Stellar Spectra Fitting with Amortized Neural Posterior Estimation and nbi

• URL: http://arxiv.org/abs/2312.05687v1
• Date: Sat, 9 Dec 2023 21:30:07 GMT
• Title: Stellar Spectra Fitting with Amortized Neural Posterior Estimation and nbi
• Authors: Keming Zhang, Tharindu Jayasinghe, Joshua S. Bloom
• Abstract summary: 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.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Modern surveys often deliver hundreds of thousands of stellar spectra at once, which are fit to spectral models to derive stellar parameters/labels. Therefore, the technique of Amortized Neural Posterior Estimation (ANPE) stands out as a suitable approach, which enables the inference of large number of targets as sub-linear/constant computational costs. Leveraging our new nbi software package, we train an ANPE model for the APOGEE survey and demonstrate its efficacy on both mock and real APOGEE stellar spectra. Unique to the nbi package is its out-of-the-box functionality on astronomical inverse problems with sequential data. As such, we have been able to acquire the trained model with minimal effort. We introduce an effective approach to handling the measurement noise properties inherent in spectral data, which utilizes the actual uncertainties in the observed data. This allows training data to resemble observed data, an aspect that is crucial for ANPE applications. Given the association of spectral data properties with the observing instrument, we discuss the utility of an ANPE "model zoo," where models are trained for specific instruments and distributed under the nbi framework to facilitate real-time stellar parameter inference.

Related papers

• Learning with Noisy Foundation Models [95.50968225050012]
  This paper is the first work to comprehensively understand and analyze the nature of noise in pre-training datasets. We propose a tuning method (NMTune) to affine the feature space to mitigate the malignant effect of noise and improve generalization.
  arXiv  Detail & Related papers  (2024-03-11T16:22:41Z)
• Inferring Atmospheric Properties of Exoplanets with Flow Matching and Neural Importance Sampling [10.847353970405285]
  Atmospheric retrievals characterize exoplanets by estimating atmospheric parameters from observed light spectra. Traditional approaches such as nested sampling are computationally expensive, sparking an interest in solutions based on machine learning (ML) We first explore flow matching posterior estimation (FMPE) as a new ML-based method for AR and find that, in our case, it is more accurate than neural posterior estimation (NPE) We then combine both FMPE and NPE with importance sampling, in which case both methods outperform nested sampling in terms of accuracy and simulation efficiency.
  arXiv  Detail & Related papers  (2023-12-13T17:12:03Z)
• nbi: the Astronomer's Package for Neural Posterior Estimation [0.0]
  We introduce a new framework and open-source software nbi (Neural Bayesian Inference), which supports both amortized and sequential NPE. First, nbi provides built-in "featurizer" networks with efficacy on sequential data, such as light curve and spectra, thus obviating the need for this customization on the user end. Second, we introduce a modified algorithm SNPE-IS, which facilities exact posterior inference by using the posterior under NPE only as a proposal distribution for importance sampling.
  arXiv  Detail & Related papers  (2023-12-06T19:00:00Z)
• 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)
• 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)
• Hodge-Aware Contrastive Learning [101.56637264703058]
  Simplicial complexes prove effective in modeling data with multiway dependencies. We develop a contrastive self-supervised learning approach for processing simplicial data.
  arXiv  Detail & Related papers  (2023-09-14T00:40:07Z)
• O-type Stars Stellar Parameter Estimation Using Recurrent Neural Networks [0.0]
  In previous work, we compare a set of machine learning and deep learning algorithms in order to establish a reliable way to fit a stellar model. Here we present the process to estimate individual physical parameters from an artificial neural network perspective. The development of three different recurrent neural network systems, the training process using stellar spectra models, the test over nine different observed stellar spectra, and the comparison with estimations in previous works are presented.
  arXiv  Detail & Related papers  (2022-10-23T17:18:52Z)
• An Outlier Exposure Approach to Improve Visual Anomaly Detection Performance for Mobile Robots [76.36017224414523]
  We consider the problem of building visual anomaly detection systems for mobile robots. Standard anomaly detection models are trained using large datasets composed only of non-anomalous data. We tackle the problem of exploiting these data to improve the performance of a Real-NVP anomaly detection model.
  arXiv  Detail & Related papers  (2022-09-20T15:18:13Z)
• Inertial Hallucinations -- When Wearable Inertial Devices Start Seeing Things [82.15959827765325]
  We propose a novel approach to multimodal sensor fusion for Ambient Assisted Living (AAL) We address two major shortcomings of standard multimodal approaches, limited area coverage and reduced reliability. Our new framework fuses the concept of modality hallucination with triplet learning to train a model with different modalities to handle missing sensors at inference time.
  arXiv  Detail & Related papers  (2022-07-14T10:04:18Z)
• 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)

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.