model can effectively utilize unlabeled samples. In the comparative experiments, the proposed Spectrum-BERT significantly outperforms the baselines in multiple metrics.
  arXiv  Detail & Related papers  (2022-10-22T13:11:25Z)
• Spectral Decomposition Representation for Reinforcement Learning [100.0424588013549]
  We propose an alternative spectral method, Spectral Decomposition Representation (SPEDER), that extracts a state-action abstraction from the dynamics without inducing spurious dependence on the data collection policy. A theoretical analysis establishes the sample efficiency of the proposed algorithm in both the online and offline settings. An experimental investigation demonstrates superior performance over current state-of-the-art algorithms across several benchmarks.
  arXiv  Detail & Related papers  (2022-08-19T19:01:30Z)
• Explainable Predictive Modeling for Limited Spectral Data [0.0]
  We introduce applying recent explainable AI techniques to interpret the prediction outcomes of high-dimensional and limited spectral data. Due to instrument resolution limitations, pinpointing important regions of the spectroscopy data creates a pathway to optimize the data collection process. We specifically design three different scenarios to ensure that the evaluation of ML models is robust for the real-time practice.
  arXiv  Detail & Related papers  (2022-02-09T15:46:17Z)
• Unsupervised Machine Learning for Exploratory Data Analysis of Exoplanet Transmission Spectra [68.8204255655161]
  We focus on unsupervised techniques for analyzing spectral data from transiting exoplanets. We show that there is a high degree of correlation in the spectral data, which calls for appropriate low-dimensional representations. We uncover interesting structures in the principal component basis, namely, well-defined branches corresponding to different chemical regimes.
  arXiv  Detail & Related papers  (2022-01-07T22:26:33Z)
• Deep Autoregressive Models with Spectral Attention [74.08846528440024]
  We propose a forecasting architecture that combines deep autoregressive models with a Spectral Attention (SA) module. By characterizing in the spectral domain the embedding of the time series as occurrences of a random process, our method can identify global trends and seasonality patterns. Two spectral attention models, global and local to the time series, integrate this information within the forecast and perform spectral filtering to remove time series's noise.
  arXiv  Detail & Related papers  (2021-07-13T11:08:47Z)
• Spectral Methods for Data Science: A Statistical Perspective [37.2486912080998]
  Spectral methods have emerged as a simple yet surprisingly effective approach for extracting information from massive, noisy and incomplete data. This book aims to present a systematic, comprehensive, yet accessible introduction to spectral methods from a modern statistical perspective.
  arXiv  Detail & Related papers  (2020-12-15T18:40:56Z)

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.