US LHC Blog US LHC Blogs 2011-05-09T06:21:54Z WordPress Anna Phan <![CDATA[What made those tracks?]]> 2011-05-09T06:21:54Z 2011-05-08T20:29:24Z Last post I discussed how we reconstruct tracks in LHCb. The next logical step is to talk about how we identify what sort of particle left which track. Continuing with my analogy about animal tracking, animals leave very distinctive tracks related to their paw prints and how they move. You can basically tell what animal left a track by examining it carefully…
The above image was taken from this webpage.

Unfortunately this is not possible with particle tracks. Only given the parameters of a reconstructed track, there is no way to determine what type of particle left that track. More information is required and that is where the RICH1 and RICH2 detectors come in.

The identity of a particle can be determined from its mass. The mass of a particle can be determined from its momentum and speed. The momentum of a charged particle is measured by its deflection in a magnetic field. The purpose of the RICH detectors is to match this information with a measurement of the particle’s speed.

RICH detectors work by measuring emissions of Cherenkov radiation. A charged particle traveling faster than the local speed of light in a medium emits Cherenkov radiation in the form of light, in a cone at an angle which depends on the speed of the particle. The RICH detectors focus the cone of Cherenkov light into a ring using mirrors onto an array of detectors. The radius of this ring provides information about the particle’s speed. Here are a few of the rings seen in RICH2 from an early LHC event.

The system of RICH detectors consists of an upstream detector (RICH1) which uses silica aerogel and \(C_{4}F_{10}\) gas as Cherenkov media located just behind the VELO, and a downstream detector (RICH2) using \(CF_{4}\) positioned after the magnet and tracking system. The use of silica aerogel allows the detector to identify low momentum particles (order of a few GeV), the use of \(C_{4}F_{10}\) allows the identification of higher momentum particles (between 10 GeV to around 65 GeV), while the use of \(CF_{4}\) allows the identification of even higher momentum particles (between 15 GeV to around 100 GeV).

Here is a schematic of the RICH1. Particles will enter the detector from the VELO on the left, then travel through the Cherenkov media, producing Cherenkov light which are reflected by the mirrors into the photon detectors. RICH2 is fairly similar.

The two RICH detectors are responsible for identifying a range of different particles that result from the decay of B mesons. Particle identification is crucial to reduce background in selected final states. For example, in the plots below, we are searching for the decay of a \(B_s\) meson into two \(K\) mesons. On the left, you can see that without the RICH it would be very hard to separate the signal, shown in red, from the backgrounds, since we would have no way of accurately differentiating \(K\) mesons from \(\phi\) mesons and \(\rho\) mesons. We would also have problems differentiating between \(B_s\) mesons and \(B_d\) mesons. On the right, using the RICH detectors, you can see that the signal is much much cleaner. They are very nice, useful detectors!

Robin Erbacher <![CDATA[This just in! Congratulations!]]> 2011-05-07T15:50:33Z 2011-05-07T15:44:23Z

Congratulations goes out to fellow US LHC Blogger Prof. Sarah Demers for just being awarded the Department of Energy’s Early Career Award.  The announcement is naturally featured prominently on the website of her home institution, Yale University Physics Department.  This award has recently replaced the long-standing DOE Outstanding Junior Investigator Award (OJI), which has awarded grants to promising junior faculty members from 1978-2008, an impressive run!  The new Early Career award and has brought the previous National Science Foundation’s Early Career Award and DOE OJI awards together to a more similar format and award level.

These awards can mean a tremendous amount to a new faculty member in particle physics. I was fortunate enough to receive an OJI from the DOE, and fellow blogger Prof. Ken Bloom was fortunate to receive a Career Award from NSF, when we were both new junior professors.  This allowed us both to support perhaps a graduate student and part of a postdoc’s salary as well as our own summer salaries while we established our research programs as new faculty members.  Now Sarah has earned a peer-reviewed grant, which is a major milestone for a new professor, and which enables her to proceed with her successful research program without relying on university start-up funds (which eventually dry up).  Here’s to Sarah’s future success!

Photo by Waldo Jaquith

Aidan Randle-Conde <![CDATA[Tramtastic!]]> 2011-05-06T15:13:49Z 2011-05-06T15:13:49Z CERN is the place to be if you’re a particle physicist! It has everything you could want here: the most promising experiments, all kinds of experts on hand, some of the most powerful computing systems in the world, fascinating seminars. It’s enough to draw people in from all over the world. The only downside is that it’s a bit tricky to get away from CERN for an evening in the city. Well not anymore! This week the tram arrived at CERN, giving us an essential lifeline to Geneva, with all its services and nightlife.

CERN tram

The CERN tram!

The town of Meryin saw the new tram as cause for a street party, with all kinds of entertainers, a jazz band, and free rides on an historic tram. So I went along to see what there was to offer, and how people reacted to the new transport link. Everyone seemed to be very happy about it (except perhaps for a few motorists!) “Great!” I thought, this gives us an easy way to get around. We can socialize more often, making it easier to meet people, enjoy ourselves, and making short trips to CERN all the more fun. There are many people who come to CERN for a few weeks or months at a time over the summer, and there’s pressure to cram as much into their time here as possible. Trimming some minutes off the journeys to and from Geneva makes things just that little bit easier for everyone!

People coming to explore CERN

People coming to explore CERN

What impressed me most was how CERN used this opportunity to reach out the public. In retrospect it was silly that I didn’t realize the tram went to CERN as well as from CERN! The new service included a tram advertizing CERN, taking people right up the Microcosm and the Globe, where they were welcomed in to see what CERN has to offer. Presumably this is only the start of a new way of approaching CERN (literally and figuratively.) This is the first time people can get directly from the heart of Geneva to the center of CERN’s public spaces. The icing on the cake is the tram itself, which is so modern and spacious. First impressions matter, and no longer relying on the rickety number 56 bus to go the final mile will make a big difference to people’s perceptions of CERN. It’s a place which is modern, relevant, well connected and a vital part of the greater Geneva area. It’s deserved a tram stop for years and one has finally arrived!

Flip Tanedo <![CDATA[A diagrammatic hint of masses from the Higgs]]> 2011-05-06T01:09:27Z 2011-05-06T01:09:27Z A couple of weeks ago we met the Higgs boson and discussed its Feynman rules.



Recall the big picture for how to draw Feynman diagrams:



The Higgs is special














Giving mass to the other particles







Bumping up against the Higgs






Some closing remarks




Andrew Adare <![CDATA[Before QCD, there were fireballs]]> 2011-05-04T08:36:32Z 2011-05-04T08:31:37Z How many different particles can you make from quarks? A lot. Every two years or so, the particle data group puts out a catalog of the ones we know about. I always love getting mine in the mail. It’s as big as a phone book, with thin paper like a Bible. The compilation of all the particles and their properties represents a truly massive intellectual effort. Most of the hadrons are just labeled with Greek letters, but they’re festooned with all kinds of superscripts and asterisks, and their properties have names as colorful and idiosyncratic as their discoverers. For example, the neutral Ξ or “cascade” hyperon is a doubly-strange baryon with negative half-integer isospin. To my ear, most science fiction falls flat compared to real conversations between particle physicists.

By adding energy to hadrons, they can change their nature and go into excited states called resonances. The idea is loosely analogous to exciting atoms in a laser or fluorescent lamp, except more relativistic. Their mass can change. The humble proton, for example, can be excited into something called a Δ resonance, which is around 30% more massive, because some of the absorbed energy converts to mass. They don’t hang around very long, but as you look at higher and higher masses, you see more and more of them. By the 1960’s, the number of newly discovered particles and resonances had grown rapidly in step with the energy of the accelerators that produced them. This proliferation led to questions about how to explain such large variety, and what, if any, the limitations are in the number of states. When the quantum-mechanical rules governing properties like spin, charge, angular momentum, etc. were taken into account, the number of hadronic states was found to rise exponentially with mass. This plot is a fairly recent example:


Up to a certain mass, the number of hadrons rises exponentially. The red curve includes particles that weren't plotted in earlier references, represented by the green curve.

When you see a straight line on a semi-log plot, it’s a dead giveaway for an exponential form. Why is that pattern followed? What’s even more interesting is that the number of particles rises with mass at the same rate as it falls with increasing (transverse) momentum, at least below a few GeV. Several creative ideas emerged as attempts to explain the hadron spectra, but a physicist named Rolf Hagedorn gets the credit for developing a theory using statistical mechanics. This is before the era of quarks, remember: he referred to hadrons as “fireballs”, and considered that the heavy resonances were compositions of lighter ones, which were in turn composed of still lighter ones. In one of his lively papers, he said:

His mathematical line of reasoning implied that if you were to collect a bunch of hadrons together and treat them as a gas of particles, their energy would become infinite as the temperature approached a limiting value. He seems to have been quite a character. In the same paper, he concluded:

It follows that T is the highest possible temperature—a kind of ‘boiling point of hadronic matter’ in whose vicinity particle creation becomes so vehement that the temperature cannot increase anymore, no matter how much energy is fed in.

And now we come to the point. Hagedorn’s argument implies a change in the number of fundamental degrees of freedom of the system. In other words, it has to break down to more fundamental building blocks. Instead of remaining as a gas of hadrons, a superheated system would melt into a phase with simpler constituents at a temperature near what is now known as the Hagedorn temperature. Using the best data available, he extrapolated from the known spectra to obtain a value of the critical temperature near 160 MeV, or in more familiar units, a trillion degrees Celsius.

With a more sophisticated understanding thanks to Quantum Chromodynamics (QCD), more tools have become available to check this number. It’s a tough job, because this physics lies in the so-called “non-perturbative” regime,  where pencil-and-paper solutions to the QCD equations don’t work well. But that’s what supercomputers are for. The founders of QCD devised a way to crunch out the answers by dividing space-time itself into a grid of points called the lattice, “playing” the equations forward numerically in steps. It takes a lot of CPU cycles, but the answer seems to corroborate Hagedorn’s estimate.

So nuclear matter melts if you get it hot enough. It was suggested over 40 years ago, and theoretical innovations only seem to confirm it. So what happens then? And is this temperature achievable in the lab? I’ll post again soon to follow up on these questions.


Matthew Tamsett <![CDATA[Why Frank loves SUSY]]> 2011-05-04T03:30:06Z 2011-05-04T03:30:06Z This week I’ve been in Arlington Texas, attending the excellent south western ATLAS analysis jamboree. As a special treat the jamboree dinner was held in conjunction with an event at Southern Methodist University just to the north of Dallas.



Me and Frank Wilczek

Me and Frank Wilczek


















Flip Tanedo <![CDATA[Physics-themed audio and video]]> 2011-05-04T00:38:15Z 2011-05-04T00:38:15Z Hi everyone! Readers of this blog might enjoy some of the following recent multimedia by some well-known  particle physicists.

  • First, a podcast from Jim Gates of the University of Maryland about his path in  Go Tell It on the Mountain (link to iTunes, link to mp3) from The Moth. The talk is from the 2008 World Science Festival, which will be held again this year in New York City in a month.
  • Next, a very nice animated discussion with Daniel Whiteson and Jonathan Feng from UC Irvine on PhD Comics. They discuss dark matter, particle physics, and the Large Hadron Collider.
  • Along the lines of dark matter and particle physics, here’s a mission briefing from NASA on AMS-2, the “particle detector in space,” featuring principal investigator (and Nobel laureate for the discovery of the J/ψ particle) Sam Ting. Matt mentioned AMS-2 in his inaugural post. A lot of particle physicists are excited about AMS due to recent anomalies in the spectrum cosmic positrons and anti-protons that may be a result of dark matter interactions.
  • Finally, some time ago I had a general-public-level post about Nima Arkani-Hamed‘s (and collaborators) work in scattering amplitudes. For those with a technical background who interested in learning more, his informal lectures to the Cornell particle theory group are now posted online: part 1, part 2, part 3, part 4, part 5. For those who can’t get enough, there’s also an ongoing program at the KITP with lots of recorded talks. These links are at the level of theoretical physicists doing work in the field; for a general public version, see Nima’s messenger lectures.
Robin Erbacher <![CDATA[The April Meeting]]> 2011-05-03T21:08:07Z 2011-05-03T10:06:10Z 2011 American Physical Society April Meeting, Anaheim, CA












Hello from Anaheim, California!

Yes it is that time of year: the April APS (American Physical Society) meeting.   It has become tradition that each year in April, the membership of the APS in the Division of Particles and Fields meets together with the membership of somewhat related divisions: Astrophysics, Nuclear Physics, Computational Physics, Physics of Beams, and Plasma Physics.  I find these meetings particularly broadening, as I can sometimes hear about topics that I do not necessarily get exposure to all of the time in my day-to-day work in hadron collider physics.  In fact, some of the more entertaining session titles I have seen here include “Black Holes: Nature’s Ultimate Spinmeisters, “Much Ado about Nothing: The Quantum Vacuum, and “So Many Dynamos: Flow-Generated Magnetic Fields in Nature, in the Computer, and in the Lab.  (I believe the latter also wins for longest session title, barely beating out the more straightforward and understandable– for me at least– “Precision Measurements, Fundamental Symmetries, and Tests of the Standard Model“.)

Other interesting topics at this meeting, such as “Nuclear Weapons at 65“, “The Status of Arms Control“, and “Best Practices in K12 Physics Teacher Education Programs”  are a result of the inclusion of the Forum on Society, the Forum on Education, and other such broad-interest topics in this meeting.  Yet in my opinion one of the most important roles that these APS (and the Divisional) meetings play is to provide a forum for students to give 10-15 minute parallel session talks on their own analysis.  At other conferences it is rare to have single-result talks rather than summaries, and summaries are generally given to more senior people.  This is often the first (and sometimes only) chance a graduate student has to prepare a talk for a non-expert (non-working group) audience. With these talks they learn to prepare a summary of their work with an appropriate level of detail, omitting jargon, timing it properly, and most importantly, stating the big picture (the context) of their work, as well as the bottom line.  When I was a graduate student I found the APS meetings to be valuable training in public presentations.  For this reason I sent my student, David Cox, from Fermilab to Anaheim to present his own recent work on our searches for a massive top-like, perhaps 4th generation, quark (“tprime“) at the Tevatron.  He has actually had practice giving talks at other meetings, but this is still good experience for him.  He is also attending useful career sessions for graduate students as well.

My own main purpose for attending this meeting has been to present results in an invited plenary talk on Top Quark Physics, which I delivered on Saturday morning during one of several plenary sessions. My talk focused on results from the Tevatron‘s CDF and D0 experiments, not from the LHC.  This was in fact a tall order for a 30 minute talk, since the large datasets from Run 2 of the Tevatron, together with the years of experience with these detectors and analysis tools, have meant a plethora of interesting and innovative results from CDF and D0 constantly being released to the public.  Measurements of the top quark mass for example, the all-important electroweak parameter, have reached sensitivities to less than a percent relative, much better than the Run 2 goal of 3 GeV.  Yet some relatively new measurements, such as the studies of the difference between the mass of the top quark and the mass of the anti-top quark (expected to be zero if CPT is conserved), still have very little statistical sensitivity due to the difficulty of the measurement.

The measurements of the forward-backward asymmetry AFB in top pair production have received attention earlier this year not only because both CDF and D0 continue to see a 2-sigma (or more) discrepancy with the theoretical predictions, but also because there appears to be a dependence on the invariant mass of the top pair system, which could imply the existence of new high-mass particles decaying to top quarks.  (The original AFB measurement at the Tevatron was actually performed by my postdoc, Tom Schwarz — CDF Top Group Convener, when he was a thesis student at U. Michigan, and we’ve continued to study this anomaly with our collaborators from Michigan since then.)  This measurement has generated quite a bit of theoretical interest so I was happy to take some time for these measurements,  along with many other interesting topics, such as whether the top quark really has an exotic -4/3 charge instead of the +2/3 charge of the Standard Model.

While the Tevatron is producing spectacular results in the area of top quark physics (and many other areas), the reality is that even at half of the design energy, the LHC will soon outshine the Tevatron for most measurements.  The production cross section (production rate) for top pairs at the 7 TeV LHC is much greater than at the ~2 TeV Tevatron due to the higher energies available.  Measurements of things like the top-antitop mass difference, or the top quark charge, will soon have better sensitivity at the LHC.  It may take a little longer for the LHC experiments to catch up in the area of the precision top mass measurement, mainly due to the complicated systematic uncertainties that need to be taken into account, but eventually the Tevatron will be bested there as well.  The AFB measurement will be difficult to challenge or improve upon at the LHC, however, since the asymmetry is thought to result from quark-antiquark annihilation, which is much more dominant at the Tevatron’s proton anti-proton collider than the proton-proton collider of the LHC.  For that we will still have more to say from the Tevatron’s final datasets.

Giving this talk has been a nice way for me to pay tribute to the amazing results from dedicated analysts at the Tevatron over the ~16 years since the top quark was discovered there. Although the Tevatron is scheduled to close down later this year , I cannot help be excited about the new projects I and many others are working on at the LHC.  Some are topics that we could barely touch at the Tevatron such as boosted top quarks, which I am currently working on at CMS.  (See Flip Tanedo’s recent post on this subject from Atlas.)  Some, like our tprime searches, have shown hints of excess events on the tails of the distribution, so we are excited to see whether this excess grows at the LHC.  Regardless of the particular topic, we are all approaching the LHC with the knowledge we have gained from the Tevatron, and are excited to continue to explore the particle frontier with the greater rates and energies of the LHC.  And we are definitely on the look-out for discoveries!


Sarah Demers <![CDATA[Making ATLAS Results Public]]> 2011-05-02T14:02:57Z 2011-05-02T14:02:57Z Any large collaboration like ATLAS needs a process for allowing members to communicate their work to each other and to the public. There have been some recent questions about how this process works, so I’m going to address the topic in this post.

We particle physicists are a bit unusual, though not unique, among scientific disciplines in that our authors sign official papers in alphabetical order as opposed to being ranked by how much they contributed to the work. We are also famous for our long author lists, which for the large LHC experiments include up to a few thousand people since all members of the collaboration sign each paper unless individuals request that their names be removed.

There has been some debate in the field about whether our author lists should be more exclusive and include only those people who worked directly on the physics analysis being published. I have always appreciated the lack of squabbling over author lists and the way our inclusive list gives a nod to the fact that our detector is incredibly complex and could only be built, maintained and interpreted for physics results with a large team. There are also many people who have contributed to the upstream work of an analysis, which makes the final result possible. The counter-argument is that it is nearly impossible for people outside the field to know who did the actual analysis work for any particular result. I think that people inside the field can usually find out who did what, even at other experiments, pretty easily by seeing who gives the related talks at the conferences and from reference letters within the collaboration, and even just by asking around.

Regardless of where you come down on the author list debate, the fact that our author list is currently the entire collaboration puts a burden on our result approval process in that every author needs to be given the opportunity to comment on every result he/she will sign.

Before we worry about communicating our results to the world, we need to have a mechanism to communicate our work in real time to each other within the collaboration. This allows us to scrutinize the steps as they are taken so we know that we are building a solid analysis. We achieve that by giving presentations at meetings and writing emails, but we communicate probably most efficiently by writing notes to each other to document snapshots of the early stages of an analysis. This documentation can have a much smaller list of authors who are responsible for the specific set of ideas presented. Documents like this are simply labeled “COM” for “communication,” and they are not intended for public consumption. Any ATLAS member can write a COM note at any time, and people do not necessarily put the names of all of the people on which their work relies on the author list.

If you want your work to move toward official internal ATLAS approval, you can request that it be given the status “INT” for “internal”. At this point leaders of the relevant physics group appoint reviewers, and the authors have a chance to get feedback in a formal way from other collaboration members. A note that has gained INT status has undergone at least some peer review, though it stays internal to the collaboration.  The content of the INT note is often too technical for general public interest, but can be invaluable for other ATLAS collaborators who want to either reproduce a result or take the analysis to the next step with a good understanding of everything that has come before.

Some COM-notes can also become public (i.e. available to everyone on the planet). Together with published papers, these public notes report the scientific output of the experiment.  In order for the result to take the final step to become public, an editorial board is appointed, and often a new note is written (starting as a COM note) with an attempt to remove ATLAS-specific jargon and details that people outside the collaboration would not necessarily find useful. With the help of the editorial board, the note is brought to a stage where it is ready to receive feedback from the entire collaboration. If the note is approved by the collaboration it will be posted to an archive that is available to the public, submitted for publication and/or the results will be shown at conferences.

There are, of course, many details that I haven’t described, but the end result is that an analysis that has been publicly approved by ATLAS will have come under scrutiny at many stages of the process. People work very hard to make sure that the results presented to the public are worthy of being signed by the collaboration. Our goal is to work as a team as quickly as we can to get these results out to the rest of the world while at the same time ensuring that we have not made mistakes.  Our scientific reputation is on the line.

Brian Dorney <![CDATA[A Day in the Life: Cross-Sections]]> 2011-05-01T20:10:53Z 2011-05-01T20:10:53Z Hello again!

I thought I might take some time to describe what an experimental particle physicist actually does on a day-to-day basis.

I remember when I was an undergraduate studying physics, I found particle physics so fascinating.  It was this high tech world that seemed so glamorous.  But, at the time, I had no idea what a particle physicist did!  Big shiny detectors, and billion dollar machines were all that I knew about!

But, now that I’ve spent two years in the field, perhaps I can give you an idea of what happens “behind the scenes.”  I’m going to talk about cross-sections, and how we go about finding them.

(If you are unfamiliar with what a cross-section is, then take a look at these nice posts by Aidan Randle-Conde and Seth Zenz found here, and here, respectively.)


The Bane of My Existence: Coding

So one of the things I’ve gotten far better at over the years has been computer programming.  Sadly, I purposefully avoided almost all computer-programming classes during my undergraduate studies.  This was a horrifically stupid idea in retrospect.  And if anyone reading this is interested in pursuing a career in a math, science, or an engineering related discipline; my suggestion to you is learn to code before you’re expected to.  It will do wonders for your career.

Moving on though, long gone are the days were particle physics experiments relied on photographic plates and cloud chambers.  Nowadays our detectors record everything electronically.

The detectors spit out an electric signal.  Then we perform what is called “reconstruction” on these signals (using computer algorithms), to make physics objects (observable particles, like photons, electrons, muons, jets, etc…).

Now, if you are a computer programmer, you might know where I’m going with this discussion.  If not a bit of some background info is required.  There is something called object-oriented programming (OOP).  In OOP you make what is called a class.  A class is like a template, which you use make objects.

Imagine I own a factory that makes cars.  Somewhere in my factory are the blue prints for the cars I produce.  Well a blueprint is what a class is in OOP.  Each blueprint is a template for a car, just as each class is a template for an object.  So we see that in this analogy, a car represents an object.

Now classes have what are called methods and data members.  On the blueprint for the 2012 Ford Mustang there is a data member for the car’s color, and there is a method for what type of transmission the car will be manufactured with.  So data members store information (car’s color), and methods perform actions on objects (manufacture with transmission type X).

But what do classes and methods have to do with High Energy Physics?  Well, physicists use classes present in an OOP language to store and analyze our data.  In CMS we use two OOP languages to accomplish this; they are python and C++; and we make our own custom classes to store our data.

So what types of classes do we have?  Well, there are classes for all physics objects (electron, a muon, a jet, etc…), detector pieces, and various other things.  In fact we’ve created an entire software framework to perform our research.

But, lets take the electron class as an example.  Because of these classes, all electrons in our data have the same structure.  The way they are accessed is the same regardless of the electron; and all the information about a particular electron is stored/retrieved in the same way (via the methods & data members of the electron class).

This is a very good thing, because a physicist may have to look at hundreds of thousands of electrons in the course of their research; so having a standardized way to access information is beneficial.

So in summary, to do research and analyze data we write code, and we run our analysis code on super-computing clusters around the world.


Event Selection

Okay, now we know we need to write code to get anywhere, but what do we do from there?

Well we need to decide on what type of physics we want to study.  And how to find that physics in the data.

In 2010, the CMS detector recorded 43 inverse picobarns of data.  Now, there are approximately 7 * 1010 (or 70 billion) proton-proton collisions in one inverse pico-barn.  This makes for a total of  3 trillion recorded proton-proton collision events for 2010.

That’s a lot of data…and not all of it is going to be useful to a physicist.  But as they say, one person’s trash is another’s treasure.

For example, in my own analysis I look for low energy muons inside jets because this helps me find b-Jets in an event.  But an electro-weak physicist looking for W or Z’s decaying to muons is going to think the events that I use are garbage.  My muons are low energy whereas an electro-weak physicist needs high energy muons.  My muons are within jets whereas an electroweak physicist needs muons that are isolated (nothing else around them).  So while my data is perfect for the physics I’m trying  to do, it is worthless to an electroweak physicist.

With this in mind we as physicists make checklists of what an event needs for it to be considered useful.  This type of checklist is called a pre-selection, and it will include what type of data acquisition trigger was used; and a list of physics objects that must be present (and restrictions on them) in the event.

After an event has been tagged as being possibly useful to us, we investigate it further using another checklist, called a full event-selection.

For example, I might be interested in studying B-Physics, and I want to look at the correlations between two B-Hadrons produced in an event.


My pre-selection check-list for this might be:

  • Jets detected by the High Level Trigger
  • Presence of a Secondary Vertex in the event

My Event Selection Checklist might then be:

  • The most energetic jet in the event must have an energy above threshold X
  • The invariant mass of the secondary vertex must be above some value Y.


In case you are wondering, a secondary vertex is a point at which a heavy particle decayed within the detector, this occurs away from the primary vertex (point at which the protons collided).  The invariant mass of the secondary vertex is found by summing the invariant masses of all of the products that the heavy particle decayed into.

So in summary, we make checklists of what we are looking for; and then implement this into our computer code.



Finally we need to measure the efficiency of our selection process, or what percent of events that are created do we actually select.  We use a combination of real collision data and simulated data to make this estimation.  Then our efficiency is a measure of everything from the detectors ability to record the collision, our reconstruction process, and up to our specific selection techniques listed above.

The reason we need to measure this efficiency is that we are, more often then not, interested in performing inclusive measurements in physics.  Meaning, I want to study every single proton-proton collision that could give insight into my physics process of interest (i.e all events in which two B-Hadrons were produced).

The problem is, I could never possibly study all such collisions.  For one, we are colliding protons every 50 nano-seconds at the LHC currently.  We design our trigger system to only capture the most interesting events, and this sometimes causes us to purposefully drop a few here and there.  But this is a story for another time, and Aidan has done a good job describing this already in this post.

Anyway, so we convert our measurements back to this “inclusive” case.  This conversion allows us to say, “well if we were able to record all possible events, this is what our results would look like.”

But how is this accomplished?  Well, one way to do this is restrict ourselves to the point of which our data acquisition triggers have an efficiency of greater then 99%.


Courtesy of the CMS Collaboration


Here is a plot that shows the efficiency to record an event via several single jet triggers available in CMS.  Three triggers are plotted here, they each have a minimum energy/momentum threshold to detect a jet.

As an example, if in a proton-proton collision, a jet is produced with a momentum of 50 GeV/c; then this event will be recorded:

  • 99% of the time by the trigger represented by the green line
  • 50% of the time by the trigger represented by the blue line
  • 0% of the time by the trigger represented by the red line (The Jet’s momentum isn’t high enough for that  trigger!).

So by playing with the jet energy thresholds in our Event Selection above, I can ensure that my detector will inclusively record all events in  this region of phase space (99% or higher chance to record an event).

But as I said earlier this is just one way we can transform our measurements into inclusive measurements.  There are usually other steps that must also be done to get back to the inclusive case.


Experimental Cross-Section

Now that I’ve selected my events and physics objects within those events; and determined the efficiency of this process, I’m ready to make my measurement.

This part of the process takes much less time then our previous two steps.  In fact, it may take months for physicists to write our analysis code, and become confident in our selection techniques (rigorous investigation is required for this part).

Then, to determine an inclusive cross-section with respect to some quantity (say the angle between two B-Hadrons), I make a histogram.

The angle between two B-Hadrons can be between 0 and 180 degrees.  So the x-axis of this histogram is in degrees, and is binned into different regions.  The y-axis is then counts, or number of times I observed a B-Hadron pair with angle φ between them.

Next, I need to divide by the number of counts in each bin of my histogram by three things:


  1. The integrated luminosity of my data sample (see Aidan’s post “What the L!?”), this makes the Y-Axis go from counts to units of inverse barn (or more appropriately, inverse picobarn).
  2. My selection efficiency, this takes my measurement to the inclusive case
  3. The width of each bin, this puts my cross-section purely in units of inverse barn (rather then inverse barn times degrees)


And finally, I’m left with a cross-section:

Image Courtesy of the CMS Collaboration.  Here the data points are shown in black, and the theoretical prediction is shown in green.


I’m now left with the differential scattering cross-section, for the production of 2 B-Hadrons, with respect to the angle between the two B-Hadrons.

Three cross-sections are actually plotted here.  Each of them corresponds to one of the triggers in our efficiency graph above.  The researchers who made this plot also multiplied two of the distributions by a factor of 2 and a factor of 4 (as shown in the legend).  This was done so the three curves wouldn’t fall on top of each other, and other scientists could interpret the data in an easier fashion.

This plot tells us that, at LHC Energies, B-Hadron pairs are more likely to be produced with small angles between them (the data points near the zero region on the x-axis are higher then the other points).  This is because a process called gluon splitting (a gluon splits into a quark and anti-quark) occurs more often then other processes.  Due to conservation of momentum, the angle between the quark/anti-quark that the gluon split into is very small.  But this is also a lengthy discussion for another time!

But that’s how we experimentally measure cross-sections, from start to finish.  We need to: write computer code, make a checklists of what we are looking for, determine the efficiency of our selection technique, and then make our measurement.

So hopefully this gives you an idea as to what an experimental particle physicists actually does on a day to day basis.  This is by no means all we do, measuring cross-sections is only one part of the research being done at the LHC.  I could not hope to, in a single post, cover all of our research activities.


Until next time,





Anna Phan <![CDATA[Are those tracks?]]> 2011-05-07T17:19:20Z 2011-05-01T16:11:18Z Today’s post is a continuation of my description of the LHCb detector. From my other post on identifying vertices using the VELO, we naturally progress to tracking charged particles. As I mentioned in my first post, the VELO along with the TT, T1, T2, and T3 stations are used to reconstruct particle tracks inside LHCb.

Particle tracking is somewhat akin to animal tracking. The first thing you need is some material where particle tracks will leave a trace. It is very hard to find animal tracks on concrete, but very easy on sand or snow…

This where the VELO along with the TT, T1, T2 and T3 come in. When charged particles pass though these detector components, they leave hits. Two different types of technology are used to measure particle interactions. The VELO, TT and the inner sections of the T stations are made of layers of silicon strips while the outer sections of the T stations consist of straw tubes filled with a mixture of argon and carbon dioxide gas. The layout of the TT and T stations is shown below. The silicon sections are coloured purple, while the drift tube sections are coloured blue.

Depending on which detector components register hits, tracks can be classified into four different groups:

  1. Long tracks which pass through all parts of the tracking system, from the VELO, through the TT to the outer T stations;
  2. Upstream tracks which only pass through the VELO and TT stations;
  3. Downstream tracks which only pass though the TT and T stations;
  4. VELO tracks which only pass through the VELO; and
  5. T tracks which only pass through the T stations.

Each of these track types is shown in the image below:

All of these types of tracks are useful for reconstructing B meson events. An example of a reconstructed event is displayed below. The average number of successfully reconstructed tracks in fully simulated B meson events is about 72, which are distributed among the track types as follows: 26 long tracks, 11 upstream tracks, 4 downstream tracks, 26 VELO tracks and 5 T tracks.

You may notice that in the images above, the tracks are curved. This is due to the LHCb dipole magnet. The experiment contains what essentially is a very large horseshoe magnet, which produces a field of 4 Tesla between its two large coils. Particles normally travel in straight lines, but in a magnetic field the paths of charged particles curve, with positive and negative particles moving in opposite directions.

So that’s how we measure particle tracks in LHCb and the types of tracks we record. Stay tuned for how we figure out what type of particle left which track…

Matthew Tamsett <![CDATA[Jet spotting]]> 2011-05-02T12:33:32Z 2011-05-01T02:02:19Z Every week I try to take a few hours to study something different. The idea being that this will give me a broader sense of what’s going on within the ATLAS collaboration and the world of particle physics at large. Last week I was mostly watching Gavin Salam’s superb lectures on jets. They’re available as videos from here.

So what is a jet? It’s certainly nothing to do with aeroplanes. Jets are what we observe at ATLAS when a highly energetic quark or a gluon (collectively referred to here as partons) is produced in a collision.

I won’t take the time to explain the physics behind a jet and how they come into being. Those interested can see Flip’s excellent post. In essence, instead of the individual partons, what we see in the detector is a spray of collimated particles. This is what we refer to as a “jet”.

At hadron colliders, such as the LHC, jets are everywhere. In fact the vast majority of interactions at a hadron collider will result in the creation of multiple jets. They are our window on partons and on to the strong force itself.

Being so ubiquitous it’s important that we’re able to reliably identify these within our detector. Unfortunately this isn’t always such an easy task. The event display below illustrates a typical jet event. How many jets do you see?



Here is ATLAS’s answer.



In this case the jets have helpfully been colour coded. In real life, this doesn’t happen.

As you can tell the definition of a jet can be somewhat ambiguous. At ATLAS the trigger system has to quickly identify thousands of jets a second in order to pick out the interesting events to record. Identifying such a large number of jets is no easy feat.

To solve this problem we use jet algorithms. These are pieces of software which define jets based on what we see in the detector. They come in all sorts of shapes and sizes, from “simple” versions where a jet is defined as all the particles inside a cone, to more advanced versions which sequentially combine together individual particles based on their separation and energy.

Different algorithms have different strengths and weaknesses. Cone based jets are relatively simple and provide nice, round jets. Unfortunately though, the jets they identify can easily be altered by changes within the jet itself, or by small amounts of energy coming from unrelated collisions. This makes them very hard to compare to the predictions from theory. More complicated algorithms such as the “kT” algorithm remove these ambiguities, but often result in “ugly” irregularly shaped jets.

The current vogue algorithm both at CMS and ATLAS is the so called “anti-kT” algorithm. This starts from the most energetic single particles and sequentially combines them with everything nearby, stopping at some pre-defined distance. This algorithm results in the identification of nice, round jets, and does this consistently regardless of the small amounts of additional energy or the structure of the jets themselves.

Aidan Randle-Conde <![CDATA[Science friendly browsing]]> 2011-04-27T18:24:48Z 2011-04-27T17:54:17Z (Note: This post requires JavaScript to be enabled on your browser.)

For years the internet has been a wonderful tool for people all over the world, bringing distant communities together, changing the way think about communication and information and having a huge impact for the better in nearly every aspect of our lives. Unfortunately there are still major problems when it comes to sharing scientific knowledge. This is changing very quickly though, making this an exciting time for internet-savvy scientists.

LaTeX sets the high standards we have come to expect for mathematical markup.

How did this kind of situation arise? Science journals have had their own markup language, LaTeX, for decades, predating the internet by many years. LaTeX is available to anyone makes it very easy to generate simple, attractive documents with excellent support for a wide variety of mathematical symbols. (Making complicated documents isn’t quite so easy, but still possible!) Making documents like this can be very intensive, as every margin and the space between every character is analyzed, with restrictions imposed by paper sizes.

On the other hand, the hypertext markup language (HTML) and cascading style sheets (CSS) are the standards which are widely used on the internet, and they are focused mainly on the aesthetics of more popular kinds of journalism. The HTML standards are intended to work on any operating system, and they should give a semantic description of the content of a webpage, without consideration for style. The CSS then take over and decide how the information is displayed on the screen. (Check out the CSS Zen Garden to see the power of CSS.) In principle, writing a webpage that follows the HTML and CSS standards is quite easy, but in reality it’s it can be a very problematic and tedious task. The internet is a dynamic medium, with different developers trying different tricks, different browsers supporting different features and no real control concerning the best practices. Groups such as W3C have tried to standardize HTML and CSS, with quite a lot of success, but it’s a slow process and it has taken years to get to where we are today.

CSS makes the internet an aesthetically compelling medium. (CSS Zen Garden)

Trying to get mathematical markup with these kinds of constraints is quite tricky! Math is inherently two dimensional, making good use of subscripts, superscripts, indices, fraction, square roots… HTML is much better at handling long passages of text which flow from one line to the next, without much scope for anything as exciting as a nested superscript. And so for a long time it became very awkward to include math on a webpage.

Over the years there have been many approaches to this problem, including LaTeX2HTML, MathML, using images, or expecting the poor user to interpret LaTeX markup! Eventually, the CSS standards settled down, browsers started to conform to the same behavior, and it became possible to display math without the use of any images, plugins or other suboptimal solutions.

With the exciting developments of Web 2.0, we have access to MathJAX. We can take LaTeX markup and put it directly into a webpage and MathJAX can turn this:

  \nabla \times \vec{H} & = & \vec{J} + \frac{\partial \vec{D}}{\partial t}

into this:

\nabla \times \vec{H} = \vec{J} + \frac{\partial \vec{D}}{\partial t}

Beautiful! It also works inline like this: \(E=mc^2\) becomes \(E=mc^2\). (None of this will work if JavaScript is disabled on your browser, which is a shame for you, because it looks very pretty on the page!) Using MathJAX is as simple as writing normal LaTeX between \[ and \] symbols for block-level text, \( and \) symbols for inline text.

We finally have a way to show equations on any browser, with any operating system, that complies with all the standards laid out by the W3C. So much for math markup. What about technical drawings and graphs? Scientists have been using vector graphics in their work for decades, so it would also be nice to have a way to show these kinds of images.

This is the kind of image we can make with the canvas! Making graphs can be easy, and the output can be beautiful and interactive.

Some browsers have supported vector graphics for a few years, but once again, different browsers behave differently, and vector graphics have been developed rather late, so there are large performance issues. However, with the development of the next generation of HTML browsers should support a brand new kind of image, the HMTL5 canvas. It allows designers of websites to draw detailed images on the fly, even allowing the user to interact with the images! It will take some time before most of the users on the internet can have access to the HTML5 canvas, so until then we can’t rely on these new features to share information.

On the other hand it means that we living in a very exciting time where anyone can develop their own work using the canvas, and help shape our experiences with the internet in the future! The standards used online have always lagged behind how the latest developers are using the tools at their disposal, and when the standards get updated the ingenuity of the developers is taken into account. Soon the canvas will support 3D graphics, making our online experiences even richer! Want to help shape how this is developed? Then get involved! Try out the canvas today and see what you can create! There are dozens of fascinating examples at Canvas Demos. Here are some of my favorites:

  • MolGrabber 3D- a great way to visualize molecules in three dimensions.
  • Flot- how to show graphs on a webpage.
  • Pacman- a clone of the classic arcade game!

The internet is going to get very cool in the near future, giving us the ability to share information like never before! When anyone can create animations and simulations, blogs like this will become even more interactive, even more compelling and even more useful. I can’t wait to see what MathJAX and the HTML5 canvas will deliver!

Brian Dorney <![CDATA[The CERN Accelerator Complex]]> 2011-04-27T17:00:45Z 2011-04-24T20:58:56Z With all the buzz this past week regarding the breaking of the world instantaneous luminosity record, I thought it might be interesting for our readers to get an idea of how we as physicists achieved this goal.

Namely, how do we accelerate particles?

(This may be a review for some of our veteran readers due to this older post by Regina)


The Physics of Acceleration

Firstly, physicists rely on a principle many of us learn in our introductory physics courses, the Lorentz Force Law.  This result, from classical electromagnetism, states that a charged particle in the presence of external electric and/or magnetic fields will experience a force.  The direction and magnitude (how strong) of the force depends on the sign of the particle’s electric charge and its velocity (or direction its moving, and with what speed).

So how does this relate to accelerators?  Accelerators use radio frequency cavities to accelerate particles.  A cavity has several conductors that are hooked up to an alternating current source.  Between conductors there is empty space, but this space is spanned by a uniform electric field.  This field will accelerate a particle in a specific direction (again, depending on the sign of the particle’s electric charge).  The trick is to flip this current source such that as a charged particle goes through a succession of cavities it continues to accelerate, rather than be slowed down at various points.

A cool Java Applet that will help you visualize this acceleration process via radio frequency cavities can be found here, courtesy of CERN.

Now that’s the electric field portion of the Lorentz Force Law, what about the magnetic?  Well, magnetic fields are closed circular loops, as you get farther and farther away from their source the radii of these loops continually increases.  Whereas electric fields are straight lines that extend out to infinity (and never intersect) in all directions from their source.  This makes the physics of magnetic fields very different from that of electric fields.  We can use magnetic fields to bend the track (or path) of charged particles.  A nice demonstration of this can be found here (or any of the other thousands of hits I got for Googling “Cathode Ray Tube + YouTube”).

Imagine, if you will, a beam of light; you can focus the beam (make it smaller) by using a glass lens, you can also change the direction of the beam using a simple mirror.  Now, the LHC ring uses what are called dipole and quadropole magnets to steer and focus the beam.  If you combine the effects of these magnets you can make what is called a magnetic lens, or more broadly termed “Magnetic Optics.”  In fact, the LHC’s magnetic optics currently focus the beam to a diameter of ~90 micro-meters  (the diameter of a human hair is ~100 micro-meters, although it varies from person to person, and where on the body the hair is taken from).  However, the magnetic optics system was designed to focus the beam to a diameter of ~33 micro-meters.

In fact, the LHC uses 1232 dipole magnets, and 506 quadrupole magnets.  These magnets have  a peak magnetic field of 8.3 Tesla, or 100,000 times stronger than Earth’s magnetic field.  An example of the typical magnetic field emitted by the dipole magnets of the LHC ring is shown here [1]:

Image courtesy of CERN


The colored portions of the diagram indicate the magnetic flux, or the amount of magnetic field passing through a given area.  Whereas the arrows indicate the direction of the magnetic field.  The two circles (in blue) in the center of the diagram indicate the beam pipes for beams one and two.  Notice how the arrows (direction of the magnetic field) point in opposite directions!  This allows CERN Accelerator physicists to control two counter-rotating beams of protons in the same beam pipe (Excellent Question John Wells)!

Thus, accelerator physicists at CERN use electric fields to accelerate the LHC proton/lead-ion beams and the magnetic fields to steer and squeeze these beams (Also, these “magnetic optics” systems are responsible for “Lumi Leveling” discussed by Anna Phan earlier this week).

However, this isn’t the complete story, things like length contraction, and synchrotron radiation affect the acceleration process, and design of our accelerators.  But these are stories best left for another time.


The Accelerator Complex

But where does this process start?  Well, to answer this let’s start off with the schematic of this system:

Image courtesy of CERN

One of our readers (thanks GP!) has given us this helpful link that visualizes the acceleration process at the LHC (however, when this video was made, the LHC was going to be operating at design specifications…but more on that later).

A proton’s journey starts in a tank of research grade hydrogen gas (impurities are measured in parts per million, or parts per billion).  We first take molecular hydrogen (a diatomic molecule for those of you keeping track) and break it down into atomic hydrogen (individual atoms).  Next, we strip hydrogen’s lone electron from the atom (0:00 in the video linked above).  We are now left with a sample of pure protons.  These protons are then passed into the LINear ACcelerator 2 (LINAC2, 0:50 in the video linked above), which is the tiny purple line in the bottom middle of the above figure.

The LINAC 2 then accelerates these protons to an energy of 50 MeV, or to a 31.4% percent of the speed of light [2].  The “M” stands for mega-, or times one million.  The “eV” stands for electron-volts, which is the conventional unit of high energy physics.  But what is an electron-volt, and how does it relate to everyday life?  Well, for that answer, Christine Nattrass has done such a good job comparing the electron-volt to a chocolate bar, that any description I could give pales in comparison to hers.

Moving right along, now thanks to special relativity, we know that as objects approach the speed of light, they “gain mass.”  This is because energy and mass are equivalent currencies in physics.  An object at rest has a specific mass, and a specific energy.  But when the object is in motion, it has a kinetic energy associated with it.  The faster the object is moving, the more kinetic energy, and thus the more mass it has.  At 31.4% the speed of light, a proton’s mass is ~1.05 times its rest mass (or the proton’s mass when it is not moving).

So this is a cruel fact of nature.  As objects increase in speed, it becomes increasingly more difficult to accelerate them further!  This is a direct result of Newton’s Second Law.  If a force is applied to a light object (one with little mass) it will accelerate very rapidly; however, the same force applied to a massive object will cause a very small acceleration.

Now at an energy of 50 MeV, travelling at 31.4% the speed of light, and with a mass of 1.05 times its rest mass, the protons are injected into the Proton Synchrotron (PS) Booster (1:07 in the video).  This is the ellipse, labeled BOOSTER, in the diagram above.  The PS Booster then accelerates the protons to an energy of 1.4 GeV (where  the “G” stands for giga- or a billion times!), and a velocity that is 91.6% the speed of light [2].  The proton’s mass is now ~2.49 times its rest mass.

The PS Booster then feeds into the Proton Synchrotron (labeled as PS above, see 2:03 in video), which was CERN’s first synchrotron (and was brought online in November of 1959).  The PS then further accelerates the protons to an energy of 25 GeV, and a velocity that is 99.93% the speed of light [2].  The proton’s mass is now ~26.73 times its rest mass!  Wait, WHAT!?

At 31.4% the speed of light, the proton’s mass has barely changed from its rest mass.  Then at 91.6% the speed of light (roughly three times the previous speed), the proton’s mass was only two and a half times its rest mass.  Now, we increased speed by barely 8%, and the proton’s mass was increase by a factor of 10!?

This comes back to the statement earlier, objects become increasingly more difficult to accelerate the faster they are moving.  But this is clearly a non-linear affect.  To get an idea of what this looks like mathematically, take a look at this link here [3].  In this plot, the Y-axis is in multiples of rest mass (or Energy), and the x-axis is velocity, in multiples of the speed of light, c.  The red line is this relativistic effect that we are seeing, as we go from ~91% to 99% the speed of light, the mass increases gigantically!

But back to the proton’s journey, the PS injects the protons into the Super Proton Synchrotron (names in high energy physics are either very generic, and bland, or very outlandish, e.g. matter can be charming).  The Super Proton Synchrotron (SPS, also labeled as such in above diagram, 3:10 in video above) came online in 1976, and it was in 1983 that the W and Z bosons (mediators of the weak nuclear force) were discovered when the SPS was colliding protons with anti-protons.  In today’s world however, the SPS accelerates protons to an energy of 450 GeV, with a velocity of 99.9998% the speed of light [2].  The mass of the proton is now ~500 times its rest mass.

The SPS then injects the proton beams directly into the Large Hadron Collider.  This occurs at 3:35 in video linked above, however, when this video was recorded the LHC was operating at design energy, with each proton having an energy of 7 TeV (“T” for tera-, a million million times).  However, presently the LHC accelerates the proton to half of the design energy, and a velocity of 99.9999964% the speed of light.  The protons are then made to collide in the heart of the detectors.  At this point the protons have a mass that is ~3730 times their rest mass!



So, the breaking of the world instantaneous luminosity record was not the result of one single instrument, but the combined might of CERN’s full accelerator complex, and in no small part by the magnetic optics systems in these accelerators (I realize I haven’t gone into much detail regarding this, my goal was simply to introduce you to the acceleration process that our beams undergo before collisions).


Until next time,






[1] CERN, “LHC Design Report,”

[2] CERN, “CERN faq: The LHC Guide,”

[3]  School of Physics, University of Southern Wales, Sydney Australia,

Anna Phan <![CDATA[LUMI LEVELING: What, Why and How?]]> 2011-05-07T17:19:59Z 2011-04-24T16:19:59Z I’m interrupting my descriptions of LHCb to discuss something more relevant to the current status of the LHC. Namely this LHC status from just after midnight the other day:

Ken has already discussed the luminosity record in this post, and today I’ll be discussing luminosity leveling (LUMI LEVELING). You may be wondering what this has got to do with LHCb? Well, interaction point 8 (IP8) is where LHCb is located as can be seen in this image:

Aidan has timely discussed what luminosity is in this post where he said that larger instantaneous luminosity means having more events, we want to do everything we can to increase instantaneous luminosity. However, if you’ve been looking at the LHC luminosity plots for 2011, like the one for peak instantaneous luminosity below, you might have noticed that the instantaneous luminosities of ALICE and LHCb are lower than those of ATLAS and CMS.

The reason for the difference between the experiments is that the design instantaneous luminosities for LHCb and ALICE are much lower than for ATLAS and CMS. The target instantaneous luminosity for LHCb is \(2 \times 10^{32} cm^{-2} s^{-1} \) to \(3 \times 10^{32} cm^{-2} s^{-1}\) and for ALICE is \(5 \times 10^{29} cm^{-2} s^{-1} \) to \(5 \times 10^{30} cm^{-2} s^{-1}\) while ATLAS and CMS are designed for an instantaneous luminosity of \(10^{34} cm^{-2} s^{-1}\).

This means that while the LHC operators are trying to maximise instantaneous luminosity at ATLAS and CMS, they are also trying to provide LHCb and ALICE with their appropriate luminosities.

As Aidan mentioned in his post, there are a couple of different ways to modify instantaneous luminosity: you can change the number of proton bunches in the beam or you can change the area of the proton bunches that collide.

Last year the LHC operators optimised the collision conditions and this year have been increasing instantaneous luminosity by increasing the number of proton bunches.

The varying instantaneous luminosity requirements of the experiments have so far been handled by having a different number of proton bunches colliding at each of the interaction points. For example, last week there were 228 proton bunches in the beam, 214 of which were colliding in ATLAS and CMS, 12 of which were colliding in ALICE and 180 of which were colliding in LHCb.

However as more and more proton bunches are injected into the beam, it is not possible to continue to limit the instantaneous luminosity at ALICE and LHCb by limiting the number of colliding bunches. Instead, the LHC operators need to modify the collision conditions. This is what luminosity leveling refers to.

Luminosity leveling is performed by moving the proton beams relative to each other to modify the area available for interactions as the bunches pass through each other. This concept is much easier to explain diagrammatically: if the centres of the beams are aligned like on the left, there are more interactions than if they are offset from each other like on the right.

This luminosity leveling process can be seen in action in the graph below from the nice long LHC fill from last night. You can see the ATLAS and CMS luminosities slowly decreasing due to collisions, while the LHCb luminosity stays roughly constant at \(1.3 \times 10^{32} cm^{-2} s^{-1} \), where the vertical red lines are when the beam adjustments were made.