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Evaluation of the single-component thermal dust emission model in CMB experiments

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arxiv 2411.04543 v2 pith:FIUZWEI4 submitted 2024-11-07 astro-ph.CO

Evaluation of the single-component thermal dust emission model in CMB experiments

classification astro-ph.CO
keywords single-componentdustemissionestimationsmodelanswercomplicatedcomponents
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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It is well known that multiple Galactic thermal dust emission components may exist along the line of sight, but a single-component approximation is still widely used, since a full multi-component estimation requires a large number of frequency bands that are only available with future experiments. In light of this, we present a reliable, quantitative, and sensitive criterion to test the goodness of all kinds of dust emission estimations. This can not only give a definite answer to the quality of current single-component approximations; but also help determine preconditions of future multi-component estimations. Upon the former, previous works usually depend on a more complicated model to improve the single-component dust emission; however, our method is free from any additional model, and is sensitive enough to directly discover a substantial discrepancy between the Planck HFI data (100-857 GHz) and associated single-component dust emission estimations. This is the first time that the single-component estimation is ruled out by the data itself. For the latter, a similar procedure will be able to answer two important questions for estimating the complicated Galactic emissions: the number of necessary foreground components and their types.

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Cited by 2 Pith papers

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    astro-ph.GA 2026-07 conditional novelty 6.0

    LiteBIRD is forecast to measure Galactic polarized dust and synchrotron spectral parameters with errors as low as σ(T_d)≈0.2 K, σ(β_d)≈0.006, σ(β_s)≈0.04, and to detect E/B and T/P spectral discrepancies in the diffuse ISM.

  2. BROOM: a python package for model-independent analysis of microwave astronomical data

    astro-ph.CO 2026-04 unverdicted novelty 4.0

    BROOM is a Python package that applies ILC and GILC techniques for model-independent separation of CMB, SZ, and foreground signals in microwave data along with diagnostic and simulation utilities.