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Archive for February, 2010

Roaming Washington

Thursday, February 18th, 2010

The last days the annual meeting of the American Physical Society took place in Washington, D.C.

Although commonly perceived as sort of student conference due to the multitude of student talks given in parallel sessions, I think the APS offers an excellent variety of review talks and one can learn a great deal about neighboring fields. In addition there are sort of historical and prize sessions of great interest. Most notably this year maybe (at least for me as Higgs searcher), the Sakurai Prize. It is one of the most renowned prized in physics and was rewarded this year to Robert Brout, Francois Englert, Gerald Guralnik, C. R. Hagen, Peter Higgs, and Tom Kibble. E.g. the fathers of the spontaneous symmetry breaking in gauge theories and the masses of vector boson. Or to make it short, the Higgs Mechanism. Of course considering that there are six laureates I hesitate a bit using only the term ‘Higgs’ here. I think this was the case as well for other participants. E.g. Rob Rosser was referring only to  the ‘Brout-Englert-Higgs-Hagen-Guralnik-Kibble’ (BEHHGK) mechanism in this plenary talk some days later.

Anyway, it is important to note that this would have been the first time that all six of them (nicknamed ‘Gang of Six’) are under one roof. Unfortunately Peter Higgs couldn’t make it due to health concerns.  Even more surprising, Engler and (I think) Hagen mentioned that they never met Peter Higgs before! Wow, almost 50 years gone by since their famous papers and they’ve never met. Would haven been great to seem them altogether. But five of them was great as well.

Gang of Five
Gang of Five

The session itself was very interesting. Each of them gave a very nice review of their important papers back in ~1964, sprinkled with anecdotes setting things in the historical and scientific context. Very educating, very interesting.

Of course there were many more interesting talk, covering topics from the invention of the  laser via low energy experiment up to modern cosmology and dark energy searches. No to mention the general location of the APS in Washington, D.C. The capital of the US hosts some very impressive museums. Of course I had to pay a visit to the Smithsonian National Air and Space Museum. Wow! I think you don’t have to be a rocket scientist (or physicist) to be impressed. Right when entering one sees the original capsule of Friendship 7 and Apollo 11, hovering above them the X-15, Space Ship One, a Pioneer space probe and many many more unique exhibits.

Entrance of the Smithsonian National Air and Space Museum
Entrance of the Smithsonian National Air and Space Museum

The adjacent rooms host countless exhibits of man’s explorations. Just to mention a few, the original plane of the Brothers Wright, the space suits of Yuri Gagarin and Alan Shepard and the ones from the Apollo 11 crew, still dusty from the moon dust. There are countless more exhibits, originals and true to the original replicas of the space missions, probes, spacecrafts, moon lander and vehicle, civil, research and military air planes etc.

P1030160
Apollo and Soyuz spacecraft docking
P1030170
Apollo 11 Lunar Suit
P1030177
Moon Lander

Really great. A tribute to the explatory spirit. Makes one consider, that since the moon landing and now almost as much time has passed as between the first flight of the Brother Wright and the moon landing. Somehow not really much has happened, unless of course someone considers the iPod or GSM phones or similar gadgets as a big leap in mankind’s development.

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Combating the Sedentary Lifestyle?

Wednesday, February 17th, 2010

Recently, a study led by Prof. David Dunstan has shown that sitting for long hours is detrimental to our health, even if we otherwise exercise regularly. If you think about it, it’s not even that surprising, though it’s unpleasant news for those of us who work out regularly before or after work. Despite our efforts, we’re still in the danger zone!

And let’s face it, the lifestyle of a theoretical physicist (and I am guessing also of most experimental particle physicists) is definitely sedentary. Several websites went on to collect tips on how to bring a bit of movement into our daily office lives (e.g. some older tips here, and for more extreme tastes, here). Examples are pacing when you’re on the phone, taking the stairs instead of the elevator, or passing by the office of a colleague instead of writing an e-mail. I even once read the advice of drinking lots of water (which makes for more frequent trips to the bathroom).
Now I hardly ever make phone calls or need to see people in other offices, so what would work for me? I guess the single best opportunity for a theorist not to be sitting down all the time is to discuss with collaborators on the blackboard instead of at the desk on a sheet of paper. You’re standing up at least, and you have to move your arms to write.
What sometimes works for me is printing out my reading materials and reading them standing up instead of reading them on the screen, sitting down (I have the added benefit of having the printer rather far away. On the other hand, I end up killing more trees like that). Apart from this, it’s a though one. Computer work is computer work. I try to make an effort to stand up at least once every hour and stretch my arms and shoulders a little. But that’s probably hardly enough.

How do other physicists get moving during their working hours? Are you worried about your sedentary lifestyle at all, and if yes, how do you fight it?

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APS “April” meeting or Bust!

Monday, February 15th, 2010

This weekend/week is the APS “April” meeting in Washington, DC. Every year the APS has a series of meetings for each of the physics disciplines. The April meeting is reserved for Nuclear/Particle/Astrophysics. This year I submitted an abstract and gave a talk about the work I did on the uniformity study of the calorimeter (See my previous post). You can check out my abstract here.

You can actually check out all the abstracts, but I’m just giving you a link for mine :). At the meeting we get the opportunity to meet other physicists to see what other experiments are working on. There’s lots of interesting science and a great opportunity to network. I joined lots of my colleagues from the Phenix collaboration. But… April meeting in February, you ask? This year it was a joint meeting with the American Association of Physics Teachers. It was a great opportunity for physicists and physics teachers to get together and discuss how to get more students interested in physics. These talks are particularly interesting because there’s always an emphasis on getting more girls into physics – something I’m obviously very interested in.

Also as I learned this week, it’s actually possible for anyone to give a talk at the APS meeting (provided they are a member – which means they pay membership feel), so every year there are people from the non-scientific community who come and present their theories. Most of these talks end up in the same session or two, so they’re easy and kind of fun to go to. Most of these are crazy and logically inconsistent or just plain wrong. If they are particularly crazy they fall into the realm of “crackpot”. I was shown a list of criteria for determining who is the biggest crackpot. See the them listed.

See if you can read the abstracts and find the ones that fall in this realm.

-Regina

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Let’s draw Feynman diagrams!

Sunday, February 14th, 2010

Greetings! This post turned into a multi-part ongoing series about the Feynman rules for the Standard Model and a few of its extensions. I’ll use this first post as an index for all of the parts of the series.

  1. Let’s draw Feynman diagrams! (this post)
  2. More Feynman diagrams.
  3. Introducing the muon.
  4. The Z boson and resonances.
  5. Neutrinos.
  6. The W boson, mixing things up.
  7. Meet the quarks.
  8. World of glue.
  9. QCD and confinement.
  10. Known knowns of the Standard Model. (summary)
  11. When Feynman Diagrams Fail.
  12. An idiosyncratic introduction to the Higgs.
  13. A diagrammatic hint of masses from the Higgs
  14. Higgs and the vacuum: Viva la “vev”
  15. Helicity, Chirality, Mass, and the Higgs
  16. The Birds and the Bs
  17. The spin of gauge bosons
  18. Who ate the Higgs?
  19. Unitarization of vector boson scattering
  20. Private lives of Standard Model particles (summary)

There are few things more iconic of particle physics than Feynman diagrams. These little figures of squiggly show up prominently on particle physicists’ chalkboards alongside scribbled equations. Here’s a ‘typical’ example from a previous post.

The simplicity of these diagrams has a certain aesthetic appeal, though as one might imagine there are many layers of meaning behind them. The good news is that’s it’s really easy to understand the first few layers and today you will learn how to draw your own Feynman diagrams and interpret their physical meaning.

You do not need to know any fancy-schmancy math or physics to do this!

That’s right. I know a lot of people are intimidated by physics: don’t be! Today there will be no equations, just non-threatening squiggly lines. Even school children can learn how to draw Feynman diagrams (and, I hope, some cool science). Particle physics: fun for the whole family. 🙂

For now, think of this as a game. You’ll need a piece of paper and a pen/pencil. The rules are as follows (read these carefully):

  1. You can draw two kinds of lines, a straight line with an arrow or a wiggly line:

    You can draw these pointing in any direction.
  2. You may only connect these lines if you have two lines with arrows meeting a single wiggly line.

    Note that the orientation of the arrows is important! You must have exactly one arrow going into the vertex and exactly one arrow coming out.
  3. Your diagram should only contain connected pieces. That is every line must connect to at least one vertex. There shouldn’t be any disconnected part of the diagram.

    In the image above the diagram on the left is allowed while the one on the right is not since the top and bottom parts don’t connect.
  4. What’s really important are the endpoints of each line, so we can get rid of excess curves. You should treat each line as a shoelace and pull each line taut to make them nice and neat. They should be as straight as possible. (But the wiggly line stays wiggly!)

That’s it! Those are the rules of the game. Any diagram you can draw that passes these rules is a valid Feynman diagram. We will call this game QED. Take some time now to draw a few diagrams. Beware of a few common pitfalls of diagrams that do not work (can you see why?):

After a while, you might notice a few patterns emerging. For example, you could count the number of external lines (one free end) versus the number of internal lines (both ends attached to a vertex).

  • How are the number of external lines related to the number of internal lines and vertices?
  • If I tell you the number of external lines with arrows point inward, can you tell me the number of external lines with arrows pointing outward? Does a similar relation hole for the number of external wiggly lines?
  • If you keep following the arrowed lines, is it possible to end on some internal vertex?
  • Did you consider diagrams that contain closed loops? If not, do your answers to the above two questions change?

I won’t answer these questions for you, at least not in this post. Take some time to really play with these diagrams. There’s a lot of intuition you can develop with this “QED” game. After a while, you’ll have a pleasantly silly-looking piece of paper and you’ll be ready to move on to the next discussion:

What does it all mean?

(more…)

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Make-up of a CERN Collaboration

Thursday, February 11th, 2010
Screen shot 2010-02-11 at 3.10.40 PM

Grad students working for the CMS detector.

The experiments at CERN are, in total, a collaboration of several thousands of physicists, scientists, engineers, and students. Here I show the make-up of just graduate students from just one of the experiments at CERN, the CMS detector.

People come from all over the world to contribute to these projects. It’s fantastic that so many countries and cultures are represented, and work with each other on common goals such as: recreating the big bang in the lab, studying these mini big-bangs to understand out the laws of nature that govern our universe, and then sharing these discoveries with people all over the world.

These are lofty goals, but you can be sure that whatever discoveries are made, with all the languages spoken at CERN, the knowledge will spread far.

Mike

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Intuition

Wednesday, February 10th, 2010

The Oxford Dictionary defines intuition as “the apparent ability to acquire knowledge without inference or the use of reason.” We all have these hunches about how certain things will behave, which, mostly unconsciously, are based on our earlier experience with this type of thing. If you see for example a car approaching on the street, you can easily tell whether or not you’ll have time to cross the street before or not, because this situation keeps repeating itself. Our intuition is formed by our experience.

This is the problem with forming an intuition for physical theories that are far removed from our daily life experience. Our judgement is good for Newtonian physics. We rarely experience directly an object of relativistic speeds (i.e. near the speed of light), let alone move at relativistic speeds ourselves. And when have you last come close to an object heavy enough to bring general relativity into play, say for example a black hole like it can be found at the centers of many galaxies?
This is why the predictions of General Relativity seem incredible and strange at first, they just have nothing in common with the view of the world we have formed based on our daily experience. An effect like time dilation never happens to us (while on the other hand, every child is familiar with the Doppler effect).
The same is of course true for Quantum Mechanics. We simply cannot experience directly the behavior of single elementary particles.

But the case is not hopeless. Luckily, the human brain is so powerful that we need not rely solely on our direct sensory experience to form an intuition. It is enough to think about a certain abstract concept and its implications for extended periods of time to form an expectation for how similar concepts will behave. Experiences can take place exclusively in your head, they will be an equally good guide as the intuitions that were formed on the basis of real life experiences. Already after one semester of studying General Relativity, its implications seem much less mind-boggling than when we heard about them the first time. But this is merely the beginning. If you have spent a lot of time doing a particular kind of calculation, you will know that a certain result is wrong just by looking at it, and before re-checking everything step by step.

Sometimes it is said about a famous scientist that he or she has this great “physical intuition”. Guess what: they were not born with that. They simply spent a very large amount of time thinking about their physical models, toying with example calculations and tinkering around with them, wherever they were, including scribbling on napkins while waiting at the restaurant. They are able to see connections others could not see because they have seen so many similar things in their life as scientists. This acquired knowledge needn’t manifest itself as a rational thought process. It can really feel the same as just “having a hunch”, it’s having a vague, fuzzy feeling about how a certain thing should work or how some object should behave, even if this object is an abstract concept.
I have already seen this kind of intuition in action in more senior people I have worked with. And sometimes it even happens to myself that I find myself telling someone that a certain thing should work like this because I somehow just know that it must behave like that.

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Around a week ago, I submitted the first paper to have me as the sole author. For someone working in such a large collaboration this is a pretty exciting moment, even if it is just proceedings 🙂

Last September, I was given the incredible opportunity to attend one of the most prestigious conferences in the world of quark-related research. The Strangeness in Quark Matter conference, held every few years, gathers physicists from around the world to an exotic location to discuss our current understanding of the strange quark, and the unusual behavior of the particles it creates. In September last year it was held in Buzios, a tiny fishing village on the coast north of Rio de Janeiro.  I was invited to give a talk at the conference, and I was lucky enough to get funding for the trip as I was also giving a talk on diffraction the week before in Rio (See Strong couplings: Tales from Brazil).

photoSQM7

This was truly the most beautiful place I have ever seen (even compared to the stunning French snowy mountains I was falling down just a few weeks ago). It was also one of the strangest experiences of my life, and I am not attempting a pun. International conferences are a world unto themselves – indulgent in every sense. You feast frequently on a variety of delicious foods. You mingle with minds that are expertly extreme, taking various representations and interpretations of experimental analysis, sampling ideas and concepts from theorists from around the globe and across the field. Having never been to South America (or anywhere near as far as that) before in my life, the setting, for me, was entrancing and alien. Everywhere you looked there was a mango tree or a parasitic orchid hanging from a trunk. Our buffets and breakfasts were adorned with Papaya and Guava. We were even treated to an exciting boat trip to a nearby island (nicknamed “ugly island”), and got to dive into the salty waters and snorkel!

photoSQM3

photoSQM6

Outside scheduled talk time we were constantly supplied with Caipirinhas – cocktails with ice, sugar, lime and Cachaca (a spirit made from sugar-cane). In fact, after one long day, during a lively and late discussion that united the attendees with outstanding questions, drinks were brought round to encourage us to stay!

photoSQM5

The topics under discussion, (and to some extent, debate), were just as unusual. At the start of my PhD, I had only known my own limitations in understanding data, theoretical concepts or predictions. Before the conference, discussion with many theorists to help me to understand the expectations for the LHC only served to confuse and excite me more. However, as well as answering a lot of questions for me, this conference demonstrated the true nature of being at the very front end of science – right now, we know very little for certain. Ask any scientist about what the LHC and RHIC heavy ion experiments are all about, and they will very quickly start to tell you about exciting things such as the “Quark Gluon Plasma”, and evidence to suggest its properties, like “strangeness enhancement”. Try saying either one of these phrases too loudly at a conference like this, however, and expect some funny looks. The fact is, there isn’t much you can say without a little skepticism (or careful rewording) right now.

photoSQM4

One thing I know for sure is that my analysis area is not lacking in interest. Strange particle production in heavy ion collisions at RHIC, compared to pp collisions, can be explained quite powerfully by theory, but the phi resonance, which is not technically strange (made up of an s and anti-s quark) is somewhat more confusing. Asking what might happen to phi production in Pb-Pb collisions at the LHC is a tough enough question. However, begin to postulate what might occur in pp collisions with such high energy density that they become (in some ways) comparable to heavy ions, and you start to get some of those funny looks I mentioned. This was exactly what I did, and it sparked an argument between theorists of two extreme viewpoints, who eventually were asked to leave the room whilst the poor speaker continued. Of course, myself and another (very brilliant) ALICE physicist, Federico Antinori, who was keen to understand this issue, followed them out to take notes. 🙂

photoSQM

The conference was full of moments like this, and I am sure many of them are. Unusual data presented by experimentalists struggling to interpret it, theorists arguing passionately about the consequences. I’d like to make a rather controversial statement that there is probably an equivalent to the “Phlogiston” phenomenon at work in much of front-line science. (If you don’t know what I am talking about, don’t just Wikipedia it, you should also watch “Chemistry: A Volatile History”, presented by Prof. Jim Al-Khalili on BBC4 Catch up TV, and hurry as you only have a few days left!) What I mean is, wherever we are dealing with the unknown, there are many contradicting ideas and some of them have to be nonsense. Unfortunately what seems like nonsense can be exactly what we are looking for. You only have to look at the history and evolution of science to see how these red herrings can take a long time to unveil, and how what looks like a ridiculous mistake (parity violation, for example!) could turn out to be a curiously perfect answer.

photoSQM2

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Si je vous dis réseau de neurones, vous pensez certainement au cerveau, ou même si vous avez suivi des cours de biologie vous pensez aux synapses, dendrites etc… Mais ce n’est pas là où je veux vous amener. Pour le moment.
Vous êtes vous déjà demandé comment était lu le code postal sur les enveloppes, ou encore comment le filtre anti-spam de votre messagerie préférée faisait pour stopper les mails indésirables ? Tout ceci demande une capacité à effectuer une décision reliée à un processus statistique. En effet, 2 personnes n’écriront jamais le même chiffre de la même manière et deux spams ne contiendront pas exactement les mêmes mots. Nous nous retrouvons face un ensemble d’éléments potentiellement infini tous différents les uns des autres et qui pourtant peuvent se regrouper en un nombre restreint de groupes de même caractéristique (ce caractère est un 3 ou encore ce mail est un spam…).

C’est dans cet objectif de tri que sont utilisés ce qu’on appelle des algorithmes d’apprentissage, dont font partie les réseaux de neurones artificiels. Ceux-ci vont être capable d’apprendre à identifier une certaine caractéristique dans un échantillon qui lui est soumis.

Architecture d'un réseau de neurones

Architecture d'un réseau de neurones

Les réseaux de neurones sont basés sur un modèle simplifié du neurone biologique, ils se composent généralement de neurones d’entrée, puis une couche dite cachée enfin une couche de sortie (voir schéma). Le tout reliés par des synapses. En entrée sont donnés les différents critères utiles au tri (par exemple l’occurrence de certains mots pour l’identification de spams), la sortie est la réponse du réseau (c’est plutôt un spam ou non).
Mathématiquement le principe repose sur le fait que n’importe quelle fonction peut être approximée par une combinaison linéaire de fonctions d’activation ( sigmoïde, tangente hyperbolique ou fonction de Heaviside ). Ainsi chaque neurone se trouve doté de cette fonction et chaque lien entre les neurones (synapse) est pondéré suivant le problème à résoudre.

Un tel réseau est à la base parfaitement stupide, il ne sait rien faire à part un traitement purement aléatoire de l’information. Comme quand vous voulez apprendre à faire quelque chose, il va falloir s’entraîner!
Durant cette étape nous allons soumettre à notre algorithme un échantillon de caractéristiques connues à trier. On pourra ainsi comparer la réponse du réseau à la réponse correcte. Sachant cela, nous pourrons améliorer le résultat en modifiant les poids synaptiques. Après plusieurs essais, le réseau de neurones aura une sortie proche de celle attendue et sera désormais prêt à utiliser ses capacités sur un échantillon quelconque.
L’analogie avec l’apprentissage humain est très fort : imaginez que je doive apprendre à quelqu’un à reconnaître une souris d’ordinateur. Je vais lui présenter plusieurs objets en lui disant à chaque fois si c’est une souris. Si je lui montre un nombre important de souris (diverses et variées), il va au final réussir à repérer les caractéristiques pertinentes et va pouvoir en extrapoler un «concept souris». Après la phase d’apprentissage, la comparaison à ce concept général sera utilise à chaque fois qu’il devra reconnaître une souris :
«Ah d’accord… Une souris est plus ou moins ovale, possède deux boutons et parfois un bouton au milieu, et elle est souvent raccordée par un fil etc…  Donc si je vois toutes ces caractéristiques sur un objet, j’aurai de bonnes chances de présumer que c’est une souris d’ordinateur».

Très bien, mais je suis un peu loin de la physique des particules ici n’est-ce pas? Alors revenons-y.
En physique des particules, le principe critique est de pouvoir discerner un phénomène bien particulier (le signal) au milieu des millions de collisions amenant à des phénomènes qui ne nous intéresse pas (le bruit de fond). Autrement dit, trouver l’aiguille dans la botte de foin… La théorie physique sous-jacente aux phénomènes observés dans les collisionneurs de particules étant la mécanique quantique, nous ne pouvons jamais avec certitude connaître l’issue d’une collision en particulier. Nous ne pouvons donner que les probabilités.
La méthode première pour augmenter nos chances est d’effectuer des «coupures» : je ne regarde que ce qui a une énergie supérieure à un tel seuil ou encore je ne prends que ce qui a été détecté dans une certaine partie du détecteur etc… Car je sais que c’est dans ces cas que j’ai le plus de probabilités de trouver mon bonheur.
C’est exactement ce que va faire un réseau de neurone, mais de manière optimisée, il va, de part son entraînement, apprendre à ne sélectionner que les évènements possédant les caractéristiques qui ont le plus de chance d’être du signal et rejeter tout ce qui a de fortes chances d’être du bruit de fond.
Le sujet de ma thèse est justement de mettre en évidence un phénomène particulier qui fait intervenir le boson de Higgs et de par la même découvrir (ou exclure) son existence. Il faut savoir que ce phénomène a une probabilité extrêmement faible de survenir, il est donc crucial de pouvoir trier ces évènements. C’est pour cela que je travail à l’aide de réseaux de neurones adaptés à la reconnaissance de ce phénomène.

Akinator, une application internet capable de deviner à quoi vous pensez grâce a un algorithme d'apprentissage.

Akinator, une application internet capable de deviner à quoi vous pensez grâce a un algorithme d'apprentissage.

L’intérêt pour les réseaux de neurones et les algorithmes d’apprentissage en général n’a cessé de croître ces 20 dernières années et sont couramment utilisés dans des domaines aussi variés que les milieux financiers (prédiction des fluctuations de marches), dans le domaine bancaire (pour déceler les fraudes aux cartes de crédit), en aéronautique (pilotes automatiques), en intelligence artificielle etc… Même certaines applications internet se vantant de pouvoir lire dans vos pensées ont vu le jour sur la toile comme 20q ou encore Akinator et utilisent ces algorithmes.

Nous pouvons voir que ces nouvelles techniques d’analyse ont un bel avenir devant eux. Au delà des applications sans cesse plus nombreuses, celles-ci s’améliorent de jour en jour grâce au travail des chercheurs et deviennent ainsi plus puissantes, plus rapides et plus précises. Mais comme nous l’avons vu, malgré le mot neurone, nous sommes encore bien loin d’un cerveau humain. Alors avant d’imaginer une invasion de robot tueurs, sachez bien que Terminator sait pour le moment à peine lire et que c’est déjà pas mal!

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How much data, how soon?

Sunday, February 7th, 2010

First off, we should mention here that CMS’s first paper from collision data has now been accepted for publication by the Journal of High Energy Physics. It’s a measurement of the angular distribution and momentum spectrum of charged particles produced in proton collisions at 0.9 and 2.36 TeV, using about 50,000 collision events recorded in December. It is really wonderful that this result could be turned around so quickly! The first of many papers to come, we hope.

Meanwhile, as already mentioned here, we now have the news of the run plan for the LHC. CERN is preparing for the longest continuous accelerator run of its history, 18 to 24 months. The inverse femtobarn of data to be recorded in that time is a lot, and will give us an opportunity to make many interesting measurements. Whether any of them will be evidence of new physics, I for one am not going to speculate! But if nothing else, this plan sets out what our LHC life for the next ~three years is going to look like.

But a shorter-term question comes to mind — 1 fb-1 over 18 to 24 months is one thing. But what about just the next few months? There is a major international conference coming up in July. What sort of LHC results might be ready by then? That will depend in part on how many collisions are delivered. I’ve seen various estimates for that, but they vary by an order of magnitude depending on the level of optimism, so I’d rather not guess. It will also depend on the experiments’ performance. How efficiently can we record those collisions? How quickly can we process them? How soon will we understand various parts of the detectors well enough to make quality measurements? How smart and clever can we be throughout the entire process? How much sleep is everyone going to get?

Ask me again in July. Meanwhile, game on.

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Heat to kill the pain

Sunday, February 7th, 2010
A sliver of sunlight on the next mountain, amindst clouds and snowfall.

A sliver of sunlight on the next mountain, amidst clouds and snowfall.

I’ve been a bit slow with blogging lately… And the reason is not a lack of things that are going on, far from that. Things got even more busy because of a long-planned week of skiing, and all the things I had to finish before then. Now, teaching is over for this semester, and since yesterday around noon, we are in a small mountain village in south western Austria.

Over the last few days there has been quite a bit of fresh snow, good for the slopes, but bad for visibility, especially since the clouds this morning were right at the altitude of the ski resort. After lunch, we saw a first sliver of sunlight, and the day ended with sunshine. The snow was great to ski on, but of course in the middle of a cloud I went into a little depression a bit to fast without seeing it, and jolted my back. But the sauna in our hotel hopefully helped to loosen the muscles again… Nothing like baking for a while to kill the pain of a long day of skiing.

I hope that over the next few days I’ll also have the time to write a bit about things that have been going on lately: A submitted paper, meeting in Paris, maybe more…. But no promises, skiing comes first!

The tool to soothe sore muscles: The sauna in our hotel in the ski resort.

The tool to soothe sore muscles: The sauna in our hotel in the ski resort.

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