Accurately constraining velocity information from spectral imaging observations using machine learning techniques

Determining accurate plasma Doppler (line-of-sight) velocities from spectroscopic measurements is a challenging endeavour, especially when weak chromospheric absorption lines are often rapidly evolving and, hence, contain multiple spectral components in their constituent line profiles. Here, we present a novel method that employs machine learning techniques to identify the underlying components present within observed spectral lines, before subsequently constraining the constituent profiles through single or multiple Voigt fits. Our method allows active and quiescent components present in spectra to be identified and isolated for subsequent study. Lastly, we employ a Ca II 8542 {\AA} spectral imaging dataset as a proof-of-concept study to benchmark the suitability of our code for extracting two-component atmospheric profiles that are commonly present in sunspot chromospheres. Minimisation tests are employed to validate the reliability of the results, achieving median reduced $\chi^2$ values equal to 1.03 between the observed and synthesised umbral line profiles.Comment: 23 pages, 8 figures. Improved formatting of abstract and reference

MCALF: Multi-Component Atmospheric Line Fitting

Determining accurate velocity measurements from observations of the Sun is of vital importance to solar physicists who are studying the wave dynamics in the solar atmosphere. Weak chromospheric absorption lines, due to dynamic events in the solar atmosphere, often consist of multiple spectral components. Isolating these components allows for the velocity field of the dynamic and quiescent regimes to be studied independently. However, isolating such components is particularly challenging due to the wide variety of spectral shapes present in the same dataset. MCALF provides a novel method and infrastructure to determine Doppler velocities in a large dataset. Each spectrum is fitted with a model adapted to its specific spectral shape

Ambipolar diffusion in the lower solar atmosphere: magnetohydrodynamic simulations of a sunspot

Magnetohydrodynamic (MHD) simulations of the solar atmosphere are often performed under the assumption that the plasma is fully ionized. However, in the lower solar atmosphere a reduced temperature often results in only the partial ionization of the plasma. The interaction between the decoupled neutral and ionized components of such a partially ionized plasma produces ambipolar diffusion. To investigate the role of ambipolar diffusion in propagating wave characteristics in the photosphere and chromosphere, we employ the Mancha3D numerical code to model magnetoacoustic waves propagating through the atmosphere immediately above the umbra of a sunspot. We solve the non-ideal MHD equations for data-driven perturbations to the magnetostatic equilibrium and the effect of ambipolar diffusion is investigated by varying the simulation to include additional terms in the MHD equations that account for this process. Analyzing the energy spectral densities for simulations with/without ambipolar diffusion, we find evidence to suggest that ambipolar diffusion plays a pivotal role in wave characteristics in the weakly ionized low density regions, hence maximizing the local ambipolar diffusion coefficient. As a result, we propose that ambipolar diffusion is an important mechanism that requires careful consideration into whether it should be included in simulations, and whether it should be utilized in the analysis and interpretation of particular observations of the lower solar atmosphere.Comment: 13 pages, 10 figures. Accepted for publication in The Astrophysical Journa

Accurately constraining velocity information from spectral imaging observations using machine learning techniques

Determining accurate plasma Doppler (line-of-sight) velocities from spectroscopic measurements is a challenging endeavour, especially when weak chromospheric absorption lines are often rapidly evolving and, hence, contain multiple spectral components in their constituent line profiles. Here, we present a novel method that employs machine learning techniques to identify the underlying components present within observed spectral lines, before subsequently constraining the constituent profiles through single or multiple Voigt fits. Our method allows active and quiescent components present in spectra to be identified and isolated for subsequent study. Lastly, we employ a Ca ɪɪ 8542 Å spectral imaging dataset as a proof-of-concept study to benchmark the suitability of our code for extracting two-component atmospheric profiles that are commonly present in sunspot chromospheres. Minimization tests are employed to validate the reliability of the results, achieving median reduced χ 2 -values equal to 1.03 between the observed and synthesized umbral line profiles. This article is part of the Theo Murphy meeting issue ‘High-resolution wave dynamics in the lower solar atmosphere’.</jats:p

astropy/pytest-arraydiff: v0.6.0 Release Notes

&lt;h2&gt;What's Changed&lt;/h2&gt; &lt;ul&gt; &lt;li&gt;add initial pandas HDF fileformat by @wkerzendorf in https://github.com/astropy/pytest-arraydiff/pull/23&lt;/li&gt; &lt;li&gt;update python version classifiers by @alexmalins in https://github.com/astropy/pytest-arraydiff/pull/32&lt;/li&gt; &lt;li&gt;Pass &lt;code&gt;atol&lt;/code&gt; parameter to FITSDiff by @svank in https://github.com/astropy/pytest-arraydiff/pull/33&lt;/li&gt; &lt;li&gt;Test inside &lt;code&gt;pytest_runtest_call&lt;/code&gt; hook by @ConorMacBride in https://github.com/astropy/pytest-arraydiff/pull/36&lt;/li&gt; &lt;li&gt;Updated continuous integration by @astrofrog in https://github.com/astropy/pytest-arraydiff/pull/38&lt;/li&gt; &lt;/ul&gt; &lt;h2&gt;New Contributors&lt;/h2&gt; &lt;ul&gt; &lt;li&gt;@wkerzendorf made their first contribution in https://github.com/astropy/pytest-arraydiff/pull/23&lt;/li&gt; &lt;li&gt;@alexmalins made their first contribution in https://github.com/astropy/pytest-arraydiff/pull/32&lt;/li&gt; &lt;li&gt;@svank made their first contribution in https://github.com/astropy/pytest-arraydiff/pull/33&lt;/li&gt; &lt;li&gt;@ConorMacBride made their first contribution in https://github.com/astropy/pytest-arraydiff/pull/36&lt;/li&gt; &lt;/ul&gt; &lt;p&gt;&lt;strong&gt;Full Changelog&lt;/strong&gt;: https://github.com/astropy/pytest-arraydiff/compare/v0.5.0...v0.6.0&lt;/p&gt

astropy/sphinx-automodapi: v0.17.0 Release Notes

&lt;p&gt;Also see &lt;code&gt;CHANGES.rst&lt;/code&gt;.&lt;/p&gt; &lt;h2&gt;What's Changed&lt;/h2&gt; &lt;ul&gt; &lt;li&gt;MNT: Drop Python 3.7 and update test matrix again by @pllim in https://github.com/astropy/sphinx-automodapi/pull/177&lt;/li&gt; &lt;li&gt;CI: fix environment name by @bsipocz in https://github.com/astropy/sphinx-automodapi/pull/178&lt;/li&gt; &lt;li&gt;CI: update versions by @bsipocz in https://github.com/astropy/sphinx-automodapi/pull/179&lt;/li&gt; &lt;li&gt;Updated &quot;Use &lt;strong&gt;dict&lt;/strong&gt; and ignore &lt;strong&gt;slots&lt;/strong&gt; on classes #169 by @kylefawcett in https://github.com/astropy/sphinx-automodapi/pull/181&lt;/li&gt; &lt;li&gt;Add automodsumm_included_members option, take2 by @bsipocz in https://github.com/astropy/sphinx-automodapi/pull/165&lt;/li&gt; &lt;li&gt;Bump codecov/codecov-action from 3 to 4 in /.github/workflows by @dependabot in https://github.com/astropy/sphinx-automodapi/pull/183&lt;/li&gt; &lt;li&gt;Fix nonascii object names by @m-rossi in https://github.com/astropy/sphinx-automodapi/pull/184&lt;/li&gt; &lt;/ul&gt; &lt;h2&gt;New Contributors&lt;/h2&gt; &lt;ul&gt; &lt;li&gt;@kylefawcett made their first contribution in https://github.com/astropy/sphinx-automodapi/pull/181&lt;/li&gt; &lt;li&gt;@dependabot made their first contribution in https://github.com/astropy/sphinx-automodapi/pull/183&lt;/li&gt; &lt;/ul&gt; &lt;p&gt;&lt;strong&gt;Full Changelog&lt;/strong&gt;: https://github.com/astropy/sphinx-automodapi/compare/v0.16.0...v0.17.0&lt;/p&gt

The Astropy Project: Sustaining and Growing a Community-oriented Open-source Project and the Latest Major Release (v5.0) of the Core Package

International audienceThe Astropy Project supports and fosters the development of open-source and openly developed Python packages that provide commonly needed functionality to the astronomical community. A key element of the Astropy Project is the core package astropy, which serves as the foundation for more specialized projects and packages. In this article, we summarize key features in the core package as of the recent major release, version 5.0, and provide major updates on the Project. We then discuss supporting a broader ecosystem of interoperable packages, including connections with several astronomical observatories and missions. We also revisit the future outlook of the Astropy Project and the current status of Learn Astropy. We conclude by raising and discussing the current and future challenges facing the Project

SunPy

The community-developed, free and open-source solar data analysis environment for Python