Travels to the edge of time!

As many of you may know, the ATLAS detector (and for that matter CMS) is physically huge. It weighs about 7000 tons, and measures approximately 46 meters in length and 25 meters in diameter. It is literally the size of a small ship, one packed with sensitive silicon sensors, sophisticated electronics, and powerful on-board computers. Take a tour of the ATLAS detector for more details.

When we collide 4 TeV proton beams at the LHC, we re-create conditions that existed about 10^-12 second after the big bang (of course, a lot of the interesting stuff had already happened in the first 10^-37 second). In contrast, the Hubble space telescope can only see as far back as about one or two billion years after the big bang and the WMAP experiment which studies the Cosmic Microwave background observes the Universe about 300,000 years after the big bang! See the accompanying cartoon for a timeline of the Universe.

A brief history of time

All the heavy particles that we are trying to observe in these proton collisions, e.g., the Higgs boson, Supersymmetric partners to the usual particles, etc., existed freely in the aftermath of the big bang. One interesting phenomena that we may have observed is a new state of matter, called the Quark Gluon Plasma, which only exists at very high densities and temperatures. There was indirect evidence for it in the past, and recent results have put it on a firmer footing (e.g., see this result from ATLAS. You can also check out the website for ALICE, another experiment at CERN, which was specifically built to probe this state of matter).

Although, these collisions will be taking place in the laboratory, one can picture the good ship ATLAS travelling back to the beginning of time, and sending information about what is going on there!

Vivek Jain is a Scientist at Indiana University, Bloomington. His current interests range from understanding various aspects of tracking to R-parity violating Supersymmetry. More information about his interests can be found at http://www.indiana.edu/~iubphys/faculty/jain2.shtml

What does 8 TeV mean?

Inspired by Regina Caputo’s excellent post on the CERN accelerator complex, I thought I should give you some fun facts about the LHC (in “human units”).

1) In 2012, the LHC is operating at Center of Mass energy of 8 TeV. What does this mean?

The LHC collides two beams of protons, each with energy of 4 TeV. If you were standing at the collision point, you would feel a total energy of 8 TeV, but you would not have moved in any direction. In contrast, if you were to be hit by only beam, you are likely to fall backward. Substitute a moving car for the proton beam, and you will see what I mean! OK, so what does 8 TeV mean?

What protons feel just before collision

Please look at Wikipedia for a discussion of units. Briefly, 1 Joule is the energy of a 1 Kilogram mass moving with a speed of 1 meter/second (1 J = 1 Kg * (1 m/s) 2). In particle physics units, it is equal to about 6*10¹⁸ electron volts, i.e., 6*10⁶ TeV (1 T(era)eV = 10¹² eV).

When operating at design parameters, the LHC will have two beams of protons, where each beam consists of ~2800 individual bunches, and each bunch contains ~10¹¹ protons. Each proton will have energy of 8 TeV, so the energy of each bunch of protons is ~ 8*10¹¹ TeV, i.e., 133,000 Joules (or 133 kilo Joules).

A bullet fired from a rifle typically weighs 4 grams, and is travelling at about 1000 m/s when it leaves the barrel. This corresponds to a kinetic energy of about 2000 Joules, or 2 kilo Joules, i.e., roughly 1/66 the energy of one bunch of protons. Anti-tank shells (used in WW II) had energies anywhere from 150-800 kilo Joules.

So it is crucial that the beam does not hit something that it is not intended to hit! (BTW, I have not included the energy stored in the magnets, which is a whole different story, and is many times larger).

2) How cold is the LHC?

The magnets in the LHC are superconducting, i.e., they have almost negligible electrical resistance. For this to occur, they have to be cooled to about 2 deg K(elvin), i.e., -271 deg. Celsius, or -455 deg. Fahrenheit.

By studying the Cosmic Microwave Background, which is a form of electromagnetic radiation filling the universe, astronomers have deduced that the current temperature of the universe is about 2.7 deg K.

Some experiments in solid state physics laboratories operate much closer to absolute zero, e.g., at 10^-6 Kelvin, so we cannot claim that the LHC is the coldest place in the universe, but it certainly is one of the coolest (in more ways than one!).

3) How about those magnets?

To keep the proton beam circulating at 7 TeV, we need very strong magnetic fields to essentially keep the beams in the circular ring. For this purpose, the LHC has 1232 dipole magnets. Each of these magnets is 14 m long, weighs about 35 tons, and the required magnetic field is generated by passing about 11700 Amps of current through 5 Km of superconducting wire.

Then there are about 7066 magnets that focus the beam, and otherwise correct the path of the proton beam. For instance, if nothing was done, a proton will “fall” down due to gravity after traveling a mere 850 times around the ring (in one second, a proton goes around the ring about 11000 times).

To learn more about the LHC, please take a look here and at the links therein

Vivek Jain is a Scientist at Indiana University, Bloomington. His current interests range from understanding various aspects of tracking to R-parity violating Supersymmetry. More information about his interests can be found at http://www.indiana.edu/~iubphys/faculty/jain2.shtml

Needle in a haystack

The LHC is designed to collide bunches of protons every 25 ns, i.e., at a 40 MHz rate (40 million/second). In each of these collisions, something happens. Since there is no way we can collect data at this rate, we try to pick only the interesting events, which occur very infrequently; however, this is easier said than done. Experiments like ATLAS employ a very sophisticated filtering system to keep only those events that we are interested in. This is called the trigger system, and it works because the interesting events have unique signatures that can be used to distinguish them from the uninteresting ones.

The ATLAS Trigger and Data Acquisition System

The ATLAS trigger system is a combination of electronic circuit boards and software running on hundreds of computers and is designed to reduce the 40 MHz collision rate to a manageable 200-400 events per second. Each event is expected to be around 1 Mbyte (for comparison, this post corresponds to about 4-5 kilobytes), so you can see that we are dealing with a lot of data. And, all this has to be done in real time. In a previous post, Regina Caputo gave an overview of triggers. Here I expand on that.

Before I get to the numbers of events that we collect, let me first explain a couple of concepts: cross-section of a particular process and luminosity. Cross-section is jargon; basically, it gives you a measure of the probability of a certain kind of event happening, and is a function of the energy of the collision. In general, higher the collision energy, higher is the cross-section of a process, especially if we are producing a heavy particle (there are some subtleties that I won’t get into now). Luminosity is a measure of the “intensity” of the beam. The product of Luminosity and Cross-section gives the number of events that are produced for a given process. The beauty of the trigger system is that it can be configured to pick the kinds of events we want to study.

One common kind of event happens when two protons “glance” off each other, without really breaking up; these are called Elastic Collisions”. Then you have protons colliding and breaking up, and producing “garden-variety” stuff, e.g., pions, kaons, protons, charm quarks, bottom quarks, etc; these are labelled Inelastic Collisions. The sum of all these processes is the “total cross-section”, and is about 70-80 millibarns at a collision energy of 7 TeV, i.e., 1/12th of barn; the concept of a “barn” probably derives from the expression “something is as easy as hitting the side of a barn”! So, a cross-section of 80 millibarns implies a very, very large probability (1 barn = 10^-24 cm² ). At collision energies of 14 TeV, this might increase by about 10-20%.

In contrast, the cross-section for producing a Higgs boson (with mass = 150 GeV, i.e., 150 times the mass of a proton) in 7 TeV collisions is approximately 8 picobarns (8*10^-12 barns), i.e., approximately 10 billion times less than the “total cross-section”. The cross-section for producing top quarks is about 170 picobarns. Events containing a Higgs or top quarks have some unique signatures that are exploited by the trigger algorithms. (At 14 TeV, the cross-section for these interesting events can increase by as much as a factor of five, so you can see why we want to keep increasing the energy of these collisions.)

The LHC is designed to have a luminosity of 10³⁴ , i.e., looking head-on at the beam there are 10³⁴ protons/square cm/second. In reality, each colliding bunch only has about 10¹¹ protons, but they are squeezed into a circle with a radius of 0.003 cm, and come about 40 million times/sec. So, taking the product of cross-section and luminosity, we estimate that we will get approximately 10⁹ “junk events”/second and 0.1 Higgs events/second! Of course, there are other interesting events that we would like to collect, e.g., those containing top quarks that come at a rate of 2 Hz. We also record some of the “garden-variety” events, because they are very useful in understanding how the detector is working. So, this is what the trigger does, separate what we want from what we don’t want, and all in “real time”.

As mentioned above, we plan to write to disk approximately 200-400 events per second, with each event being 1 MB in size. If we run the accelerator continuously for a year, we will collect (6-12)*10¹⁵ bytes of data, i.e., 6-12 petabytes; this will fill about 38,000-76,000 IPods (ones with 160 GB of storage)! Each event is then passed through the reconstruction software (see the ATLAS Blog “From 0-60 in 10 million seconds! – Part 1“), which only adds to its size; talk about standing in front of a fire hose!

–Vivek Jain, Indiana University

p.s. For fun facts about ATLAS, check out the ATLAS pop-up book! You can find it on Facebook, watch a video on YouTube, and purchase it on Amazon.

Vivek Jain is a Scientist at Indiana University, Bloomington. His current interests range from understanding various aspects of tracking to R-parity violating Supersymmetry. More information about his interests can be found at http://www.indiana.edu/~iubphys/faculty/jain2.shtml

From 0-60 in 10 million seconds! – Part 2

This is continuing from the previous post (http://pdg3.lbl.gov/atlasblog/?p=1071), where I discussed how we convert data collected by ATLAS into usable objects. Here I explain the steps to get a Physics result.

I can now use our data sample to prove/disprove the predictions of Supersymmetry (SUSY), string theory or what have you. What steps do I follow? Well, I have to understand the predictions of this theory; is it saying that there will be multiple muons in an event or there will be only one very energetic jet in the event, etc? For instance, the accompanying figure shows the production and decay of SUSY particles, which lead to events with many energetic jets, a muon, and particles that escape the detector without leaving a trace (missing energy), like X₁.

Cartoon of the production and decay of SUSY particles

If the signature is unique, then my life is considerably simpler; essentially, I will write some software to go through each event and pick out those that match the prediction (you can think of this as finding the proverbial (metal) needle in a haystack). If the signal I am searching for is not very unique, then I have to be much cleverer (think of this as looking for a fat, wooden needle in a haystack).

First, I have to decide the selection criteria, e.g., I want one muon with momentum greater than, say, 100 GeV/c, or one electron and exactly two jets, etc. Once I’ve decided the selection criteria, I cannot change them, and have to accept the results, whatever they may be. Otherwise, there is a very real danger of biasing the result. To decide these selection criteria, I may look at simulation, i.e., fake data, and/or sacrifice a small portion of real data to do my studies on. With these criteria, I could have a non-zero number of candidate events, or zero events.

In either case, I have to estimate how many events I expect to see due to garden-variety physics effects, which can occur as much as a million or a billion times more frequently, and may produce a similar signature; this is called background. This can happen because our reconstruction software could mis-identify a pion as a muon, or make a wrong measurement of an electron’s energy, or if we produce enough of these garden-variety events a few of them (out in the “tails”) may look like new physics. So I have to think of all the standard processes that can mimic what I am searching for. One way to do this is to run my analysis software on simulated events; since we know what a garden-variety process looks like, we generate tons of fake data and see if some events look like the new effect that I am looking for. I can also use “real” data, and by applying a different set of selection criteria, come up with what we call “data driven background estimate”. If the background estimate is much less than the number of candidate signal events, excitement mounts, and the result pops up on the collaboration’s radar screen.

There is usually a trade-off between increasing the efficiency of finding signal events and reducing background. If you use loose selection criteria, you expect to find more signal events, i.e., increase in efficiency, but also more background. Since the background can overwhelm the signal, one has to be careful. Conversely, if you choose very strict criteria, you could have zero background, but also zero signal efficiency – not very useful!!

There is one more thing that I need to do, which sometimes can take a while, and for which there is definitely no standard prescription. I need to determine systematic uncertainties, i.e., an error estimate for my methodology, on both the signal efficiency, and on the background estimate. For instance, if I use a meter-scale to measure the length of a table, how do I know the meter-scale is correct? I have to quantify the correctness of the meter-scale. A result in our field has to have systematic uncertainties otherwise it is meaningless. This step is usually a source of lot of arguments. For instance, in the paper mentioned in Part 1 (http://arxiv.org/pdf/1110.6191v2.pdf), we say that there is a systematic uncertainty of 6.6% (see section 6). Depending on whether this is smaller (larger) than the statistical uncertainty, we say that the result is statistics (systematics) limited. In the first case, adding more data is necessary, and in the second case, a better understanding is needed. At times, one can have a statistical fluctuation that disappears by adding more data; conversely, many results go by the wayside because of people not understanding systematic effects.

Since there is no fixed recipe to do analysis, I can sometimes run into obstacles, or my results may look “strange”; I then have to step back and think about what is going on. After I get some preliminary results I have to convince my colleagues that they are valid; this involves giving regular progress reports within the analysis group. This is followed by a detailed note, which is reviewed by an internal committee appointed by the experiment’s Publication Committee and/or the Physics Coordinator. If I pass this hurdle, the note is released to the entire collaboration for further review. All along this process, people ask me to do all sorts of checks, or tell me that I am completely wrong, or whatever. Given that every physicist thinks that he/she is smarter than the next, this process can be cantankerous at times, since I have to respond to and satisfy each and every comment. Once the experiment’s leader signs off on the paper, we submit it to a peer-reviewed journal, where the external referee(s) can make you jump through hoops; sometimes their objections are valid, sometimes not. I have been on both sides of this process. Needless to say, as a referee my objections are always valid!!

Depending on the complexity of the analysis, the time from the start to finish can be anywhere from a few months to a year or more (causing a few more grey hair, or in my case a few less hair). The two papers that I mentioned at the start of part 1 took about 1-2 years each. Luckily, I had collaborators and we divided up the work among ourselves, so I could work on both of them in parallel.

Vivek Jain is a Scientist at Indiana University, Bloomington. His current interests range from understanding various aspects of tracking to R-parity violating Supersymmetry. More information about his interests can be found at http://www.indiana.edu/~iubphys/faculty/jain2.shtml

From 0-60 in 10 million seconds! – Part 1

OK, so I’ll try to give a flavour of how the data that we collect gets turned into a published result. As the title indicates, it takes a while! The post got very long, so I have split it in two parts. The first will talk about reconstructing data, and the second will explain the analysis stage.

I just finished working on two papers, which have now been published, one in the Journal of Instrumentation, and the other in Physics Letters B. You can see them here (http://arxiv.org/abs/1110.6191 and http://arxiv.org/abs/1109.2242). By the way, some of the posts I am linking to are from two to three years ago, so the wording may be dated, but the explanations are still correct.

When an experiment first turns on this process is longer than when it has been running for a while, since it takes time to understand how the detector is behaving. It also depends on the complexity of the analysis one is doing. To be familiar with some of the terms I mention below, you should take the online tour of the ATLAS experiment at http://atlas.ch/etours_exper/index.html; slides 7 and 8 will give you an overview of how different particle species are detected and what the various sub-systems look like. For more details you should go take the whole tour; it is meant for non-scientists.

For each event, data recorded by ATLAS is basically a stream of bytes indicating whether a particular sensor was hit in the tracking detectors or the amount of energy deposited in the calorimeter or the location of a hit in the muon system, etc. Each event is then processed through the reconstruction software. This figure gives you an idea of how different particle species leave a signal in ATLAS.

Signals left behind by different particle species

For instance, the part of the software that deals with the tracking detectors will find hits that could be due to a charged particle like a pion or a muon or an electron; in a typical event there may be 100 or more such particles, mostly pions. By looking at the curvature of the trajectory of a particle as it bends in the magnetic field, we determine its momentum (see Seth Zenz’s post on tracking – http://blogs.uslhc.us/?p=481). Similarly, the software dealing with the calorimeter will look at the energy deposits and try to identify clusters that could be due to a single electron or to a spray of particles (referred to as a “jet”), and so on. I believe the ATLAS reconstruction software runs to more than 1 million lines of code! It is very modular, with different parts written by different physicists (graduate students, post-docs, more senior people, etc.).

However, before the reconstruction software can do its magic, a lot of other things need to be done. All the sub-detectors have to be calibrated. What this means is that we need to know how to convert, say, the size of the electronic signal left behind in the calorimeter into energy units such as MeV (million electron volts – the mass of the electron is 0.5 MeV). This work is done using data that we are collecting now (we also rely on old data from test beams, simulation (http://blogs.uslhc.us/?p=843)), and cosmic rays (http://blogs.uslhc.us/?p=1591).

Similarly, we have to know the location of the individual elements of the tracking detectors as precisely as possible. For instance, by looking at the path of an individual track we can figure out precisely where detector elements are relative to one another; this step is known as alignment. Remember, the Pixel detector (http://blogs.uslhc.us/?p=277) can measure distances of the order of 1/10th the thickness of human hair, so knowing its position is critical.

Periodically, we re-reconstruct the data to take advantage of improved in algorithms, calibration and/or alignment and also to have all of the collected data processed with the same version of the software (see Jamie’s post – http://pdg3.lbl.gov/atlasblog/?p=816).

In the next post, I will take you through the analysis stage.

Vivek Jain is a Scientist at Indiana University, Bloomington. His current interests range from understanding various aspects of tracking to R-parity violating Supersymmetry. More information about his interests can be found at http://www.indiana.edu/~iubphys/faculty/jain2.shtml