Getting better

One of our main tools in our research are numerical simulations. E.g. the research of the previous entry would have been impossible without.

Numerical simulations require computers to run them. And even though computers become continuously more powerful, they are limited in the end. Not to mention that they cost money to buy and to use. Yes, also using them is expensive. Think of the electricity bill or even having space available for them.

So, to reduce the costs, we need to use them efficiently. That is good for us, because we can do more research in the same time. And that means that we as a society can make scientific progress faster. But it also reduces financial costs, which in fundamental research almost always means the taxpayer's money. And it reduces the environmental stress which we exercise by having and running the computers. That is also something which should not be forgotten.

So what does efficiently mean?

Well, we need to write our own computer programs. What we do nobody did before us. Most of what we do is really the edge of what we understand. So nobody was here before us and could have provided us with computer programs. We do them ourselves.

For that to be efficient, we need three important ingredients.

The first seems to be quite obvious. The programs should be correct before we use them to make a large scale computation. It would be very wasteful to run on a hundred computers for several months, just to figure out it was all for naught, because there was an error. Of course, we need to test them somewhere, but this can be done with much less effort. But this takes actually quite some time. And is very annoying. But it needs to be done.

The next two issues seems to be the same, but are actually subtly different. We need to have fast and optimized algorithms. The important difference is: The quality of the algorithm decides how fast it can be in principle. The actual optimization decides to which extent it uses this potential.

The latter point is something which requires a substantial amount of experience with programming. It is not something which can be learned theoretically. And it is more of a craftsmanship than anything else. Being good in optimization can make a program a thousand times faster. So, this is one reason why we try to teach students programming early, so that they can acquire the necessary experience before they enter research in their thesis work. Though there is still today research work which can be done without computers, it has become markedly less over the decades. It will never completely vanish, though. But it may well become a comparatively small fraction.

But whatever optimization can do, it can do only so much without good algorithms. And now we enter the main topic of this entry.

It is not only the code which we develop by ourselves. It is also the algorithms. Because again, they are new. Nobody did this before. So it is also up to us to make them efficient. But to really write a good algorithm requires knowledge about its background. This is called domain-specific knowledge. Knowing the scientific background. One reason more why you cannot get it off-the-shelf. Thus, if you want to calculate something new in research using computer simulations that means usually sitting down and writing a new algorithm.

But even once an algorithm is written down this does not mean that it is necessarily already the fastest possible one. Also this requires on the one hand experience, but even more so it is something new. And it is thus research as well to make it fast. So they can, and need to be, made better.

Right now I am supervising two bachelor theses where exactly this is done. The algorithms are indeed directly those which are involved with the research mentioned in the beginning. While both are working on the same algorithm, they do it with quite different emphasis.

The aim in one project is to make the algorithm faster, without changing its results. It is a classical case of improving an algorithm. If successful, it will make it possible to push the boundaries of what projects can be done. Thus, it makes computer simulations more efficient, and thus satisfies allows to do more research. One goal reached. Unfortunately the 'if' already tells that, as always with research, there is never a guarantee that it is possible. But if this kind of research should continue, it is necessary. The only alternative is waiting for a decade for the computers to become faster, and doing something different in the time in between. Not a very interesting option.

The other one is a little bit different. Here, the algorithm should be modified to serve a slightly different goal. It is not a fundamentally different goal, but subtly different so. Thus, while it does not create a fundamentally new algorithm, it still does create something new. Something, which will make a different kind of research possible. Without the modification, the other kind of research may not be possible for some time to come. But just as it is not possible to guarantee that an algorithm can be made more efficient, it is also not always possible that an algorithm with any reasonable amount of potential can be created at all. So this is also true research.

Thus, it remains exciting of what both theses will ultimately lead to.

So, as you see, behind the scenes research is quite full of the small things which make the big things possible. Both of these projects are probably closer to our everyday work than most of the things I have been posting before. The everyday work in research is quite often grinding. But, as always, this is what makes the big things ultimately possible. Without such projects as these two theses, our progress would be slowed down to a snail's speed.

Tackling ambiguities

I have recently published a paper with a rather lengthy and abstract title. I wanted to enlighten in this entry a little bit what is going on.

The paper is actually on a problem which occupies me by now since more than a decade. And this is the problem how to really define what we mean when we talk about gluons. The reason for this problem is a certain ambiguity. This ambiguity arises because it is often much more convenient to have auxiliary additional stuff around to make calculations simple. But then you have to deal with this additional stuff. In a paper last year I noted that the amount of stuff is much larger than originally anticipated. So you have to deal with more stuff.

The aim of the research leading to the paper was to make progress with that.

So what did I do? To understand this, it is first necessary to say a few words about how we describe gluons. We describe them by mathematical functions. The simplest such mathematical functions makes, loosely speaking, a statement about how probable it is that a gluon moves from one point to another. Since a fancy word for moving is propagating, this function is called a propagator.

So the first question I posed was whether the ambiguity in dealing with the stuff affects this. You may ask whether this should happen at all. Is a gluon not a particle? Should this not be free of ambiguities? Well, yes and no. A particle which we actually detect should be free of ambiguities. But gluons are not detected. Gluons are, in fact, never seen directly. They are confined. This is a very peculiar feature of the strong force. And one which is not satisfactorily fully understood. But it is experimentally well established.

Since therefore something happens to gluons before we can observe them, there is now a way out. If the gluon is ambiguous, then this ambiguity has to be canceled by whatever happens to it. Then whatever we detect is not ambiguous. But cancellations are fickle things. If you are not careful in your calculations, something is left uncanceled. And then your results become ambiguous. This has to be avoided. Of course, this is purely a problem for us theoreticians. The experimentalists never have this problem. A long time ago I actually already wrote together with a few other people a paper on this, showing how it may proceed.

So, the natural first step is to figure out what you have to cancel. And therefore to map the ambiguity in its full extent. The possibilities discussed since decades look roughly like this:

As you see, at short distances there is (essentially) no ambiguity. This is actually quite well understood. It is a feature very deeply embedded in the strong interaction. It has to do with the fact that, despite its name, the strong interaction makes itself less known the shorter the distance. But for weak effects we have very precise tools, and we therefore understand it.

On the other hand at long distances - well, there we knew for a long time not even qualitatively what is going on for sure. But, finally, over the decades, we were able to constrain the behavior at least partly. Now, I tested a large part of the remaining range of ambiguities. In the end, it indeed mattered little. There is almost no effect left of the ambiguity on the behavior of the gluon. So, it seems we have this under control.

Or do we? One of the important things in research is that it is never sufficient to confirm your result just by looking at a single thing. Either your explanation fits everything we see and measure, or it cannot be the full story. Or may even be wrong and the agreement with part of the observations is just a lucky coincidence. Well, actually not lucky. Rather terrible, since this misguides you.

Of course, doing all in one go is a horrendous amount of work, and so you work on a few at the time. Preferably, you first work on those where the most problems are expected. It is just ultimately that you need to have covered everything. But you cannot stop and claim victory before you did.

So I did, and looked in the paper at a handful of other quantities. And indeed, in some of them there remain effects. Especially, if you look at how strong the strong interaction is, depending on the distance where you measure it, something remains:

The effects of the ambiguity are thus not qualitative. So it does not change our qualitative understanding of how the strong force works. But there remains some quantitative effect, which we need to take into account.

There is one more important side effect. When I calculated the effects of the ambiguity, I learned also to control how the ambiguity manifests. This does not alter that there is an ambiguity, nor that it has consequences. But it allows others to reproduce how I controlled the ambiguity. This is important because now two results from different sources can be put together, and when using the same control they will fit such that for experimental observables the ambiguity cancels. And thus we have achieved the goal.

To be fair, however, this is currently at the level of an operative control. It is not yet a mathematically well-defined and proven procedure. As with so many cases, this still needs to be developed. But having operative control allows to develop the rigorous control easier than starting without it. So, progress has been made.

Using evolution for particle physics

(I will start to illustrate the entries with some simple sketches. I am not very experienced with it, and thus, they will be quite basic. But with making more of them I should gain experience, and they should become better eventually)

This entry will be on the recently started bachelor thesis of Raphael Wagner.

He is addressing the following problem. One of the mainstays of our research are computer simulations. But our computer simulations are not exact. They work by simulating a physical system many times with different starts. The final result is then an average over all the simulations. There is an (almost) infinite number of starts. Thus, we cannot include them all. As a consequence, our average is not the exact value we are looking for. Rather, it is an estimate. We can also estimate in which range around the real result should be.

This is sketched in the following picture

The black line is our estimate and the red lines give the range were the true value should be. From left to right some parameter runs. In the case of the thesis, the parameter is the time. The value is roughly the probability for a particle to survive this time. So we have an estimate for the survivability probability.

Fortunately, we know a little more. From quite basic principles we know that this survivability cannot depend in an arbitrary way on the time. Rather, it has a particular mathematical form. This function depends only on a very small set of numbers. The most important one is the mass of the particle.

What we then do is to start with some theory. We simulate it. And then we extract from such a survival probability the masses of the particles. Yes, we do not know them beforehand. This is because the masses of particles are changed in a quantum theory by quantum effects. These are which we simulate, to get a final value of the masses.

Up to now, we try to determine the mass in a very simple-minded way: We determined them by just looking for numbers for the mathematical functions which are closest to the data. That seems reasonable. Unfortunately, the function is not so simple. Thus, you can mathematically show that this does not give necessarily the best result. You can imagine this in the following way: Imagine you want to find the deepest valley in area. Surely, walking down hill will get you in a valley. But only walking down hill this will usually not be the deepest one:

But this is the way we determine the numbers so far. So there may be other options.

There is a different possibility. In the picture of the hills, you could rather deploy a number of ants, of which some prefer to walk up, some down, and some sometimes so and otherwise opposite. The ants live, die, and reproduce. Now, if you give the ants more to eat if they live in a deeper valley, at some time evolution will bring the population to live in the deepest valley:

And then you have what you want.

This is called a genetic algorithm. It is used in many areas of engineering. The processor of the computer or smartphone you use to read this has likely been optimized using such algorithms.

The bachelor thesis is now to apply the same idea to find better estimates for the masses of the particles in our simulations. This requires to understand what would be the equivalent to the deepness of the valley and the food for the ants. And how long we let evolution run its course. Then, we have only to monitor the (virtual) ants to find our prize.