Intercomparison of Brown Dwarf Model Grids and Atmospheric Retrieval Using Machine Learning

• URL: http://arxiv.org/abs/2305.07719v2
• Date: Thu, 6 Jul 2023 06:28:43 GMT
• Title: Intercomparison of Brown Dwarf Model Grids and Atmospheric Retrieval Using Machine Learning
• Authors: Anna Lueber, Daniel Kitzmann, Chloe E. Fisher, Brendan P. Bowler, Adam J. Burgasser, Mark Marley, Kevin Heng
• Abstract summary: We study the information content of 14 previously published model grids of brown dwarfs. The random forest method allows us to analyze the predictive power of these model grids. We find that the effective temperature of a brown dwarf can be robustly predicted independent of the model grid chosen.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Understanding differences between sub-stellar spectral data and models has proven to be a major challenge, especially for self-consistent model grids that are necessary for a thorough investigation of brown dwarf atmospheres. Using the supervised machine learning method of the random forest, we study the information content of 14 previously published model grids of brown dwarfs (from 1997 to 2021). The random forest method allows us to analyze the predictive power of these model grids, as well as interpret data within the framework of Approximate Bayesian Computation (ABC). Our curated dataset includes 3 benchmark brown dwarfs (Gl 570D, {\epsilon} Indi Ba and Bb) as well as a sample of 19 L and T dwarfs; this sample was previously analyzed in Lueber et al. (2022) using traditional Bayesian methods (nested sampling). We find that the effective temperature of a brown dwarf can be robustly predicted independent of the model grid chosen for the interpretation. However, inference of the surface gravity is model-dependent. Specifically, the BT-Settl, Sonora Bobcat and Sonora Cholla model grids tend to predict logg ~3-4 (cgs units) even after data blueward of 1.2 {\mu}m have been disregarded to mitigate for our incomplete knowledge of the shapes of alkali lines. Two major, longstanding challenges associated with understanding the influence of clouds in brown dwarf atmospheres remain: our inability to model them from first principles and also to robustly validate these models.

Related papers

• Reconstructing Galaxy Cluster Mass Maps using Score-based Generative Modeling [9.386611764730791]
  We present a novel approach to reconstruct gas and dark matter projected density maps of galaxy clusters using score-based generative modeling. Our diffusion model takes in mock SZ and X-ray images as conditional observations, and generates realizations of corresponding gas and dark matter maps by sampling from a learned data posterior.
  arXiv  Detail & Related papers  (2024-10-03T18:00:03Z)
• 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)
• Diffusion posterior sampling for simulation-based inference in tall data settings [53.17563688225137]
  Simulation-based inference ( SBI) is capable of approximating the posterior distribution that relates input parameters to a given observation. In this work, we consider a tall data extension in which multiple observations are available to better infer the parameters of the model. We compare our method to recently proposed competing approaches on various numerical experiments and demonstrate its superiority in terms of numerical stability and computational cost.
  arXiv  Detail & Related papers  (2024-04-11T09:23:36Z)
• 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)
• Geometric Neural Diffusion Processes [55.891428654434634]
  We extend the framework of diffusion models to incorporate a series of geometric priors in infinite-dimension modelling. We show that with these conditions, the generative functional model admits the same symmetry.
  arXiv  Detail & Related papers  (2023-07-11T16:51:38Z)
• A machine learning and feature engineering approach for the prediction of the uncontrolled re-entry of space objects [1.0205541448656992]
  We present the development of a deep learning model for the re-entry prediction of uncontrolled objects in Low Earth Orbit (LEO) The model is based on a modified version of the Sequence-to-Sequence architecture and is trained on the average altitude profile as derived from a set of Two-Line Element (TLE) data of over 400 bodies. The novelty of the work consists in introducing in the deep learning model, alongside the average altitude, three new input features: a drag-like coefficient (B*), the average solar index, and the area-to-mass ratio of the object.
  arXiv  Detail & Related papers  (2023-03-17T13:53:59Z)
• 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)
• Uncertainty Quantification Techniques for Space Weather Modeling: Thermospheric Density Application [0.0]
  We propose two techniques to develop nonlinear ML models to predict thermospheric density. We show the performance for models trained on local and global datasets. We achieve errors of 11% on independent test data with well-calibrated uncertainty estimates.
  arXiv  Detail & Related papers  (2022-01-06T14:17:50Z)
• Inverting brain grey matter models with likelihood-free inference: a tool for trustable cytoarchitecture measurements [62.997667081978825]
  characterisation of the brain grey matter cytoarchitecture with quantitative sensitivity to soma density and volume remains an unsolved challenge in dMRI. We propose a new forward model, specifically a new system of equations, requiring a few relatively sparse b-shells. We then apply modern tools from Bayesian analysis known as likelihood-free inference (LFI) to invert our proposed model.
  arXiv  Detail & Related papers  (2021-11-15T09:08:27Z)
• The Generalized Lasso with Nonlinear Observations and Generative Priors [63.541900026673055]
  We make the assumption of sub-Gaussian measurements, which is satisfied by a wide range of measurement models. We show that our result can be extended to the uniform recovery guarantee under the assumption of a so-called local embedding property.
  arXiv  Detail & Related papers  (2020-06-22T16:43:35Z)

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.