Tag Archives: Feynman diagrams

What Gets Conserved at Vertices in Feynman Diagrams?

The simple answer is – everything! If there’s a symmetry in your theory then the associated Noether charge must be conserved at a Feynman vertex. A simple and elegant rule, and one of the great strengths of Feynman’s method.

Even better, it’s not hard to see why all charges are conserved at vertices. Remember, every vertex corresponds to an interaction term in the Lagrangian. These are automatically constructed to be Lorentz invariant so angular momentum and spin had better be conserved. Translation invariance is built in by virtue of the Lagrangian spacetime integral so momentum is conserved too.

Internal symmetries work in much the same way. Color or electric charge must be conserved at each vertex because the symmetry transformation exactly guarantees that contributions from interaction terms cancel transformations of the kinetic terms. If you ain’t convinced go and check this in any Feynman diagram!

But watch out, there’s a subtlety! Suppose we’re interested in scalar QED for instance. One diagram for pair creation and annihilation looks like

scalarqed

Naively you might be concerned that angular momentum and momentum can’t possibly be conserved. After all, don’t photons have spin and mass squared equal to zero? The resolution of this apparent paradox is provided by the realization that the virtual photon is off-shell. This is a theorist’s way of saying that it doesn’t obey equations of motion. Therefore the usual restrictions from symmetries do not apply to the virtual photon! Thinking another way, the photon is a manifestation of a quantum fluctuation.

Erratum: a previous version of this article erroneously claimed that Noether’s second theorem is related to Ward identities that guarantee gauge invariance at the quantum level. This is not the case, to our knowledge. Indeed, the Ward identity is a statement about averaging over field configurations, which necessarily depends on the behaviour of the path integral measure, a quantity that Noether never concerned herself with! Interestingly, there is a connection between the second theorem and large residual gauge symmetries, as pointed out in https://arxiv.org/abs/1510.07038.

Advertisements

Conference Amplitudes 2015 – Integration Ahoy!

I recall fondly a maths lesson from my teenage years. Dr. Mike Wade – responsible as much an anyone for my scientific passion – was introducing elementary concepts of differentiation and integration. Differentiation is easy, he proclaimed. But integration is a tricky beast.

That prescient warning perhaps foreshadowed my entry into the field of amplitudes. For indeed integration is of fundamental importance in determining the outcome of scattering events. To compute precise “loop corrections” necessarily requires integration. And this is typically a hard task.

Today we were presented with a smorsgasbord of integrals. Polylogarithms were the catch of the day. This broad class of functions covers pretty much everything you can get when computing amplitudes (provided your definition is generous)! So what are they? It fell to Dr. Erik Panzer to remind us.

Laymen will remember logarithms from school. These magic quantities turn multiplication into addition, giving rise to the ubiquitous schoolroom slide rules predating electronic calculators. Depending on your memory of math class, logarithms are either curious and fascinating or strange and terrifying! But boring they most certainly aren’t.

One of the most amusing properties of a logarithm comes about from (you guessed it) integration. Integrating x^{a-1} is easy, you might recall. You’ll end up with x^a/a plus some constant. But what happens when a is zero? Then the formula makes no sense, because dividing by zero simply isn’t allowed.

And here’s where the logarithm comes to the rescue. As if by witchcraft it turns out that

\displaystyle \int_0^x x^{-1} = -\log (1-x)

This kind of integral crops when you compute scattering amplitudes. The traditional way to work out an amplitudes is to draw Feynman diagrams – effectively pictures representing the answer. Every time you get a loop in the picture, you get an integration. Every time a particle propagates from A to B you get a fraction. Plug through the maths and you sometimes see integrals that give you logarithms!

But logarithms aren’t the end of the story. When you’ve got many loop integrations involved, and perhaps many propagators too, things can get messy. And this is where polylogarithms come in. They’ve got an integral form like logarithms, only instead of one integration there are many!

\displaystyle \textrm{Li}_{\sigma_1,\dots \sigma_n}(x) = \int_0^z \frac{1}{z_1- \sigma_1}\int_0^{z_1} \frac{1}{z_2-\sigma_2} \dots \int_0^{z_{n-1}}\frac{1}{z_n-\sigma_n}

It’s easy to check that out beloved \log function emerges from setting n=1 and \sigma_1=0. There’s some interesting sociology underlying polylogs. The polylogs I’ve defined are variously known as hyperlogs, generalized polylogs and Goncharov polylogs depending on who you ask. This confusion stems from the fact that these functions have been studied in several fields besides amplitudes, and predictably nobody can agree on a name! One name that is universally accepted is classical polylogs – these simpler functions emerging when you set all the \sigmas to zero.

So far we’ve just given names to some integrals we might find in amplitudes. But this is only the beginning. It turns out there are numerous interesting relations between different polylogs, which can be encoded by clever mathematical tools going by esoteric names – cluster algebras, motives and the symbol to name but a few. Erik warmed us up on some of these topics, while also mentioning that even generalized polylogs aren’t the whole story! Sometimes you need even wackier functions like elliptic polylogs.

All this gets rather technical quite quickly. In fact, complicated functions and swathes of algebra are a sad corollary of the traditional Feynman diagram approach to amplitudes. But thankfully there are new and powerful methods on the market. We heard about these so-called bootstraps from Dr. James Drummond and Dr. Matt von Hippel.

The term bootstrap is an old one, emerging in the 1960s to describe methods which use symmetry, locality and unitarity to determine amplitudes. It’s probably a humorous reference to the old English saying “pull yourself up by your bootstraps” to emphasise the achievement of lofty goals from meagre beginnings. Research efforts in the 60s had limited success, but the modern bootstrap programme is going from strength to strength. This is due in part to our much improved understanding of polylogarithms and their underlying mathematical structure.

The philosophy goes something like this. Assume that your answer can be written as a polylog (more precisely as a sum of polylogs, with the integrand expressed as \prod latex d \log(R_i) for appropriate rational functions R_i). Now write down all the possible rational functions that could appear, based on your knowledge of the process. Treat these as alphabet bricks. Now put your alphabet bricks together in every way that seems sensible.

The reason the method works is that there’s only one way to make a meaningful “word” out of your alphabet bricks. Locality forces the first letter to be a kinematic invariant, or else your answer would have branch cuts which don’t correspond to physical particles. Take it from me, that isn’t allowed! Supersymmetry cuts down the possibilities for the final letter. A cluster algebra ansatz also helps keep the possibilities down, though a physical interpretation for this is as yet unknown. For 7 particles this is more-or-less enough to get you the final answer. But weirdly 6 particles is smore complicated! Counter-intuitive, but hey – that’s research. To fix the six point result you must appeal to impressive all-loop results from integrability.

Next up for these bootstrap folk is higher loops. According to Matt, the 5-loop result should be gettable. But beyond that the sheer number of functions involved might mean the method crashes. Naively one might expect that the problem lies with having insufficiently many constraints. But apparently the real issue is more prosaic – we just don’t have the computing power to whittle down the options beyond 5-loop.

With the afternoon came a return to Feynman diagrams, but with a twist. Professor Johannes Henn talked us through an ingenious evaluation method based on differential equations. The basic concept has been known for a long time, but relies heavily on choosing the correct basis of integrals for the diagram under consideration. Johannes’ great insight was to use conjectures about the dlog form of integrands to suggest a particularly nice set of basis integrals. This makes solving the differential equations a cinch – a significant achievement!

Now the big question is – when can this new method be applied? As far as I’m aware there’s no proof that this nice integral basis always exists. But it seems that it’s there for enough cases to be useful! The day closed with some experimentally relevant applications, the acid test. I’m now curious as to whether you can link the developments in symbology and cluster algebras with this differential equation technique to provide a mega-powerful amplitude machine…! And that’s where I ought to head to bed, before you readers start to worry about theoretical physicists taking over the world.

Conversations

It was a pleasure to chat all things form factors with Brenda Penante, Mattias Wilhelm and Dhritiman Nandan at lunchtime. Look out for a “on-shell” blog post soon.

I must also thank Lorenzo Magnea for an enlightening discussion on soft theorems. Time to bury my head in some old papers I’d previously overlooked!

The Calculus of Particle Scattering

Quantum field theory allows us to calculate “amplitudes” for particle scattering processes. These are mathematical functions that encode the probability of particles scattering through various angles. Although the theory is quite complicated, miraculously the rules for calculating these amplitudes are pretty easy!

The key idea came from physicist Richard Feynman. To calculate a scattering amplitude, you draw a series of diagrams. The vertices and edges of the diagram come with particular factors relevant to the theory. In particular vertices usually carry coupling constants, external edges carry polarization vectors, and internal edges carry functions of momenta.

From the diagrams you can write down a mathematical expression for the scattering amplitude. All seems to be pretty simple. But what exactly are the diagrams you have to draw?

Well there are simple rules governing that too. Say you want to compute a scattering amplitude with 2 incoming and 2 outgoing particles. Then you draw four external lines, labelled with appropriate polarizations and momenta. Now you need to connect these lines up, so that they all become part of one diagram.

This involves adding internal lines, which connect to the external ones at vertices. The types and numbers of lines allowed to connect to a vertex is prescribed by the theory. For example in pure QCD the only particles are gluons. You are allowed to connect either three or four different lines to each vertex.

Here’s a few different diagrams you are allowed to draw – they each give different contributions to the overall scattering amplitude. Try to draw some more yourself if you’re feeling curious!

5 gluons at tree level

5 Gluons at 1 loop

Now it’s immediately obvious that there are infinitely many possible diagrams you could draw. Sounds like this is a problem, because adding up infinitely many things is hard! Thankfully, we can ignore a lot of the diagrams.

So why’s that? Well it transpires that each loop in the diagram contributes an extra factor of Planck’s constant \hbar. This is a very small number, so the effect on the amplitude from diagrams with many loops is negligable. There are situations in which this analysis breaks down, but we won’t consider them here.

So we can get a good approximation to a scattering amplitude by evaluating diagrams with only a small number of loops. The simplest have 0-loops, and are known as tree level diagrams because they look like trees. Here’s a QCD example from earlier

5 gluons at tree level

Next up you have 1-loop diagrams. These are also known as quantum corrections because they give the QFT correction to scattering processes from quantum mechanics, which were traditionally evaluated using classical fields. Here’s a nice QCD 1-loop diagram from earlier

5 Gluons at 1 loop

If you’ve been reading some of my recent posts, you’ll notice I’ve been talking about how to calculate tree level amplitudes. This is sensible because they give the most important contribution to the overall result. But the real reason for focussing on them is because the maths is quite easy.

Things get more complicated at 1-loop level because Feynman’s rules tell us to integrate over the momentum in the loop. This introduces another curve-ball for us to deal with. In particular our arguments for the simple tree level recursion relations now fail. It seems that all the nice tricks I’ve been learning are dead in the water when it comes to quantum corrections.

But thankfully, all is not lost! There’s a new set of tools that exploits the structure of 1-loop diagrams. Back in the 1950s Richard Cutkosky noticed that the 1-loop diagrams can be split into tree level ones under certain circumstances. This means we can build up information about loop processes from simpler tree results. The underlying principle which made this possible is called unitarity.

So what on earth is unitarity? To understand this we must return to the principles of quantum mechanics. In quantum theories we can’t say for definite what will happen. The best we can do is assign probabilities to different outcomes. Weird as this might sound, it’s how the universe seems to work at very small scales!

Probabilities measure the chances of different things happening. Obviously if you add up the chances of all possible outcomes you should get 1. Let’s take an example. Suppose you’re planning a night out, deciding whether to go out or stay in. Thinking back over the past few weeks you can estimate the probability of each outcome. Perhaps you stay in 8 times out of 10 and go out 2 times out of ten. 8/10 + 2/10 = 10/10 = 1 just as we’d expect for probability!

Now unitarity is just a mathsy way of saying that probabilities add up to 1. It probably sounds a bit stupid to make up a word for such a simple concept, but it’s a useful shorthand! It turns out that unitarity is exactly what we need to derive Cutkosky’s useful result. The method of splitting loop diagrams into tree level ones has become known as the unitarity method.

The nicest feature of this method is that it’s easy to picture in terms of Feynman diagrams. Let’s plunge straight in and see what that looks like.

Unitarity_Method_Example

At first glance it’s not at all clear what this picture means. But it’s easy to explain step by step. Firstly observe that it’s an equation, just in image form. On the left hand side you see a loop diagram, accompanied by the word \textrm{Disc}. This indicates a certain technical property of a loop diagram that it’s useful to calculate. On the right you see two tree diagrams multiplied together.

Mathematically these diagrams represent formulae for scattering amplitudes. So all this diagram says is that some property of 1-loop amplitudes is produced by multiplying together two tree level ones. This is extremely useful if you know about tree-level results but not about loops! Practically, people usually use this kind of equation to constrain the mathematical form of a 1-loop amplitude.

If you’re particularly sharp-eyed you might notice something about the diagrams on the left and right sides of the equation. The two diagrams on the right come from cutting through the loop on the left in two places. This cutting rule enables us to define the unitarity method for all loop diagrams. This gives us the full result that Cutkosky originally found. He’s perhaps the most aptly named scientist of all time!

We’re approaching the end of our whirlwind tour of particle scattering. We’ve seen how Feynman diagrams give simple rules but difficult maths. We’ve mentioned the tree level tricks that keep calculations easy. And now we’ve observed that unitarity comes to our rescue at loop-level. But most of these ideas are actually quite old. There’s just time for a brief glimpse of a hot contemporary technique.

In our pictorial representation of the unitarity method, we gained information by cutting the loop in two places. It’s natural to ask whether you could make further such cuts, giving more constraints on the form of the scattering amplitude. It turns out that the answer is yes, so long as you allow the momentum in the loop to be a complex number!

You’d be forgiven for thinking that this is all a bit unphysical, but in the previous post we saw that using the complex numbers is actually a very natural and powerful mathematical trick. The results we get in the end are still real, but the quickest route there is via the complex domain.

So why do the complex numbers afford us the extra freedom to cut more lines? Well, the act of cutting a line is mathematically equivalent to taking the corresponding momentum (l-K) to be on-shell; that is to say (l-K)^2 =0. We live in a four-dimensional world, so l has four components. That means we can solve a maximum of four equations (l-K)^2 =0 simultaneously. So generically we should be allow to cut four lines!

However, the equations (l-K)^2 =0 are quadratic. This means we are only guaranteed a solution if the momentum l can be complex. So to use a four line cut, we must allow the loop momentum to be complex. With our simple 2 line cuts there was enough freedom left to keep l real.

The procedure of using several loop cuts is known as the generalized unitarity method. It’s been around since the late 90s, but is still actively used to determine scattering amplitudes. Much of our current knowledge about QCD loop corrections is down to the power of generalized unitarity!

That’s all for now folks. I’ll be covering the mathematical detail in a series of posts over the next few days.

My thanks to Binosi et al. for their excellent program JaxoDraw which eased the drawing of Feynman diagrams.