Collisions at the Large Hadron Collider happen fast. 40 million times a second, bunches of 1011 protons are smashed together. The rate of these collisions is so fast that the computing infrastructure of the experiments can’t keep up with all of them. We are not able to read out and store the result of every collision that happens, so we have to ‘throw out’ nearly all of them. Luckily most of these collisions are not very interesting anyways. Most of them are low energy interactions of quarks and gluons via the strong force that have been already been studied at previous colliders. In fact, the interesting processes, like ones that create a Higgs boson, can happen billions of times less often than the uninteresting ones.
The LHC experiments are thus faced with a very interesting challenge, how do you decide extremely quickly whether an event is interesting and worth keeping or not? This what the ‘trigger’ system, the Marie Kondo of LHC experiments, are designed to do. CMS for example has a two-tiered trigger system. The first level has 4 microseconds to make a decision and must reduce the event rate from 40 millions events per second to 100,000. This speed requirement means the decision has to be made using at the hardware level, requiring the use of specialized electronics to quickly to synthesize the raw information from the detector into a rough idea of what happened in the event. Selected events are then passed to the High Level Trigger (HLT), which has 150 milliseconds to run versions of the CMS reconstruction algorithms to further reduce the event rate to a thousand per second.
While this system works very well for most uses of the data, like measuring the decay of Higgs bosons, sometimes it can be a significant obstacle. If you want to look through the data for evidence of a new particle that is relatively light, it can be difficult to prevent the trigger from throwing out possible signal events. This is because one of the most basic criteria the trigger uses to select ‘interesting’ events is that they leave a significant amount of energy in the detector. But the decay products of a new particle that is relatively light won’t have a substantial amount of energy and thus may look ‘uninteresting’ to the trigger.
In order to get the most out of their collisions, experimenters are thinking hard about these problems and devising new ways to look for signals the triggers might be missing. One idea is to save additional events from the HLT in a substantially reduced size. Rather than saving the raw information from the event, that can be fully processed at a later time, instead the only the output of the quick reconstruction done by the trigger is saved. At the cost of some precision, this can reduce the size of each event by roughly two orders of magnitude, allowing events with significantly lower energy to be stored. CMS and ATLAS have used this technique to look for new particles decaying to two jets and LHCb has used it to look for dark photons. The use of these fast reconstruction techniques allows them to search for, and rule out the existence of, particles with much lower masses than otherwise possible. As experiments explore new computing infrastructures (like GPU’s) to speed up their high level triggers, they may try to do even more sophisticated analyses using these techniques.
But experimenters aren’t just satisfied with getting more out of their high level triggers, they want to revamp the low-level ones as well. In order to get these hardware-level triggers to make smarter decisions, experimenters are trying get them to run machine learning models. Machine learning has become very popular tool to look for rare signals in LHC data. One of the advantages of machine learning models is that once they have been trained, they can make complex inferences in a very short amount of time. Perfect for a trigger! Now a group of experimentalists have developed a library that can translate the most popular types machine learning models into a format that can be run on the Field Programmable Gate Arrays used in lowest level triggers. This would allow experiments to quickly identify events from rare signals that have complex signatures that the current low-level triggers don’t have time to look for.
The LHC experiments are working hard to get the most out their collisions. There could be particles being produced in LHC collisions already but we haven’t been able to see them because of our current triggers, but these new techniques are trying to cover our current blind spots. Look out for new ideas on how to quickly search for interesting signatures, especially as we get closer the high luminosity upgrade of the LHC.
There is a current crisis in the field of cosmology and it may lead to our next breakthrough in understanding the universe. In the late 1990’s measurements of distant supernovae showed that contrary to expectations at the time, the universe’s expansion was accelerating rather than slowing down. This implied the existence of a mysterious “dark energy” throughout the universe, propelling this accelerated expansion. Today, some people once again think that our measurements of the current expansion rate, the Hubble constant, are indicating that there is something about the universe we don’t understand.
The current cosmological standard model, called ΛCDM, is a phenomenological model of describing all contents of the universe. It includes regular visible matter, Cold Dark Matter (CDM), and dark energy. It is an extremely bare-bones model; assuming dark matter interacts only gravitationally and that dark energy is just a simple cosmological constant (Λ) which gives a constant energy density to space itself. For the last 20 years this model has been rigorously tested but new measurements might be beginning to show that it has some holes. Measurements of the early universe based on ΛCDM and extrapolated to today predict a different rate of expansion than what is currently being measured, and cosmologists are taking this war over the Hubble constant very seriously.
On one side of this Hubble controversy are measurements from the early universe. The most important of these is based on the Cosmic Microwave Background (CMB), light directly from the hot plasma of the Big Bang that has been traveling billions of years directly to our telescopes. This light from the early universe is nearly uniform in temperature, but by analyzing the pattern of slightly hotter and colder spots, cosmologists can extract the 6 free parameters of ΛCDM. These parameters encode the relative amount of energy contained in regular matter, dark matter, and dark energy. Then based on these parameters, they can infer what the current expansion rate of the universe should be. The current best measurements of the CMB come from the Planck collaboration which can infer the Hubble constant with a precision of less than 1%.
On the other side of the debate are the late-universe (or local) measurements of the expansion. The most famous of these is based on a ‘distance ladder’, where several stages of measurements are used to calibrate distances of astronomical objects. First, geometric properties are used to calibrate the brightness of pulsating stars (Cepheids). Cepheids are then used to calibrate the absolute brightness of exploding supernovae. The expansion rate of the universe can then be measured by relating the red-shift (the amount the light from these objects has been stretched by the universe’s expansion) and the distance of these supernovae. This is the method that was used to discover dark energy in 1990’s and earned its pioneers a Nobel prize. As they have collected more data and techniques have been refined, the measurement’s precision has improved dramatically.
In the last few years the tension between the two values of the Hubble constant has steadily grown. This had let cosmologists to scrutinize both sets of measurements very closely but so far no flaws have been found. Both of these measurements are incredibly complex, and many cosmologists still assumed that there was some unknown systematic error in one of them that was the culprit. But recently, other measurements both the early and late universe have started to weigh in and they seem to agree with the Planck and distance ladder results. Currently the tension between the early and late measurements of the Hubble constant sits between 4 to 6 sigma, depending on which set of measurements you combine. While there are still many who believe there is something wrong with the measurements, others have started to take seriously that this is pointing to a real issue with ΛCDM, and there is something in the universe we don’t understand. In other words, New Physics!
So what ideas have theorists put forward that can explain the disagreement? In general theorists have actually had a hard time trying to come up with models that can explain this disagreement while not running afoul of the multitude of other cosmological data we have, but some solutions have been found. Two of the most promising approaches involve changing the composition of universe just before the time the CMB was emitted.
The first of these is called Early Dark Energy. It is a phenomenological model that posits the existence of another type of dark energy, that behaves similarly to a cosmological constant early in the universe but then fades away relatively quickly as the universe expands. This model is able to slightly improve Planck’s fit to the CMB data while changing the contents of the early universe enough to alter the predicted Hubble constant to be consistent with the local value. Critics of the model have feel that its parameters had to been finely tuned for the solution to work. However there has been some work in mimicking its success with a particle-physics based model.
The other notable attempt at resolving the tension involves adding additional types of neutrinos and positing that neutrinos interact with each other in a much stronger way than the Standard Model. This similarly changes the interpretation of the CMB measurements to predict a larger expansion rate. The authors also posit that this new physics in the neutrino sector may be related to current anomalies seen in neutrino physics experiments that are also currently lacking an explanation. However follow up work has showed that it is hard to reconcile such strongly self-interacting neutrinos with laboratory experiments and other cosmological probes.
At present the situation remains very unclear. Some cosmologists believe this is the end of ΛCDM, and others still believe there is an issue with one of the measurements. For those who believe new physics is the solution, there is no consensus about what the best model is. However, the next few years should start to clarify things. Other late-universe measurements of the Hubble constant, using gravitational lensing or even gravitational waves, should continue to improve their precision and could give skeptics greater confidence to the distance ladder result. Next generation CMB experiments will eventually come online as well, and will offer greater precision than the Planck measurement. Theorists will probably come up with more possible resolutions, and point out additional measurements to be made that can confirm or refute their models. For those hoping for a breakthrough in our understanding of the universe, this is definitely something to keep an eye on!
Article: “Anomaly Detection for Resonant New Physics with Machine Learning”
Authors: Jack H. Collins, Kiel Howe, Benjamin Nachman
Reference : https://arxiv.org/abs/1805.02664
One of the main goals of LHC experiments is to look for signals of physics beyond the Standard Model; new particles that may explain some of the mysteries the Standard Model doesn’t answer. The typical way this works is that theorists come up with a new particle that would solve some mystery and they spell out how it interacts with the particles we already know about. Then experimentalists design a strategy of how to search for evidence of that particle in the mountains of data that the LHC produces. So far none of the searches performed in this way have seen any definitive evidence of new particles, leading experimentalists to rule out a lot of the parameter space of theorists favorite models.
Despite this extensive program of searches, one might wonder if we are still missing something. What if there was a new particle in the data, waiting to be discovered, but theorists haven’t thought of it yet so it hasn’t been looked for? This gives experimentalists a very interesting challenge, how do you look for something new, when you don’t know what you are looking for? One approach, which Particle Bites has talked about before, is to look at as many final states as possible and compare what you see in data to simulation and look for any large deviations. This is a good approach, but may be limited in its sensitivity to small signals. When a normal search for a specific model is performed one usually makes a series of selection requirements on the data, that are chosen to remove background events and keep signal events. Nowadays, these selection requirements are getting more complex, often using neural networks, a common type of machine learning model, trained to discriminate signal versus background. Without some sort of selection like this you may miss a smaller signal within the large amount of background events.
This new approach lets the neural network itself decide what signal to look for. It uses part of the data itself to train a neural network to find a signal, and then uses the rest of the data to actually look for that signal. This lets you search for many different kinds of models at the same time!
If that sounds like magic, lets try to break it down. You have to assume something about the new particle you are looking for, and the technique here assumes it forms a resonant peak. This is a common assumption of searches. If a new particle were being produced in LHC collisions and then decaying, then you would get an excess of events where the invariant mass of its decay products have a particular value. So if you plotted the number of events in bins of invariant mass you would expect a new particle to show up as a nice peak on top of a relatively smooth background distribution. This is a very common search strategy, and often colloquially referred to as a ‘bump hunt’. This strategy was how the Higgs boson was discovered in 2012.
The other secret ingredient we need is the idea of Classification Without Labels (abbreviated CWoLa, pronounced like koala). The way neural networks are usually trained in high energy physics is using fully labeled simulated examples. The network is shown a set of examples and then guesses which are signal and which are background. Using the true label of the event, the network is told which of the examples it got wrong, its parameters are updated accordingly, and it slowly improves. The crucial challenge when trying to train using real data is that we don’t know the true label of any of data, so its hard to tell the network how to improve. Rather than trying to use the true labels of any of the events, the CWoLA technique uses mixtures of events. Lets say you have 2 mixed samples of events, sample A and sample B, but you know that sample A has more signal events in it than sample B. Then, instead of trying to classify signal versus background directly, you can train a classifier to distinguish between events from sample A and events from sample B and what that network will learn to do is distinguish between signal and background. You can actually show that the optimal classifier for distinguishing the two mixed samples is the same as the optimal classifier of signal versus background. Even more amazing, this technique actually works quite well in practice, achieving good results even when there is only a few percent of signal in one of the samples.
The technique described in the paper combines these two ideas in a clever way. Because we expect the new particle to show up in a narrow region of invariant mass, you can use some of your data to train a classifier to distinguish between events in a given slice of invariant mass from other events. If there is no signal with a mass in that region then the classifier should essentially learn nothing, but if there was a signal in that region that the classifier should learn to separate signal and background. Then one can apply that classifier to select events in the rest of your data (which hasn’t been used in the training) and look for a peak that would indicate a new particle. Because you don’t know ahead of time what mass any new particle should have, you scan over the whole range you have sufficient data for, looking for a new particle in each slice.
The specific case that they use to demonstrate the power of this technique is for new particles decaying to pairs of jets. On the surface, jets, the large sprays of particles produced when quark or gluon is made in a LHC collision, all look the same. But actually the insides of jets, their sub-structure, can contain very useful information about what kind of particle produced it. If a new particle that is produced decays into other particles, like top quarks, W bosons or some a new BSM particle, before decaying into quarks then there will be a lot of interesting sub-structure to the resulting jet, which can be used to distinguish it from regular jets. In this paper the neural network uses information about the sub-structure for both of the jets in event to determine if the event is signal-like or background-like.
The authors test out their new technique on a simulated dataset, containing some events where a new particle is produced and a large number of QCD background events. They train a neural network to distinguish events in a window of invariant mass of the jet pair from other events. With no selection applied there is no visible bump in the dijet invariant mass spectrum. With their technique they are able to train a classifier that can reject enough background such that a clear mass peak of the new particle shows up. This shows that you can find a new particle without relying on searching for a particular model, allowing you to be sensitive to particles overlooked by existing searches.
This paper was one of the first to really demonstrate the power of machine-learning based searches. There is actually a competition being held to inspire researchers to try out other techniques on a mock dataset. So expect to see more new search strategies utilizing machine learning being released soon. Of course the real excitement will be when a search like this is applied to real data and we can see if machines can find new physics that us humans have overlooked!
When theorists were first developing quantum field theory in the 1940’s they quickly ran into a problem. Some of their calculations kept producing infinities which didn’t make physical sense. After scratching their heads for a while they eventually came up with a procedure known as renormalization to solve the problem. Renormalization neatly hid away the infinities that were plaguing their calculations by absorbing them into the constants (like masses and couplings) in the theory, but it also produced some surprising predictions. Renormalization said that all these ‘constants’ weren’t actually constant at all! The value of these ‘constants’ depended on the energy scale at which you probed the theory.
One of the most famous realizations of this phenomena is the ‘running’ of the strong coupling constant. The value of a coupling encodes the strength of a force. The strong nuclear force, responsible for holding protons and neutrons together, is actually so strong at low energies our normal techniques for calculation don’t work. But in 1973, Gross, Wilczek and Politzer realized that in quantum chromodynamics (QCD), the quantum field theory describing the strong force, renormalization would make the strong coupling constant ‘run’ smaller at high energies. This meant at higher energies one could use normal perturbative techniques to do calculations. This behavior of the strong force is called ‘asymptotic freedom’ and earned them a Nobel prize. Thanks to asymptotic freedom, it is actually much easier for us to understand what QCD predicts for high energy LHC collisions than for the properties of bound states like the proton.
Now for the first time, CMS has measured the running of a new fundamental parameter, the mass of the top quark. More than just being a cool thing to see, measuring how the top quark mass runs tests our understanding of QCD and can also be sensitive to physics beyond the Standard Model. The top quark is the heaviest fundamental particle we know about, and many think that it has a key role to play in solving some puzzles of the Standard Model. In order to measure the top quark mass at different energies, CMS used the fact that the rate of producing a top quark-antiquark pair depends on the mass of the top quark. So by measuring this rate at different energies they can extract the top quark mass at different scales.
Top quarks nearly always decay into W-bosons and b quarks. Like all quarks, the b quarks then create a large shower of particles before they reach the detector called a jet. The W-bosons can decay either into a lepton and a neutrino or two quarks. The CMS detector is very good at reconstructing leptons and jets, but neutrinos escape undetected. However one can infer the presence of neutrinos in an event because we know energy must be conserved in the collision, so if neutrinos are produced we will see ‘missing’ energy in the event. The CMS analyzers looked for top anti-top pairs where one W-boson decayed to an electron and a neutrino and the other decayed to a muon and a neutrino. By using information about the electron, muon, missing energy, and jets in an event, the kinematics of the top and anti-top pair can be reconstructed.
The measured running of the top quark mass is shown in Figure 2. The data agree with the predicted running from QCD at the level of 1.1 sigma, and the no-running hypothesis is excluded at above 95% confidence level. Rather than being limited by the amount of data, the main uncertainties in this result come from the theoretical understanding of the top quark production and decay, which the analyzers need to model very precisely in order to extract the top quark mass. So CMS will need some help from theorists if they want to improve this result in the future.
When a single experimental collaboration has a few thousand contributors (and even more opinions), there are a lot of rules. These rules dictate everything from how you get authorship rights to how you get chosen to give a conference talk. In fact, this rulebook is so thorough that it could be the topic of a whole other post. But for now, I want to focus on one rule in particular, a rule that has only been around for a few decades in particle physics but is considered one of the most important practices of good science: blinding.
In brief, blinding is the notion that it’s experimentally compromising for a scientist to look at the data before finalizing the analysis. As much as we like to think of ourselves as perfectly objective observers, the truth is, when we really really want a particular result (let’s say a SUSY discovery), that desire can bias our work. For instance, imagine you were looking at actual collision data while you were designing a signal region. You might unconsciously craft your selection in such a way to force an excess of data over background prediction. To avoid such human influences, particle physics experiments “blind” their analyses while they are under construction, and only look at the data once everything else is in place and validated.
This technique has kept the field of particle physics in rigorous shape for quite a while. But there’s always been a subtle downside to this practice. If we only ever look at the data after we finalize an analysis, we are trapped within the confines of theoretically motivated signatures. In this blinding paradigm, we’ll look at all the places that theory has shone a spotlight on, but we won’t look everywhere. Our whole game is to search for new physics. But what if amongst all our signal regions and hypothesis testing and neural net classifications… we’ve simply missed something?
It is this nagging question that motivates a specific method of combing the LHC datasets for new physics, one that the authors of this paper call a “structured, global and automated way to search for new physics.” With this proposal, we can let the data itself tell us where to look and throw unblinding caution to the winds.
The idea is simple: scan the whole ATLAS dataset for discrepancies, setting a threshold for what defines a feature as “interesting”. If this preliminary scan stumbles upon a mysterious excess of data over Standard Model background, don’t just run straight to Stockholm proclaiming a discovery. Instead, simply remember to look at this area again once more data is collected. If your feature of interest is a fluctuation, it will wash out and go away. If not, you can keep watching it until you collect enough statistics to do the running to Stockholm bit. Essentially, you let a first scan of the data rather than theory define your signal regions of interest. In fact, all the cool kids are doing it: H1, CDF, D0, and evenATLASandCMS have performed earlier versions of this general search.
The nuts and bolts of this particular paper include 3.2 fb-1 of 2015 13 TeV LHC data to try out. Since the whole goal of this strategy is to be as general as possible, we might as well go big or go home with potential topologies. To that end, the authors comb through all the data and select any event “involving high pT isolated leptons (electrons and muons), photons, jets, b-tagged jets and missing transverse momentum”. All of the backgrounds are simply modeled with Monte Carlo simulation.
Once we have all these events, we need to sort them. Here, “the classification includes all possible final state configurations and object multiplicities, e.g. if a data event with seven reconstructed muons is found it is classified in a ‘7- muon’ event class (7μ).” When you add up all the possible permutations of objects and multiplicities, you come up with a cool 639 event classes with at least 1 data event and a Standard Model expectation of at least 0.1.
From here, it’s just a matter of checking data vs. MC agreement and the pulls for each event class. The authors also apply some measures to weed out the low stat or otherwise sketchy regions; for instance, 1 electron + many jets is more likely to be multijet faking a lepton and shouldn’t necessarily be considered as a good event category. Once this logic applied, you can plot all of your SRs together grouped by category; Figure 2 shows an example for the multijet events. The paper includes 10 of these plots in total, with regions ranging in complexity from nothing but 1μ1j to more complicated final states like ETmiss2μ1γ4j (say that five times fast.)
Once we can see data next to Standard Model prediction for all these categories, it’s necessary to have a way to measure just how unusual an excess may be. The authors of this paper implement an algorithm that searches for the region of largest deviation in the distributions of two variables that are good at discriminating background from new physics. These are the effective mass, the sum of all jet and missing momenta, and the invariant mass, computed with all visible objects and no missing energy.
For each deviation found, a simple likelihood function is built as the convolution of probability density functions (pdfs): one Poissonian pdf to describe the event yields, and Gaussian pdfs for each systematic uncertainty. The integral of this function, p0, is the probability that the Standard Model expectation fluctuated to the observed yield. This p0 value is an industry standard in particle physics: a value of p0 < 3e-7 is our threshold for discovery.
Sadly (or reassuringly), the smallest p0 value found in this scan is 3e-04 (in the 1m1e4b2j event class). To figure out precisely how significant this value is, the authors ran a series of pseudoexperiments for each event class and applied the same scanning algorithm to them, to determine how often such a deviation would occur in a wholly different fake dataset. In fact, a p0 of 3e-04 was expected 70% of the pseudoexperiments.
So the excesses that were observed are not (so far) significant enough to focus on. But the beauty of this analysis strategy is that this deviation can be easily followed up with the addition of a newer dataset. Think of these general searches as the sidekick of the superheros that are our flagship SUSY, exotics, and dark matter searches. They can help us dot i’s and cross t’s, make sure nothing falls through the cracks— and eventually, just maybe, make a discovery.
In lieu of a typical HEP paper summary this month, I’m linking a comprehensive overview of the new results shown at this year’s Moriond conference, originally published in the CERN EP Department Newsletter. Since this includes the latest and greatest from all four experiments on the LHC ring (ATLAS, CMS, ALICE, and LHCb), you can take it as a sort of “state-of-the-field”. Here is a sneak preview:
“Every March, particle physicists around the world take two weeks to promote results, share opinions and do a bit of skiing in between. This is the Moriond tradition and the 52nd iteration of the conference took place this year in La Thuile, Italy. Each of the four main experiments on the LHC ring presented a variety of new and exciting results, providing an overview of the current state of the field, while shaping the discussion for future efforts.”
Read more in my article for the CERN EP Department Newsletter here!
March is an exciting month for high energy physicists. Every year at this time, scientists from all over the world gather for the annual Moriond Conference, where all of the latest results are shown and discussed. Now that this physics Christmas season is over, I, like many other physicists, am sifting through the proceedings, trying to get a hint of what is the new cool physics to be chasing after. My conclusions? The Higgsino search is high on this list.
The search for Higgsinos falls under the broad and complex umbrella of searches for supersymmetry (SUSY). We’ve talked about SUSY on Particlebites in the past; see a recent post on thestop search for reference. Recall that the basic prediction of SUSY is that every boson in the Standard Model has a fermionic supersymmetric partner, and every fermion gets a bosonic partner.
So then what exactly is a Higgsino? The naming convention of SUSY would indicate that the –ino suffix means that a Higgsino is the supersymmetric partner of the Higgs boson. This is partly true, but the whole story is a bit more complicated, and requires some understanding of the Higgs mechanism.
To summarize, in our Standard Model, the photon carries the electromagnetic force, and the W and Z carry the weak force. But before electroweak symmetry breaking, these bosons did not have such distinct tasks. Rather, there were three massless bosons, the B, W, and Higgs, which together all carried the electroweak force. It is the supersymmetric partners of these three bosons that mix to form new mass eigenstates, which we call simply charginos or neutralinos, depending on their charge. When we search for new particles, we are searching for these mass eigenstates, and then interpreting our results in the context of electroweak-inos.
SUSY searches can be broken into many different analyses, each targeting a particular particle or group of particles in this new sector. Starting with the particles that are suspected to have low mass is a good idea, since we’re more likely to observe these at the current LHC collision energy. If we begin with these light particles, and add in the popular theory of naturalness, we conclude that Higgsinos will be the easiest to find of all the new SUSY particles. More specifically, the theory predicts three Higgsinos that mix into two neutralinos and a chargino, each with a mass around 200-300 GeV, but with a very small mass splitting between the three. See Figure 1 for a sample mass spectra of all these particles, where N and C indicate neutralino or chargino respectively (keep in mind this is just a possibility; in principle, any bino/wino/higgsino mass hierarchy is allowed.)
This is both good news and bad news. The good part is that we have reason to think that there are three Higgsinos with masses that are well within our reach at the LHC. The bad news is that this mass spectrum is very compressed, making the Higgsinos extremely difficult to detect experimentally. This is due to the fact that when C1 or N2 decays to N1 (the lightest neutralino), there is very little mass difference leftover to supply energy to the decay products. As a result, all of the final state objects (two N1s plus a W or a Z as a byproduct, see Figure 2) will have very low momentum and thus are very difficult to detect.
The CMS collaboration Higgsino analysis documented here uses a clever analysis strategy for such compressed decay scenarios. Since initial state radiation (ISR) jets occur often in proton-proton collisions, you can ask for your event to have one. This jet radiating from the collision will give the system a kick in the opposite direction, providing enough energy to those soft particles for them to be detectable. At the end of the day, the analysis team looks for events with ISR, missing transverse energy (MET), and two soft opposite sign leptons from the Z decay (to distinguish from hadronic SM-like backgrounds). Figure 3 shows a basic diagram of what these signal events would look like.
In order to conduct this search, several new analysis techniques were employed. Reconstruction of leptons at low pT becomes extremely important in this regime, and the standard cone isolation of the lepton and impact parameter cuts are used to ensure proper lepton identification. New discriminating variables are also added, which exploit kinematic information about the lepton and the soft particles around it, in order to distinguish “prompt” (signal) leptons from those that may have come from a jet and are thus “non prompt” (background.)
In addition, the analysis team paid special attention to the triggers that could be used to select signal events from the immense number of collisions, creating a new “compressed” trigger that uses combined information from both soft muons (pT > 5 GeV) and missing energy ( > 125 GeV).
With all of this effort, the group is able to probe down to a mass splitting between Higgsinos of 20 GeV, excluding N2 masses up to 230 GeV. This is an especially remarkable result because the current strongest limit on Higgsinos comes from the LEP experiment, a result that is over ten years old! Because the Higgsino searches are strongly limited by the low cross section of electroweak SUSY, additional data will certainly mean that these searches will proceed quickly, and more stringent bounds will be placed (or, perhaps, a discovery is in store!)
Article: Improved search for a light sterile neutrino with the full configuration of the Daya Bay Experiment Authors: Daya Bay Collaboration Reference:arXiv:1607.01174
Today I bring you news from the Daya Bay reactor neutrino experiment, which detects neutrinos emitted by three nuclear power plants on the southern coast of China. The results in this paper are based on the first 621 days of data, through November 2013; more data remain to be analyzed, and we can expect a final result after the experiment ends in 2017.
For more on sterile neutrinos, see also this recent post by Eve.
Neutrinos exist in three flavors, each corresponding to one of the charged leptons: electron neutrinos (), muon neutrinos () and tau neutrinos (). When a neutrino is born via the weak interaction, it is created in a particular flavor eigenstate. So, for example, a neutrino born in the sun is always an electron neutrino. However, the electron neutrino does not have a definite mass. Instead, each flavor eigenstate is a linear combination of the three mass eigenstates. This “mixing” of the flavor and mass eigenstates is described by the PMNS matrix, as shown in Figure 2.
The PMNS matrix can be parameterized by 4 numbers: three mixing angles (θ12, θ23 and θ13) and a phase (δ).1 These parameters aren’t known a priori — they must be measured by experiments.
Solar neutrinos stream outward in all directions from their birthplace in the sun. Some intercept Earth, where human-built neutrino observatories can inventory their flavors. After traveling 150 million kilometers, only ⅓ of them register as electron neutrinos — the other ⅔ have transformed along the way into muon or tau neutrinos. These neutrino flavor oscillations are the experimental signature of neutrino mixing, and the means by which we can tease out the values of the PMNS parameters. In any specific situation, the probability of measuring each type of neutrino is described by some experiment-specific parameters (the neutrino energy, distance from the source, and initial neutrino flavor) and some fundamental parameters of the theory (the PMNS mixing parameters and the neutrino mass-squared differences). By doing a variety of measurements with different neutrino sources and different source-to-detector (“baseline”) distances, we can attempt to constrain or measure the individual theory parameters. This has been a major focus of the worldwide experimental neutrino program for the past 15 years.
1 This assumes the neutrino is a Dirac particle. If the neutrino is a Majorana particle, there are two more phases, for a total of 6 parameters in the PMNS matrix.
Many neutrino experiments have confirmed our model of neutrino oscillations and the existence of three neutrino flavors. However, some experiments have observed anomalous signals which could be explained by the presence of a fourth neutrino. This proposed “sterile” neutrino doesn’t have a charged lepton partner (and therefore doesn’t participate in weak interactions) but does mix with the other neutrino flavors.
The discovery of a new type of particle would be tremendously exciting, and neutrino experiments all over the world (including Daya Bay) have been checking their data for any sign of sterile neutrinos.
Neutrinos from reactors
Nuclear reactors are a powerful source of electron antineutrinos. To see why, take a look at this zoomed out version of the chart of the nuclides. The chart of the nuclides is a nuclear physicist’s version of the periodic table. For a chemist, Hydrogen-1 (a single proton), Hydrogen-2 (one proton and one neutron) and Hydrogen-3 (one proton and two neutrons) are essentially the same thing, because chemical bonds are electromagnetic and every hydrogen nucleus has the same electric charge. In the realm of nuclear physics, however, the number of neutrons is just as important as the number of protons. Thus, while the periodic table has a single box for each chemical element, the chart of the nuclides has a separate entry for every combination of protons and neutrons (“nuclide”) that has ever been observed in nature or created in a laboratory.
The black squares are stable nuclei. You can see that stability only occurs when the ratio of neutrons to protons is just right. Furthermore, unstable nuclides tend to decay in such a way that the daughter nuclide is closer to the line of stability than the parent.
Nuclear power plants generate electricity by harnessing the energy released by the fission of Uranium-235. Each U-235 nucleus contains 143 neutrons and 92 protons (n/p = 1.6). When U-235 undergoes fission, the resulting fragments also have n/p ~ 1.6, because the overall number of neutrons and protons is still the same. Thus, fission products tend to lie along the white dashed line in Figure 3, which falls above the line of stability. These nuclides have too many neutrons to be stable, and therefore undergo beta decay: . A typical power reactor emits 6 × 10^20 per second.
The Daya Bay experiment
The Daya Bay nuclear power complex is located on the southern coast of China, 55 km northeast of Hong Kong. With six reactor cores, it is one of the most powerful reactor complexes in the world — and therefore an excellent source of electron antineutrinos. The Daya Bay experiment consists of 8 identical antineutrino detectors in 3 underground halls. One experimental hall is located as close as possible to the Daya Bay nuclear power plant; the second is near the two Ling Ao power plants; the third is located 1.5 – 1.9 km away from all three pairs of reactors, a distance chosen to optimize Daya Bay’s sensitivity to the mixing angle .
The neutrino target at the heart of each detector is a cylindrical vessel filled with 20 tons of Gadolinium-doped liquid scintillator. The vast majority of pass through undetected, but occasionally one will undergo inverse beta decay in the target volume, interacting with a proton to produce a positron and a neutron: .
The positron and neutron create signals in the detector with a characteristic time relationship, as shown in Figure 6. The positron immediately deposits its energy in the scintillator and then annihilates with an electron. This all happens within a few nanoseconds and causes a prompt flash of scintillation light. The neutron, meanwhile, spends some tens of microseconds bouncing around (“thermalizing”) until it is slow enough to be captured by a Gadolinium nucleus. When this happens, the nucleus emits a cascade of gamma rays, which in turn interact with the scintillator and produce a second flash of light. This combination of prompt and delayed signals is used to identify interaction events.
Daya Bay’s search for sterile neutrinos
Daya Bay is a neutrino disappearance experiment. The electron antineutrinos emitted by the reactors can oscillate into muon or tau antineutrinos as they travel, but the detectors are only sensitive to , because the antineutrinos have enough energy to produce a positron but not the more massive or . Thus, Daya Bay observes neutrino oscillations by measuring fewer than would be expected otherwise.
Based on the number of detected at one of the Daya Bay experimental halls, the usual three-neutrino oscillation theory can predict the number that will be seen at the other two experimental halls (EH). You can see how this plays out in Figure 7. We are looking at the neutrino energy spectrum measured at EH2 and EH3, divided by the prediction computed from the EH1 data. The gray shaded regions mark the one-standard-deviation uncertainty bounds of the predictions. If the black data points deviated significantly from the shaded region, that would be a sign that the three-neutrino oscillation model is not complete, possibly due to the presence of sterile neutrinos. However, in this case, the black data points are statistically consistent with the prediction. In other words, Daya Bay sees no evidence for sterile neutrinos.
Does that mean sterile neutrinos don’t exist? Not necessarily. For one thing, the effect of a sterile neutrino on the Daya Bay results would depend on the sterile neutrino mass and mixing parameters. The blue and red dashed lines in Figure 7 show the sterile neutrino prediction for two specific choices of and ; these two examples look quite different from the three-neutrino prediction and can be ruled out because they don’t match the data. However, there are other parameter choices for which the presence of a sterile neutrino wouldn’t have a discernable effect on the Daya Bay measurements. Thus, Daya Bay can constrain the parameter space, but can’t rule out sterile neutrinos completely. However, as more and more experiments report “no sign of sterile neutrinos here,” it appears less and less likely that they exist.
Article: Search for magnetic monopoles with the MoEDAL prototype trapping detector in 8 TeV proton-proton collisions at the LHC Authors: The ATLAS Collaboration Reference: arXiv:1604.06645v4 [hep-ex]
Somewhere in a tiny corner of the massive LHC cavern, nestled next to the veteran LHCb detector, a new experiment is coming to life.
The Monopole & Exotics Detector at the LHC, nicknamed the MoEDAL experiment, recently published its first ever results on the search for magnetic monopoles and other highly ionizing new particles. The data collected for this result is from the 2012 run of the LHC, when the MoEDAL detector was still a prototype. But it’s still enough to achieve the best limit to date on the magnetic monopole mass.
Magnetic monopoles are a very appealing idea. From basic electromagnetism, we expect to swap electric and magnetic fields under duality without changing Maxwell’s equations. Furthermore, Dirac showed that a magnetic monopole is not inconsistent with quantum electrodynamics (although they do not appear natually.) The only problem is that in the history of scientific experimentation, we’ve never actually seen one. We know that if we break a magnet in half, we will get two new magnetics, each with its own North and South pole (see Figure 1).
This is proving to be a thorn in the side of many physicists. Finding a magnetic monopole would be great from a theoretical standpoint. Many Grand Unified Theories predict monopoles as a natural byproduct of symmetry breaking in the early universe. In fact, the theory of cosmological inflation so confidently predicts a monopole that its absence is known as the “monopole problem”. There have been occasional blips of evidence for monopoles in the past (such as a single event in a detector), but nothing has been reproducible to date.
Enter MoEDAL (Figure 2). It is the seventh addition to the LHC family, having been approved in 2010. If the monopole is a fundamental particle, it will be produced in proton-proton collisions. It is also expected to be very massive and long-lived. MoEDAL is designed to search for such a particle with a three-subdetector system.
The Nuclear Track Detector is composed of plastics that are damaged when a charged particle passes through them. The size and shape of the damage can then be observed with an optical microscope. Next is the TimePix Radiation Monitor system, a pixel detector which absorbs charge deposits induced by ionizing radiation. The newest addition is the Trapping Detector system, which is simply a large aluminum volume that will trap a monopole with its large nuclear magnetic moment.
The collaboration collected data using these distinct technologies in 2012, and studied the resulting materials and signals. The ultimate limit in the paper excludes spin-0 and spin-1/2 monopoles with masses between 100 GeV and 3500 GeV, and a magnetic charge > 0.5gD (the Dirac magnetic charge). See Figures 3 and 4 for the exclusion curves. It’s worth noting that this upper limit is larger than any fundamental particle we know of to date. So this is a pretty stringent result.
As for moving forward, we’ve only talked about monopoles here, but the physics programme for MoEDAL is vast. Since the detector technology is fairly broad-based, it is possible to find anything from SUSY to Universal Extra Dimensions to doubly charged particles. Furthermore, this paper is only published on LHC data from September to December of 2012, which is not a whole lot. In fact, we’ve collected over 25x that much data in this year’s run alone (although this detector was not in use this year.) More data means better statistics and more extensive limits, so this is definitely a measurement that will be greatly improved in future runs. A new version of the detector was installed in 2015, and we can expect to see new results within the next few years.
Article: Search for TeV-scale gravity signatures in high-mass final states with leptons and jets with the ATLAS detector at sqrt(s)=13 TeV Authors: The ATLAS Collaboration Reference: arXiv:1606.02265 [hep-ex]
What would gravity look like if we lived in a 6-dimensional space-time? Models of TeV-scale gravity theorize that the fundamental scale of gravity, MD, is much lower than what’s measured here in our normal, 4-dimensional space-time. If true, this could explain the large difference between the scale of electroweak interactions (order of 100 GeV) and gravity (order of 1016 GeV), an important open question in particle physics. There are several theoretical models to describe these extra dimensions, and they all predict interesting new signatures in the form of non-perturbative gravitational states. One of the coolest examples of such a state is microscopic black holes. Conveniently, this particular signature could be produced and measured at the LHC!
Sounds cool, but how do you actually look for microscopic black holes with a proton-proton collider? Because we don’t have a full theory of quantum gravity (yet), ATLAS researchers made predictions for the production cross-sections of these black holes using semi-classical approximations that are valid when the black hole mass is above MD. This production cross-section is also expected to dramatically larger when the energy scale of the interactions (pp collisions) surpasses MD. We can’t directly detect black holes with ATLAS, but many of the decay channels of these black holes include leptons in the final state, which IS something that can be measured at ATLAS! This particular ATLAS search looked for final states with at least 3 high transverse momentum (pt) jets, at least one of which must be a leptonic (electron or muon) jet (the others can be hadronic or leptonic). The sum of the transverse momenta, is used as a discriminating variable since the signal is expected to appear only at high pt.
This search used the full 3.2 fb-1 of 13 TeV data collected by ATLAS in 2015 to search for this signal above relevant Standard Model backgrounds (Z+jets, W+jets, and ttbar, all of which produce similar jet final states). The results are shown in Figure 1 (electron and muon channels are presented separately). The various backgrounds are shown in various colored histograms, the data in black points, and two microscopic black hole models in green and blue lines. There is a slight excess in the 3 TeV region in the electron channel, which corresponds to a p-value of only 1% when tested against the background only hypothesis. Unfortunately, this isn’t enough evidence to indicate new physics yet, but it’s an exciting result nonetheless! This analysis was also used to improve exclusion limits on individual extra-dimensional gravity models, as shown in Figure 2. All limits were much stronger than those set in Run 1.
So: no evidence of microscopic black holes or extra-dimensional gravity at the LHC yet, but there is a promising excess and Run 2 has only just begun. Since publication, ATLAS has collected another 10 fb-1 of sqrt(13) TeV data that has yet to be analyzed. These results could also be used to constrain other Beyond the Standard Model searches at the TeV scale that have similar high pt leptonic jet final states, which would give us more information about what can and can’t exist outside of the Standard Model. There is certainly more to be learned from this search!