Title
Nonlinear corrections to Lagrangians predicted by causal set theory: Flat space bosonic toy model,Used
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One of the major weaknesses of causal set theory is lack of its predictive power. Due to the lack of large scale geometry, most causal set calculations are 10100 point simulations. This makes it very difficult to reproduce simplest known results, let along make new predictions. One can "avoid" the above difficulties by simply "assuming" that manifold structure has emerged "somehow", without specifying how. Using this approach Sorkin successfully predicted cosmological constant. Nevertheless, for the best of my knowledge, there were no other testable results besides that. In this work it is proposed to predict nonlinear correction to Lagrangian densities. In the previous work by the author, the manifoldbased Lagrangians were generalized to general causal set. This involves the constructions not present in ordinary manifold based QFT. If we now "plug" these constructions "back" into a manifold, we will predict nonlinear corrections to Lagrangians. This will result in potentially new predictions of causal set theory. These predictions would have to be (semi)classical since the corrections are nonlinear and therefore nonrenormalizable.
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We ship your order within 2–3 business days for USA deliveries and 5–8 business days for international shipments. Once your package has been dispatched from our warehouse, you'll receive an email confirmation with a tracking number, allowing you to track the status of your delivery.
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We provide a 2-year limited warranty, from the date of purchase for all our products.
If you believe you have received a defective product, or are experiencing any problems with your product, please contact us.
This warranty strictly does not cover damages that arose from negligence, misuse, wear and tear, or not in accordance with product instructions (dropping the product, etc.).
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Your payment information is processed securely. We do not store credit card details nor have access to your credit card information.
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