cecilia: A Machine Learning-Based Pipeline for Measuring Metal Abundances of Helium-rich Polluted White Dwarfs

• URL: http://arxiv.org/abs/2402.05176v1
• Date: Wed, 7 Feb 2024 19:00:02 GMT
• Title: cecilia: A Machine Learning-Based Pipeline for Measuring Metal Abundances of Helium-rich Polluted White Dwarfs
• Authors: M. Badenas-Agusti, J. Via\~na, A. Vanderburg, S. Blouin, P. Dufour, S. Xu, L. Sha
• Abstract summary: 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.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Over the past several decades, conventional spectral analysis techniques of polluted white dwarfs have become powerful tools to learn about the geology and chemistry of extrasolar bodies. Despite their proven capabilities and extensive legacy of scientific discoveries, these techniques are however still limited by their manual, time-intensive, and iterative nature. As a result, they are susceptible to human errors and are difficult to scale up to population-wide studies of metal pollution. This paper seeks to address this problem by presenting cecilia, the first Machine Learning (ML)-powered spectral modeling code designed to measure the metal abundances of intermediate-temperature (10,000$\leq T_{\rm eff} \leq$20,000 K), Helium-rich polluted white dwarfs. Trained with more than 22,000 randomly drawn atmosphere models and stellar parameters, our pipeline aims to overcome the limitations of classical methods by replacing the generation of synthetic spectra from computationally expensive codes and uniformly spaced model grids, with a fast, automated, and efficient neural-network-based interpolator. More specifically, cecilia combines state-of-the-art atmosphere models, powerful artificial intelligence tools, and robust statistical techniques to rapidly generate synthetic spectra of polluted white dwarfs in high-dimensional space, and enable accurate ($\lesssim$0.1 dex) and simultaneous measurements of 14 stellar parameters -- including 11 elemental abundances -- from real spectroscopic observations. As massively multiplexed astronomical surveys begin scientific operations, cecilia's performance has the potential to unlock large-scale studies of extrasolar geochemistry and propel the field of white dwarf science into the era of Big Data. In doing so, we aspire to uncover new statistical insights that were previously impractical with traditional white dwarf characterisation techniques.

Related papers

• Real-time gravitational-wave inference for binary neutron stars using machine learning [71.29593576787549]
  We develop a machine learning approach that performs complete BNS inference in just one second without making any approximations. Our method scales to extremely long signals, up to an hour in length, thus serving as a blueprint for data analysis for next-generation ground- and space-based detectors.
  arXiv  Detail & Related papers  (2024-07-12T18:00:02Z)
• Machine Learning for Exoplanet Detection in High-Contrast Spectroscopy: Revealing Exoplanets by Leveraging Hidden Molecular Signatures in Cross-Correlated Spectra with Convolutional Neural Networks [0.0]
  Cross-correlation for spectroscopy uses molecular templates to isolate a planet's spectrum from its host star. We introduce machine learning for cross-correlation spectroscopy (MLCCS) The method aims to leverage weak assumptions on exoplanet characterisation, such as the presence of specific molecules in atmospheres, to improve detection sensitivity for exoplanets.
  arXiv  Detail & Related papers  (2024-05-22T09:25:58Z)
• SPT: Spectral Transformer for Red Giant Stars Age and Mass Estimation [10.222849465628174]
  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.
  arXiv  Detail & Related papers  (2024-01-10T03:03:12Z)
• 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)
• Machine learning methods for the search for L&T brown dwarfs in the data of modern sky surveys [67.17190225886465]
  Brown dwarfs (BD) should account for up to 25 percent of all objects in the Galaxy. Due to their weakness, spectral studies of brown dwarfs are rather laborious. Numerous attempts have been made to search for and create a set of brown dwarfs using their colours as a decision rule.
  arXiv  Detail & Related papers  (2023-08-06T08:14:35Z)
• Applications of Gaussian Processes at Extreme Lengthscales: From Molecules to Black Holes [4.18804572788063]
  This thesis aims to use GP modelling to reason about the latent emission signature from the Seyfert galaxy Markarian 335. The second contribution is to extend the GP framework to molecular and chemical reaction representations and to provide an open-source software library to enable the framework to be used by scientists. The fourth contribution is to introduce a Bayesian optimisation scheme capable of modelling aleatoric uncertainty to facilitate the identification of material compositions that possess intrinsic robustness to large scale fabrication processes.
  arXiv  Detail & Related papers  (2023-03-24T22:20:14Z)
• Self-Supervised Learning for Modeling Gamma-ray Variability in Blazars [0.0]
  Blazars are active galactic nuclei with relativistic jets pointed almost directly at Earth. Deep learning can help uncover structure in gamma-ray blazars' complex variability patterns.
  arXiv  Detail & Related papers  (2023-02-15T14:57:46Z)
• ChemVise: Maximizing Out-of-Distribution Chemical Detection with the Novel Application of Zero-Shot Learning [60.02503434201552]
  This research proposes learning approximations of complex exposures from training sets of simple ones. We demonstrate this approach to synthetic sensor responses surprisingly improves the detection of out-of-distribution obscured chemical analytes.
  arXiv  Detail & Related papers  (2023-02-09T20:19:57Z)
• GFlowNets for AI-Driven Scientific Discovery [74.27219800878304]
  We present a new probabilistic machine learning framework called GFlowNets. GFlowNets can be applied in the modeling, hypotheses generation and experimental design stages of the experimental science loop. We argue that GFlowNets can become a valuable tool for AI-driven scientific discovery.
  arXiv  Detail & Related papers  (2023-02-01T17:29:43Z)
• Spatiotemporal modeling of European paleoclimate using doubly sparse Gaussian processes [61.31361524229248]
  We build on recent scale sparsetemporal GPs to reduce the computational burden. We successfully employ such a doubly sparse GP to construct a probabilistic model of paleoclimate.
  arXiv  Detail & Related papers  (2022-11-15T14:15:04Z)
• Probing chiral edge dynamics and bulk topology of a synthetic Hall system [52.77024349608834]
  Quantum Hall systems are characterized by the quantization of the Hall conductance -- a bulk property rooted in the topological structure of the underlying quantum states. Here, we realize a quantum Hall system using ultracold dysprosium atoms, in a two-dimensional geometry formed by one spatial dimension. We demonstrate that the large number of magnetic sublevels leads to distinct bulk and edge behaviors.
  arXiv  Detail & Related papers  (2020-01-06T16:59:08Z)

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.