Physicists Accidentally Turn Lead into Gold in Big Bang Simulation

Metro Loud
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Medieval alchemists long dreamed of transforming lead into gold, a feat once deemed impossible through chemistry alone. Modern physics reveals the key difference: lead atoms carry three more protons than gold atoms in their nuclei. Researchers now achieve this transmutation, albeit in minuscule quantities, while simulating conditions just after the Big Bang.

Simulating the Universe’s Birth at the LHC

Physicists at the Large Hadron Collider (LHC) in Switzerland accelerate lead nuclei to near-light speeds and smash them together using the ALICE experiment. This recreates the extreme energy densities of the early universe. In the process, they generate tiny amounts of gold—totaling just 29 trillionths of a gram.

The Mechanism: Proton Stripping via Electric Fields

Protons reside in atomic nuclei, bound by the strong nuclear force. Overcoming this requires an immense electric field, roughly a million times stronger than those in lightning. Colliding lead beams produce such fields during near-misses.

In head-on collisions, nuclei annihilate via the strong force. Near-misses, however, rely on electromagnetic interactions. The fleeting proximity amplifies the electric field dramatically, causing nuclei to vibrate and eject protons. Losing exactly three protons converts a lead nucleus (atomic number 82) into gold (atomic number 79).

Measuring the Transmutations

Special zero-degree calorimeters in the ALICE setup detect and count stripped protons, confirming indirect production of gold nuclei. Analysis shows approximately 89,000 gold nuclei form each second during collisions. Similar processes yield thallium (one proton removed) and mercury (two protons removed).

Challenges for Future Experiments

Transformed nuclei deviate from their stable beam paths and strike the LHC’s vacuum pipe walls within microseconds, weakening the beam intensity over time. This accidental alchemy poses a practical challenge rather than a boon, yet understanding it proves vital for interpreting data and planning larger-scale experiments.

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