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Indian Aditya-L1 mission provides new data on Sun's coronal heating

BBC Science2 min read274 words
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The Sun’s corona, the outermost layer of its atmosphere, reaches temperatures of several million kelvin—far hotter than the visible surface, or photosphere, which sits at about 5,500 K. This counterintuitive temperature gradient, known as the coronal heating problem, has puzzled astronomers for decades. Recent observations from space‑borne instruments such as NASA’s Solar Dynamics Observatory and the European Space Agency’s Solar Orbiter have provided new data that support the idea that magnetic processes in the Sun’s turbulent plasma are responsible for heating the corona to such extreme temperatures.

The prevailing explanation involves the conversion of magnetic energy into thermal energy. As the Sun’s magnetic field lines twist and braid under the influence of convective motions beneath the surface, they store vast amounts of energy. When these tangled field lines reconnect—a process called magnetic reconnection—energy is released in the form of high‑frequency Alfvén waves and tiny, rapid energy bursts known as nanoflares. These waves propagate through the corona, dissipating their energy as heat and maintaining the million‑degree temperatures observed. Other mechanisms, such as the dissipation of magnetohydrodynamic waves and plasma turbulence, are also thought to contribute, but magnetic reconnection and wave heating remain the leading candidates in current models.

Scientists continue to refine their understanding of coronal heating through coordinated observations and advanced numerical simulations. By comparing high‑resolution imaging, spectroscopic data, and in situ measurements from spacecraft that venture close to the Sun, researchers aim to quantify the relative contributions of each heating mechanism. As these studies progress, the long‑standing mystery of why the Sun’s outer atmosphere is hotter than its surface is expected to be resolved, offering deeper insights into stellar physics and space‑weather forecasting.

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