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“Zhang Chaoyang discusses with Subir Sarkar: Dark matter, dark energy – reality or illusion?”

2026-08-27 08:00 General 🔥 26.9 heat score
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On August 27, 2026, Jiazi Guangnian reported that Zhang Chaoyang discussed dark matter and dark energy with Indian astrophysicist Subir Sarkar. This exchange focused on the debate between the truth and illusions surrounding these two core mysteries of cosmology, aiming to explore the current scientific understanding of their nature and the unresolved questions.

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Beijing University of Aeronautics and AstronauticsOxford UniversitySubir SakharZhang Chaoyangsohu

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Beijing University of A…1Beijing University of A…1Beijing University of A…1Beijing University of A…1Oxford University × Sub…1Oxford University × Zha…1

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  • Zhang Chaoyang1
  • Subir Sakhar1
  • sohu1
  • Oxford University1
  • Beijing University of Aeronautics and Astronautics1

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甲子光年 zh 2026-08-27 08:00

“Zhang Chaoyang discusses with Subir Sarkar: Dark matter, dark energy – reality or illusion?”

On August 18, Zhang Chaoyang, founder of Sohu, and Sir Subir Sarkar, a retired professor at Oxford University, discussed cosmological mysteries such as dark matter and dark energy in a live conversation. In response to the question of whether “dark energy can be used as a source of energy,” Sarkar pointed out that vacuum energy exists indeed, but it cannot be extracted due to the lack of potential energy difference; otherwise, it would violate thermodynamic laws. Zhang Chaoyang added that dark energy drives the accelerated expansion of the universe, but under the framework of general relativity, its total amount has no simple definition and cannot be considered a resource that can be extracted. The conversation revealed that dark matter accounts for approximately 27% of the total energy in the universe, dark energy about 68%, and ordinary matter only 5%. It was also mentioned that Sarkar’s team observed anisotropy in the rate of cosmic expansion, questioning the authenticity of dark energy in the standard model. Both parties also discussed the limitations of AI in physical research: AI is good at filtering patterns and matching data, but it struggles to propose “great” physical concepts that change how humans think, as the definition of physical problems often precedes theory.