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The standard model of the Big Bang rests on three observational pillars. The first is the cosmic microwave background radiation. The second is the cosmic expansion as measured by the light from distant supernovae. The third pillar is the set of primordial abundances of hydrogen, helium, and lithium. These elements were forged in the hot and dense early universe, during a time period of about 100 to 1000 seconds after the Big Bang. Therefore, the primordial abundance signatures were produced at a time much earlier than the observational signatures for the other two pillars.
No scientific theory other than the Big Bang can explain these observations. However, it turns out that there is a  crucial inconsistency for the third observational pillar. Although the standard model of the Big Bang very precisely predicts the primordial hydrogen and helium abundances, it cannot predict the abundance of the lithium isotope 7Li. In fact, the model overpredicts the measured priordial abundance by a factor of three. This long standing problem is referred to as the “Cosmological Lithium Problem.” Many research groups have investigated possible solutions, spanning from problems with poorly understood stellar processes (which would deplete the priordial lithium abundance at much later times), to exotic new physics beyond the standard model, to uncertain thermonuclear reaction rates that govern the synthesis and depletion of 7Li.
A research collaboration between UNC-CH and CNRS/Orsay, France, has applied a genetic algorithm (a numerical technique that belongs to Artificial Intelligence), showing that based on the most reliable nuclear physics cross sections available, the cosmological lithium problem cannot be found in the realm of nuclear physics. This narrows the solutions to either poorly understood stellar processes or exotic new physics beyond the standard model. This work has been accepted for publication in The Astrophysical Journal.
The x-axis shows the number of generations the genetic algorithm (GA) steps through. The y-axis displays the fitness of the population (here defined as the difference of simulated and predicted priordial abundance of H, D, 3He, 4He, and 7Li). Agreement is indicated by the dotted line with a fitness value of 0.33. The GA algorithm tries to find agreement by varying the thermonuclear rates of key nuclear reactions that happen shortly after the Big Bang. Genetic algorithms are very powerful for finding possible model solution. But no matter what combinations of reaction rates are explored, no nuclear physics solution can be found.
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