## LHCb’s Xmas Letdown : The R(K) Anomaly Fades Away

Just before the 2022 holiday season LHCb announced it was giving the particle physics community a highly anticipated holiday present : an updated measurement of the lepton flavor universality ratio R(K).  Unfortunately when the wrapping paper was removed and the measurement revealed,  the entire particle physics community let out a collective groan. It was not shiny new-physics-toy we had all hoped for, but another pair of standard-model-socks.

The particle physics community is by now very used to standard-model-socks, receiving hundreds of pairs each year from various experiments all over the world. But this time there had be reasons to hope for more. Previous measurements of R(K) from LHCb had been showing evidence of a violation one of the standard model’s predictions (lepton flavor universality), making this triumph of the standard model sting much worse than most.

R(K) is the ratio of how often a B-meson (a bound state of a b-quark) decays into final states with a kaon (a bound state of an s-quark) plus two electrons vs final states with a kaon plus two muons. In the standard model there is a (somewhat mysterious) principle called lepton flavor universality which means that muons are just heavier versions of electrons. This principle implies B-mesons decays should produce electrons and muons equally and R(K) should be one.

But previous measurements from LHCb had found R(K) to be less than one, with around 3σ of statistical evidence. Other LHCb measurements of B-mesons decays had also been showing similar hints of lepton flavor universality violation. This consistent pattern of deviations had not yet reached the significance required to claim a discovery. But it had led a good amount of physicists to become #cautiouslyexcited that there may be a new particle around, possibly interacting preferentially with muons and b-quarks, that was causing the deviation. Several hundred papers were written outlining possibilities of what particles could cause these deviations, checking whether their existence was constrained by other measurements, and suggesting additional measurements and experiments that could rule out or discover the various possibilities.

This had all led to a considerable amount of anticipation for these updated results from LHCb. They were slated to be their final word on the anomaly using their full dataset collected during LHC’s 2nd running period of 2016-2018. Unfortunately what LHCb had discovered in this latest analysis was that they had made a mistake in their previous measurements.

There were additional backgrounds in their electron signal region which had not been previously accounted for. These backgrounds came from decays of B-mesons into pions or kaons which can be mistakenly identified as electrons. Backgrounds from mis-identification are always difficult to model with simulation, and because they are also coming from decays of B-mesons they produce similar peaks in their data as the sought after signal. Both these factors combined to make it hard to spot they were missing. Without accounting for these backgrounds it made it seem like there was more electron signal being produced than expected, leading to R(K) being below one. In this latest measurement LHCb found a way to estimate these backgrounds using other parts of their data. Once they were accounted for, the measurements of R(K) no longer showed any deviations, all agreed with one within uncertainties.

It is important to mention here that data analysis in particle physics is hard. As we attempt to test the limits of the standard model we are often stretching the limits of our experimental capabilities and mistakes do happen. It is commendable that the LHCb collaboration was able to find this issue and correct the record for the rest of the community. Still, some may be a tad frustrated that the checks which were used to find these missing backgrounds were not done earlier given the high profile nature of these measurements (their previous result claimed ‘evidence’ of new physics and was published in Nature).

Though the R(K) anomaly has faded away, the related set of anomalies that were thought to be part of a coherent picture (including another leptonic branching ratio R(D) and an angular analysis of the same B meson decay in to muons) still remain for now. Though most of these additional anomalies involve significantly larger uncertainties on the Standard Model predictions than R(K) did, and are therefore less ‘clean’ indications of new physics.

Besides these ‘flavor anomalies’ other hints of new physics remain, including measurements of the muon’s magnetic moment, the measured mass of the W boson and others. Though certainly none of these are slam dunk, as they each causes for skepticism.

So as we begin 2023, with a great deal of fresh LHC data expected to be delivered, particle physicists once again begin our seemingly Sisyphean task : to find evidence physics beyond the standard model. We know its out there, but nature is under no obligation to make it easy for us.

Paper: Test of lepton universality in b→sℓ+ℓ− decays (arXiv link)

Authors: LHCb Collaboration

A related, still discrepant, flavor anomaly from LHCb

## The LHC is on turning on again! What does that mean?

Deep underground, on the border between Switzerland and France, the Large Hadron Collider (LHC) is starting back up again after a 4 year hiatus. Today, July 5th, the LHC had its first full energy collisions since 2018.  Whenever the LHC is running is exciting enough on its own, but this new run of data taking will also feature several upgrades to the LHC itself as well as the several different experiments that make use of its collisions. The physics world will be watching to see if the data from this new run confirms any of the interesting anomalies seen in previous datasets or reveals any other unexpected discoveries.

## New and Improved

During the multi-year shutdown the LHC itself has been upgraded. Noticably the energy of the colliding beams has been increased, from 13 TeV to 13.6 TeV. Besides breaking its own record for the highest energy collisions every produced, this 5% increase to the LHC’s energy will give a boost to searches looking for very rare high energy phenomena. The rate of collisions the LHC produces is also expected to be roughly 50% higher  previous maximum achieved in previous runs. At the end of this three year run it is expected that the experiments will have collected twice as much data as the previous two runs combined.

The experiments have also been busy upgrading their detectors to take full advantage of this new round of collisions.

The ALICE experiment had the most substantial upgrade. It features a new silicon inner tracker, an upgraded time projection chamber, a new forward muon detector, a new triggering system and an improved data processing system. These upgrades will help in its study of exotic phase of matter called the quark gluon plasma, a hot dense soup of nuclear material present in the early universe.

ATLAS and CMS, the two ‘general purpose’ experiments at the LHC, had a few upgrades as well. ATLAS replaced their ‘small wheel’ detector used to measure the momentum of muons. CMS replaced the inner most part its inner tracker, and installed a new GEM detector to measure muons close to the beamline. Both experiments also upgraded their software and data collection systems (triggers) in order to be more sensitive to the signatures of potential exotic particles that may have been missed in previous runs.

The LHCb experiment, which specializes in studying the properties of the bottom quark, also had major upgrades during the shutdown. LHCb installed a new Vertex Locator closer to the beam line and upgraded their tracking and particle identification system. It also fully revamped its trigger system to run entirely on GPU’s. These upgrades should allow them to collect 5 times the amount of data over the next two runs as they did over the first two.

Run 3 will also feature a new smaller scale experiment, FASER, which will study neutrinos produced in the LHC and search for long-lived new particles

## What will we learn?

One of the main goals in particle physics now is direct experimental evidence of a phenomena unexplained by the Standard Model. While very successful in many respects, the Standard Model leaves several mysteries unexplained such as the nature of dark matter, the imbalance of matter over anti-matter, and the origin of neutrino’s mass. All of these are questions many hope that the LHC can help answer.

Much of the excitement for Run-3 of the LHC will be on whether the additional data can confirm some of the deviations from the Standard Model which have been seen in previous runs.

One very hot topic in particle physics right now are a series of ‘flavor anomalies‘ seen by the LHCb experiment in previous LHC runs. These anomalies are deviations from the Standard Model predictions of how often certain rare decays of the b quarks should occur. With their dataset so far, LHCb has not yet had enough data to pass the high statistical threshold required in particle physics to claim a discovery. But if these anomalies are real, Run-3 should provide enough data to claim a discovery.

There are also a decent number ‘excesses’, potential signals of new particles being produced in LHC collisions, that have been seen by the ATLAS and CMS collaborations. The statistical significance of these excesses are all still quite low, and many such excesses have gone away with more data. But if one or more of these excesses was confirmed in the Run-3 dataset it would be a massive discovery.

While all of these anomalies are gamble, this new dataset will also certainly be used to measure various known entities with better precision, improving our understanding of nature no matter what. Our understanding of the Higgs boson, the top quark, rare decays of the bottom quark, rare standard model processes, the dynamics of the quark gluon plasma and many other areas will no doubt improve from this additional data.

In addition to these ‘known’ anomalies and measurements, whenever an experiment starts up again there is also the possibility of something entirely unexpected showing up. Perhaps one of the upgrades performed will allow the detection of something entirely new, unseen in previous runs. Perhaps FASER will see signals of long-lived particles missed by the other experiments. Or perhaps the data from the main experiments will be analyzed in a new way, revealing evidence of a new particle which had been missed up until now.

No matter what happens, the world of particle physics is a more exciting place when the LHC is running. So lets all cheers to that!

CERN Run-3 Press Event / Livestream Recording “Join us for the first collisions for physics at 13.6 TeV!

Symmetry Magazine “What’s new for LHC Run 3?

CERN Courier “New data strengthens RK flavour anomaly

## How to find invisible particles in a collider

You might have heard that one of the big things we are looking for in collider experiments are ever elusive dark matter particles. But given that dark matter particles are expected to interact very rarely with regular matter, how would you know if you happened to make some in a collision? The so called ‘direct detection’ experiments have to operate giant multi-ton detectors in extremely low-background environments in order to be sensitive to an occasional dark matter interaction. In the noisy environment of a particle collider like the LHC, in which collisions producing sprays of particles happen every 25 nanoseconds, the extremely rare interaction of the dark matter with our detector is likely to be missed. But instead of finding dark matter by seeing it in our detector, we can instead find it by not seeing it. That may sound paradoxical, but its how most collider based searches for dark matter work.

The trick is based on every physicists favorite principle: the conservation of energy and momentum. We know that energy and momentum will be conserved in a collision, so if we know the initial momentum of the incoming particles, and measure everything that comes out, then any invisible particles produced will show up as an imbalance between the two. In a proton-proton collider like the LHC we don’t know the initial momentum of the particles along the beam axis, but we do that they were traveling along that axis. That means that the net momentum in the direction away from the beam axis (the ‘transverse’ direction) should be zero. So if we see a momentum imbalance going away from the beam axis, we know that there is some ‘invisible’ particle traveling in the opposite direction.

We normally refer to the amount of transverse momentum imbalance in an event as its ‘missing momentum’. Any collisions in which an invisible particle was produced will have missing momentum as tell-tale sign. But while it is a very interesting signature, missing momentum can actually be very difficult to measure. That’s because in order to tell if there is anything missing, you have to accurately measure the momentum of every particle in the collision. Our detectors aren’t perfect, any particles we miss, or mis-measure the momentum of, will show up as a ‘fake’ missing energy signature.

Even if you can measure the missing energy well, dark matter particles are not the only ones invisible to our detector. Neutrinos are notoriously difficult to detect and will not get picked up by our detectors, producing a ‘missing energy’ signature. This means that any search for new invisible particles, like dark matter, has to understand the background of neutrino production (often from the decay of a Z or W boson) very well. No one ever said finding the invisible would be easy!

However particle physicists have been studying these processes for a long time so we have gotten pretty good at measuring missing energy in our events and modeling the standard model backgrounds. Missing energy is a key tool that we use to search for dark matter, supersymmetry and other physics beyond the standard model.

What happens when energy goes missing?” ATLAS blog post by Julia Gonski

How to look for supersymmetry at the LHC“, blog post by Matt Strassler

“Performance of missing transverse momentum reconstruction with the ATLAS detector using proton-proton collisions at √s = 13 TeV” Technical Paper by the ATLAS Collaboration

“Search for new physics in final states with an energetic jet or a hadronically decaying W or Z boson and transverse momentum imbalance at √s= 13 TeV” Search for dark matter by the CMS Collaboration

## Measuring the Tau’s g-2 Too

Title : New physics and tau g2 using LHC heavy ion collisions

Authors: Lydia Beresford and Jesse Liu

Reference: https://arxiv.org/abs/1908.05180

Since April, particle physics has been going crazy with excitement over the recent announcement of the muon g-2 measurement which may be our first laboratory hint of physics beyond the Standard Model. The paper with the new measurement has racked up over 100 citations in the last month. Most of these papers are theorists proposing various models to try an explain the (controversial) discrepancy between the measured value of the muon’s magnetic moment and the Standard Model prediction. The sheer number of papers shows there are many many models that can explain the anomaly. So if the discrepancy is real,  we are going to need new measurements to whittle down the possibilities.

Given that the current deviation is in the magnetic moment of the muon, one very natural place to look next would be the magnetic moment of the tau lepton. The tau, like the muon, is a heavier cousin of the electron. It is the heaviest lepton, coming in at 1.78 GeV, around 17 times heavier than the muon. In many models of new physics that explain the muon anomaly the shift in the magnetic moment of a lepton is proportional to the mass of the lepton squared. This would explain why we are a seeing a discrepancy in the muon’s magnetic moment and not the electron (though there is a actually currently a small hint of a deviation for the electron too). This means the tau should be 280 times more sensitive than the muon to the new particles in these models. The trouble is that the tau has a much shorter lifetime than the muon, decaying away in just 10-13 seconds. This means that the techniques used to measure the muons magnetic moment, based on magnetic storage rings, won’t work for taus.

Thats where this new paper comes in. It details a new technique to try and measure the tau’s magnetic moment using heavy ion collisions at the LHC. The technique is based on light-light collisions (previously covered on Particle Bites) where two nuclei emit photons that then interact to produce new particles. Though in classical electromagnetism light doesn’t interact with itself (the beam from two spotlights pass right through each other) at very high energies each photon can split into new particles, like a pair of tau leptons and then those particles can interact. Though the LHC normally collides protons, it also has runs colliding heavier nuclei like lead as well. Lead nuclei have more charge than protons so they emit high energy photons more often than protons and lead to more light-light collisions than protons.

Light-light collisions which produce tau leptons provide a nice environment to study the interaction of the tau with the photon. A particles magnetic properties are determined by its interaction with photons so by studying these collisions you can measure the tau’s magnetic moment.

However studying this process is be easier said than done. These light-light collisions are “Ultra Peripheral” because the lead nuclei are not colliding head on, and so the taus produced generally don’t have a large amount of momentum away from the beamline. This can make them hard to reconstruct in detectors which have been designed to measure particles from head on collisions which typically have much more momentum. Taus can decay in several different ways, but always produce at least 1 neutrino which will not be detected by the LHC experiments further reducing the amount of detectable momentum and meaning some information about the collision will lost.

However one nice thing about these events is that they should be quite clean in the detector. Because the lead nuclei remain intact after emitting the photon, the taus won’t come along with the bunch of additional particles you often get in head on collisions. The level of background processes that could mimic this signal also seems to be relatively minimal. So if the experimental collaborations spend some effort in trying to optimize their reconstruction of low momentum taus, it seems very possible to perform a measurement like this in the near future at the LHC.

The authors of this paper estimate that such a measurement with a the currently available amount of lead-lead collision data would already supersede the previous best measurement of the taus anomalous magnetic moment and further improvements could go much farther. Though the measurement of the tau’s magnetic moment would still be far less precise than that of the muon and electron, it could still reveal deviations from the Standard Model in realistic models of new physics. So given the recent discrepancy with the muon, the tau will be an exciting place to look next!

An Anomalous Anomaly: The New Fermilab Muon g-2 Results

When light and light collide

Another Intriguing Hint of New Physics Involving Leptons

## A symphony of data

Article title: “MUSiC: a model unspecific search for new physics in
proton-proton collisions at \sqrt{s} = 13 TeV”

Authors: The CMS Collaboration

Reference: https://arxiv.org/abs/2010.02984

First of all, let us take care of the spoilers: no new particles or phenomena have been found… Having taken this concern away, let us focus on the important concept behind MUSiC.

ATLAS and CMS, the two largest experiments using collisions at the LHC, are known as “general purpose experiments” for a good reason. They were built to look at a wide variety of physical processes and, up to now, each has checked dozens of proposed theoretical extensions of the Standard Model, in addition to checking the Model itself. However, in almost all cases their searches rely on definite theory predictions and focus on very specific combinations of particles and their kinematic properties. In this way, the experiments may still be far from utilizing their full potential. But now an algorithm named MUSiC is here to help.

MUSiC takes all events recorded by CMS that comprise of clean-cut particles and compares them against the expectations from the Standard Model, untethering itself from narrow definitions for the search conditions.

We should clarify here that an “event” is the result of an individual proton-proton collision (among the many happening each time the proton bunches cross), consisting of a bouquet of particles. First of all, MUSiC needs to work with events with particles that are well-recognized by the experiment’s detectors, to cut down on uncertainty. It must also use particles that are well-modeled, because it will rely on the comparison of data to simulation and, so, wants to be sure about the accuracy of the latter.

All this boils down to working with events with combinations of specific, but several, particles: electrons, muons, photons, hadronic jets from light-flavour (=up, down, strange) quarks or gluons and from bottom quarks, and deficits in the total transverse momentum (typically the signature of the uncatchable neutrinos or perhaps of unknown exotic particles). And to make things even more clean-cut, it keeps only events that include either an electron or a muon, both being well-understood characters.

These particles’ combinations result in hundreds of different “final states” caught by the detectors. However, they all correspond to only a dozen combos of particles created in the collisions according to the Standard Model, before some of them decay to lighter ones. For them, we know and simulate pretty well what we expect the experiment to measure.

MUSiC proceeded by comparing three kinematic quantities of these final states, as measured by CMS during the year 2016, to their simulated values. The three quantities of interest are the combined mass, combined transverse momentum and combined missing transverse momentum. It’s in their distributions that new particles would most probably show up, regardless of which theoretical model they follow. The range of values covered is pretty wide. All in all, the method extends the kinematic reach of usual searches, as it also does with the collection of final states.

So the kinematic distributions are checked against the simulated expectations in an automatized way, with MUSiC looking for every physicist’s dream: deviations. Any deviation from the simulation, meaning either fewer or more recorded events, is quantified by getting a probability value. This probability is calculated by also taking into account the much dreaded “look elsewhere effect”. (Which comes from the fact that, statistically, in a large number of distributions a random fluctuation that will mimic a genuine deviation is bound to appear sooner or later.)

When all’s said and done the collection of probabilities is overviewed. The MUSiC protocol says that any significant deviation will be scrutinized with more traditional methods – only that this need never actually arose in the 2016 data: all the data played along with the Standard Model, in all 1,069 examined final states and their kinematic ranges.

For the record, the largest deviation was spotted in the final state comprising three electrons, two generic hadronic jets and one jet coming from a bottom quark. Seven events were counted whereas the simulation gave 2.7±1.8 events (mostly coming from the production of a top plus an anti-top quark plus an intermediate vector boson from the collision; the fractional values are due to extrapolating to the amount of collected data). This excess was not seen in other related final states, “related” in that they also either include the same particles or have one less. Everything pointed to a fluctuation and the case was closed.

However, the goal of MUSiC was not strictly to find something new, but rather to demonstrate a method for model un-specific searches with collisions data. The mission seems to be accomplished, with CMS becoming even more general-purpose.

Another generic search method in ATLAS: Going Rogue: The Search for Anything (and Everything) with ATLAS

And a take with machine learning: Letting the Machines Seach for New Physics

Fancy checking a good old model-specific search? Uncovering a Higgs Hiding Behind Backgrounds

## Machine Learning The LHC ABC’s

Article Title: ABCDisCo: Automating the ABCD Method with Machine Learning

Authors: Gregor Kasieczka, Benjamin Nachman, Matthew D. Schwartz, David Shih

Reference: arxiv:2007.14400

When LHC experiments try to look for the signatures of new particles in their data they always apply a series of selection criteria to the recorded collisions. The selections pick out events that look similar to the sought after signal. Often they then compare the observed number of events passing these criteria to the number they would expect to be there from ‘background’ processes. If they see many more events in real data than the predicted background that is evidence of the sought after signal. Crucial to whole endeavor is being able to accurately estimate the number of events background processes would produce. Underestimate it and you may incorrectly claim evidence of a signal, overestimate it and you may miss the chance to find a highly sought after signal.

However it is not always so easy to estimate the expected number of background events. While LHC experiments do have high quality simulations of the Standard Model processes that produce these backgrounds they aren’t perfect. Particularly processes involving the strong force (aka Quantum Chromodynamics, QCD) are very difficult to simulate, and refining these simulations is an active area of research. Because of these deficiencies we don’t always trust background estimates based solely on these simulations, especially when applying very specific selection criteria.

Therefore experiments often employ ‘data-driven’ methods where they estimate the amount background events by using control regions in the data. One of the most widely used techniques is called the ABCD method.

The ABCD method can applied if the selection of signal-like events involves two independent variables f and g. If one defines the ‘signal region’, A,  (the part of the data in which we are looking for a signal) as having f  and g each greater than some amount, then one can use the neighboring regions B, C, and D to estimate the amount of background in region A. If the number of signal events outside region A is small, the number of background events in region A can be estimated as N_A = N_B * (N_C/N_D).

In modern analyses often one of these selection requirements involves the score of a neural network trained to identify the sought after signal. Because neural networks are powerful learners one often has to be careful that they don’t accidentally learn about the other variable that will be used in the ABCD method, such as the mass of the signal particle. If two variables become correlated, a background estimate with the ABCD method will not be possible. This often means augmenting the neural network either during training or after the fact so that it is intentionally ‘de-correlated’ with respect to the other variable. While there are several known techniques to do this, it is still a tricky process and often good background estimates come with a trade off of reduced classification performance.

In this latest work the authors devise a way to have the neural networks help with the background estimate rather than hindering it. The idea is rather than training a single network to classify signal-like events, they simultaneously train two networks both trying to identify the signal. But during this training they use a groovy technique called ‘DisCo’ (short for Distance Correlation) to ensure that these two networks output is independent from each other. This forces the networks to learn to use independent information to identify the signal. This then allows these networks to be used in an ABCD background estimate quite easily.

The authors try out this new technique, dubbed ‘Double DisCo’, on several examples. They demonstrate they are able to have quality background estimates using the ABCD method while achieving great classification performance. They show that this method improves upon the previous state of the art technique of decorrelating a single network from a fixed variable like mass and using cuts on the mass and classifier to define the ABCD regions (called ‘Single Disco’ here).

While there have been many papers over the last few years about applying neural networks to classification tasks in high energy physics, not many have thought about how to use them to improve background estimates as well. Because of their importance, background estimates are often the most time consuming part of a search for new physics. So this technique is both interesting and immediately practical to searches done with LHC data. Hopefully it will be put to use in the near future!

Quanta Magazine Article “How Artificial Intelligence Can Supercharge the Search for New Particles

Recent ATLAS Summary on New Machine Learning Techniques “Machine learning qualitatively changes the search for new particles

CERN Tutorial on “Background Estimation with the ABCD Method

Summary of Paper of Previous Decorrelation Techniques used in ATLAS “Performance of mass-decorrelated jet substructure observables for hadronic two-body decay tagging in ATLAS

## A shortcut to truth

Article title: “Automated detector simulation and reconstruction
parametrization using machine learning”

Authors: D. Benjamin, S.V. Chekanov, W. Hopkins, Y. Li, J.R. Love

The simulation of particle collisions at the LHC is a pharaonic task. The messy chromodynamics of protons must be modeled; the statistics of the collision products must reflect the Standard Model; each particle has to travel through the detectors and interact with all the elements in its path. Its presence will eventually be reduced to electronic measurements, which, after all, is all we know about it.

The work of the simulation ends somewhere here, and that of the reconstruction starts; namely to go from electronic signals to particles. Reconstruction is a process common to simulation and to the real world. Starting from the tangle of statistical and detector effects that the actual measurements include, the goal is to divine the properties of the initial collision products.

Now, researchers at the Argonne National Laboratory looked into going from the simulated particles as produced in the collisions (aka “truth objects”) directly to the reconstructed ones (aka “reco objects”): bypassing the steps of the detailed interaction with the detectors and of the reconstruction algorithm could make the studies that use simulations much more speedy and efficient.

The team used a neural network which it trained on simulations of the full set. The goal was to have the network learn to produce the properties of the reco objects when given only the truth objects. The process succeeded in producing the transverse momenta of hadronic jets, and looks suitable for any kind of particle and for other kinematic quantities.

More specifically, the researchers began with two million simulated jet events, fully passed through the ATLAS experiment and the reconstruction algorithm. For each of them, the network took the kinematic properties of the truth jet as input and was trained to achieve the reconstructed transverse momentum.

The network was taught to perform multi-categorization: its output didn’t consist of a single node giving the momentum value, but of 400 nodes, each corresponding to a different range of values. The output of each node was the probability for that particular range. In other words, the result was a probability density function for the reconstructed momentum of a given jet.

The final step was to select the momentum randomly from this distribution. For half a million of test jets, all this resulted in good agreement with the actual reconstructed momenta, specifically within 5% for values above 20 GeV. In addition, it seems that the training was sensitive to the effects of quantities other than the target one (e.g. the effects of the position in the detector), as the neural network was able to pick up on the dependencies between the input variables. Also, hadronic jets are complicated animals, so it is expected that the method will work on other objects just as well.

All in all, this work showed the perspective for neural networks to imitate successfully the effects of the detector and the reconstruction. Simulations in large experiments typically take up loads of time and resources due to their size, intricacy and frequent need for updates in the hardware conditions. Such a shortcut, needing only small numbers of fully processed events, would speed up studies such as optimization of the reconstruction and detector upgrades.

Intro to neural networks: https://physicsworld.com/a/neural-networks-explained/

## LHCb’s Flavor Mystery Deepens

Title: Measurement of CP -averaged observables in the B0→ K∗0µ+µ− decay

Authors: LHCb Collaboration

Refference: https://arxiv.org/abs/2003.04831

In the Standard Model, matter is organized in 3 generations; 3 copies of the same family of particles but with sequentially heavier masses. Though the Standard Model can successfully describe this structure, it offers no insight into why nature should be this way. Many believe that a more fundamental theory of nature would better explain where this structure comes from. A natural way to look for clues to this deeper origin is to check whether these different ‘flavors’ of particles really behave in exactly the same ways, or if there are subtle differences that may hint at their origin.

The LHCb experiment is designed to probe these types of questions. And in recent years, they have seen a series of anomalies, tensions between data and Standard Model predictions, that may be indicating the presence of new particles which talk to the different generations. In the Standard Model, the different generations can only interact with each other through the W boson, which means that quarks with the same charge can only interact through more complicated processes like those described by ‘penguin diagrams’.

These interactions typically have quite small rates in the Standard Model, meaning that the rate of these processes can be quite sensitive to new particles, even if they are very heavy or interact very weakly with the SM ones. This means that studying these sort of flavor decays is a promising avenue to search for new physics.

In a press conference last month, LHCb unveiled a new measurement of the angular distribution of the rare B0→K*0μ+μ– decay. The interesting part of this process involves a b → s transition (a bottom quark decaying into a strange quark), where number of anomalies have been seen in recent years.

Rather just measuring the total rate of this decay, this analysis focuses on measuring the angular distribution of the decay products. They also perform this mesaurement in different bins of ‘q^2’, the dimuon pair’s invariant mass. These choices allow the measurement to be less sensitive to uncertainties in the Standard Model prediction due to difficult to compute hadronic effects. This also allows the possibility of better characterizing the nature of whatever particle may be causing a deviation.

The kinematics of decay are fully described by 3 angles between the final state particles and q^2. Based on knowing the spins and polarizations of each of the particles, they can fully describe the angular distributions in terms of 8 parameters. They also have to account for the angular distribution of background events, and distortions of the true angular distribution that are caused by the detector. Once all such effects are accounted for, they are able to fit the full angular distribution in each q^2 bin to extract the angular coefficients in that bin.

This measurement is an update to their 2015 result, now with twice as much data. The previous result saw an intriguing tension with the SM at the level of roughly 3 standard deviations. The new result agrees well with the previous one, and mildly increases the tension to the level of 3.4 standard deviations.

This latest result is even more interesting given that LHCb has seen an anomaly in another measurement (the R_k anomaly) involving the same b → s transition. This had led some to speculate that both effects could be caused by a single new particle. The most popular idea is a so-called ‘leptoquark’ that only interacts with some of the flavors.

LHCb is already hard at work on updating this measurement with more recent data from 2017 and 2018, which should once again double the number of events. Updates to the R_k measurement with new data are also hotly anticipated. The Belle II experiment has also recent started taking data and should be able to perform similar measurements. So we will have to wait and see if this anomaly is just a statistical fluke, or our first window into physics beyond the Standard Model!

Symmetry Magazine “The mystery of particle generations”

Cern Courier “Anomalies persist in flavour-changing B decays”

Lecture Notes “Introduction to Flavor Physcis”

## Letting the Machines Search for New Physics

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!

1. Quanta Magazine Article “How Artificial Intelligence Can Supercharge the Search for New Particles”
2. Blog Post on the CWoLa Method “Training Collider Classifiers on Real Data”
3. Particle Bites Post “Going Rogue: The Search for Anything (and Everything) with ATLAS”
4. Blog Post on applying ML to top quark decays “What does Bidirectional LSTM Neural Networks has to do with Top Quarks?”
5. Extended Version of Original Paper “Extending the Bump Hunt with Machine Learning”

## LIGO and Gravitational Waves: A Hep-ex perspective

The exciting Twitter rumors have been confirmed! On Thursday, LIGO finally announced the first direct observation of gravitational waves, a prediction 100 years in the making. The media storm has been insane, with physicists referring to the discovery as “more significant than the discovery of the Higgs boson… the biggest scientific breakthrough of the century.” Watching Thursday’s press conference from CERN, it was hard not to make comparisons between the discovery of the Higgs and LIGO’s announcement.

Long standing Searches for well known phenomena

The Higgs boson was billed as the last piece of the Standard Model puzzle. The existence of the Higgs was predicted in the 1960s in order to explain the mass of vector bosons of the Standard Model, and avoid non-unitary amplitudes in W boson scattering. Even if the Higgs didn’t exist, particle physicists expected new physics to come into play at the TeV Scale, and experiments at the LHC were designed to find it.

Similarly, gravitational waves were the last untested fundamental prediction of General Relativity. At first, physicists remained skeptical of the existence of gravitational waves, but the search began in earnest with Joseph Webber in the 1950s (Forbes). Indirect evidence of gravitational waves was demonstrated a few decades later. A binary system consisting of a pulsar and neutron star was observed to release energy over time, presumably in the form of gravitational waves. Using Webber’s method for inspiration, LIGO developed two detectors of unprecedented precision in order to finally make direct observation.

Unlike the Higgs, General Relativity makes clear predictions about the properties of gravitational waves. Waves should travel at the speed of light, have two polarizations, and interact weakly with matter. Scientists at LIGO were even searching for a very particular signal, described as a characteristic “chirp”. With the upgrade to the LIGO detectors, physicists were certain they’d be capable of observing gravitational waves. The only outstanding question was how often these observations would happen.

The search for the Higgs involved more uncertainties. The one parameter essential for describing the Higgs, its mass, is not predicted by the Standard Model. While previous collider experiments at LEP and Fermilab were able to set limits on the Higgs mass, the observed properties of the Higgs were ultimately unknown before the discovery. No one knew whether or not the Higgs would be a Standard Model Higgs, or part of a more complicated theory like Supersymmetry or technicolor.

Monumental scientific endeavors

Answering the most difficult questions posed by the universe isn’t easy, or cheap. In terms of cost, both LIGO and the LHC represent billion dollar investments. Including the most recent upgrade, LIGO cost a total $1.1 billion, and when it was originally approved in 1992, “it represented the biggest investment the NSF had ever made” according to France Córdova, NSF director. The discovery of the Higgs was estimated by Forbes to cost a total of$13 billion, a hefty price to be paid by CERN’s member and observer states. Even the electricity bill costs more than \$200 million per year.

The large investment is necessitated by the sheer monstrosity of the experiments. LIGO consists of two identical detectors roughly 4 km long, built 3000 km apart. Because of it’s large size, LIGO is capable of measuring ripples in space 10000 times smaller than an atomic nucleus, the smallest scale ever measured by scientists (LIGO Fact Page). The size of the LIGO vacuum tubes is only surpassed by those at the LHC. At 27 km in circumference, the LHC is the single largest machine in the world, and the most powerful particle accelerator to date. It only took a handful of people to predict the existence of gravitational waves and the Higgs, but it took thousands of physicists and engineers to find them.

Life after Discovery

Even the language surrounding both announcements is strikingly similar. Rumors were circulating for months before the official press conferences, and the expectations from each respective community were very high. Both discoveries have been touted as the discoveries of the century, with many experts claiming that results would usher in a “new era” of particle physics or observational astronomy.

With a few years of hindsight, it is clear that the “new era” of particle physics has begun. Before Run I of the LHC, particle physicists knew they needed to search for the Higgs. Now that the Higgs has been discovered, there is much more uncertainty surrounding the field. The list of questions to try and answer is enormous. Physicists want to understand the source of the Dark Matter that makes up roughly 25% of the universe, from where neutrinos derive their mass, and how to quantize gravity. There are several ad hoc features of the Standard Model that merit additional explanation, and physicists are still searching for evidence of supersymmetry and grand unified theories. While the to-do list is long, and well understood, how to solve these problems is not. Measuring the properties of the Higgs does allow particle physicists to set limits on beyond the Standard Model Physics, but it’s unclear at which scale new physics will come into play, and there’s no real consensus about which experiments deserve the most support. For some in the field, this uncertainty can result in a great deal of anxiety and skepticism about the future. For others, the long to-do list is an absolutely thrilling call to action.

With regards to the LIGO experiment, the future is much more clear. LIGO has only published one event from 16 days of data taking. There is much more data already in the pipeline, and more interferometers like VIRGO and (e)LISA, planning to go online in the near future. Now that gravitational waves have been proven to exist, they can be used to observe the universe in a whole new way. The first event already contains an interesting surprise. LIGO has observed two inspriraling black holes of 36 and 29 solar masses, merging into a final black hole of 62 solar masses. The data thus confirmed the existence of heavy stellar black holes, with masses more than 25 times greater than the sun, and that binary black hole systems form in nature (Atrophysical Journal). When VIRGO comes online, it will be possible to triangulate the source of these gravitational waves as well. LIGO’s job is to watch, and see what other secrets the universe has in store.