#QuantumInformation #Panpsychism Explained and few can do so like #FedericoFaggin #AIhistory #QFT #consciousness #drones #funride

“Mathematics evolves out of consciousness. Therefore, mathematics cannot explain consciousness“

https://www.youtube.com/watch?v=0FUFewGHLLg

My article together with Kimia Shaban has appeared in JHEP today. We have examined the #statistics of #Feynmangraph s in #QFT , and how they can be exploited to efficiently compute #amplitudes at high loop order.
The article is open access, and the dataset is freely available from my website if you want to explore statistics and correlations yourself. Predicting the values of these Feynman integrals could also be interesting as a test case for #machinelearning
https://link.springer.com/article/10.1007/JHEP11(2024)038

Any physicists around who can help me understand something?

How should I picture neutrinos in a QFT context? If particles are ripples in their field, and we can detect only a teeny-tiny fraction of neutrinos, what does that say about their field? Is it somehow quiet, and only ripples when we make a chance detection of a neutrino? Is it constantly rippling, but so noisily we can hardly ever detect a signal in it?

Or have I got this all wrong?

(Boosts OK, of course.)

Our #workshop on #combinatorics in fundamental #physics has three topic days, November 26-28.

Day 1: Random Geometry for #Quantum #Gravity

Random geometry is a powerful mathematical framework for studying quantum gravity by modeling it as a statistical physics system, where each Boltzmann configuration corresponds to a spacetime geometry selected from an ensemble with a well-defined probability measure. When considering quantum gravity from a lattice perspective, where spacetime is discrete, the challenge of defining a suitable probability measure becomes a combinatorial problem.

Day 2: #Causal Set Theory

Causal Set Theory is an approach to Quantum Gravity in which spacetime is fundamentally discrete and takes the form of a locally finite partial order, or causal set. The twin questions leading much of the research in this field are: How does the continuum physics of General Relativity arise from an underlying discreteness? And what is the quantum nature of a discrete and dynamical spacetime? #causalset

Day 3: Combinatorics in Perturbative #QFT

A unique feature of quantum field theory is the central role that combinatorics plays: from generating Feynman graphs of scalar models to combinatorial maps and higher graph-like objects of sophisticated frameworks such as matrix-/tensor- and group field theories to renormalization Hopf algebras, and the theory of resurgence and asymptotic power series, to name but a few. The focus of this workshop will be on matrix-/tensor-/group-field theories and graph complexes.

Registration is open for everyone, and of course, free of charge.
https://indico.mitp.uni-mainz.de/event/422/registrations/245/

Question for the #QFT people here: What's your favourite way to explain the effective action Γ[Φ]?

The most frequent explanation I see is "quantum analogue of the classical action", but that doesn't really seem to cut it - after all, classical field theories work completely differently from quantum field theories. Most notably, in classical field theories, you don't have correlation functions - you just have a unique field that describes the current state of your system.

Two years ago, I began writing my #doctoralThesis in theoretical #physics. Most effort went into giving a very detailed pedagogical account of what the #renormalization #HopfAlgebra in #QuantumFieldTheory does, and why it is natural and transparent from a physical perspective.
One year ago, my referees recommended in their reports to publish the thesis as a book, and today I received the printed copies!
It was exciting to go through all the steps of actually publishing a book, and I hope that it will be of use to convince physicists that the Hopf algebra structure in #QFT is not a weird mathematical conundrum, but it actually encodes the very way physicists have been thinking of renormalization since the 1950s: Parametrize a theory by quantities one can actually measure, instead of fictional expansion parameters.
https://link.springer.com/book/10.1007/978-3-031-54446-0

I recently discovered an excellent #math article by Krajewski and Martinetti that I had overlooked so far: https://arxiv.org/abs/0806.4309
Basically, renormalization of #Feynmangraph s in #qft is organized in terms of rooted trees, and so are solutions to differential equations, concatenation of differential operators, numerical integration schemes, and the Hopf algebra of power series. It is intuitively clear that all these things must be closely related, but I wasn't aware that there is this article where the relations are actually spelled out in detail. They also include a derivation of Wigner's semicircle law for Gaussian random matrices in the rooted-tree formalism, which is something I didn't think about at all. Learned another unexpected connection .

New #physics preprint https://arxiv.org/abs/2403.16217 together with Kimia Shaban! Computing #Feynmangraphs in #qft is hard, but at least for periods at large loop order, their value can be estimated from properties of the underlying graph with a few percent accuracy. This allows us to identify which (out of millions) of graphs contribute most to physical predictions. We wrote a proof-of-concept Monte Carlo program which allows us to compute the primitive beta function of phi4-theory 1000-times faster than the current best approach (which would be using uniform random samples of graphs). This shows that although at high loop order the number of graphs is enormously large, they are "sufficiently regular" that one can get good estimates of physical observables with methods of #statistics instead of solving each and every Feynman integral.

here is an other nerdy one with a rather limited venn diagram intersection.
this meme is the logical continuation of:
https://det.social/@mastomememakers/110526310147517084