Dark Photons in Light Places

Title: “Searching for dark photon dark matter in LIGO O1 data”

Author: Huai-Ke Guo, Keith Riles, Feng-Wei Yang, & Yue Zhao

Reference: https://www.nature.com/articles/s42005-019-0255-0

There is very little we know about dark matter save for its existence. Its mass(es), its interactions, even the proposition that it consists of particles at all is mostly up to the creativity of the theorist. For those who don’t turn to modified theories of gravity to explain the gravitational effects on galaxy rotation and clustering that suggest a massive concentration of unseen matter in the universe (among other compelling evidence), there are a few more widely accepted explanations for what dark matter might be. These include weakly-interacting massive particles (WIMPS), primordial black holes, or new particles altogether, such as axions or dark photons. 

In particle physics, this latter category is what’s known as the “hidden sector,” a hypothetical collection of quantum fields and their corresponding particles that are utilized in theorists’ toolboxes to help explain phenomena such as dark matter. In order to test the validity of the hidden sector, several experimental techniques have been concocted to narrow down the vast parameter space of possibilities, which generally consist of three strategies:

  1. Direct detection: Detector experiments look for low-energy recoils of dark matter particle collisions with nuclei, often involving emitted light or phonons. 
  2. Indirect detection: These searches focus on potential decay products of dark matter particles, which depends on the theory in question.
  3. Collider production: As the name implies, colliders seek to produce dark matter in order to study its properties. This is reliant on the other two methods for verification.

The first detection of gravitational waves from a black hole merger in 2015 ushered in a new era of physics, in which the cosmological range of theory-testing is no longer limited to the electromagnetic spectrum. Bringing LIGO (the Laser Interferometer Gravitational-Wave Observatory) to the table, proposals for the indirect detection of dark matter via gravitational waves began to spring up in the literature, with implications for primordial black hole detection or dark matter ensconced in neutron stars. Yet a new proposal, in a paper by Guo et. al., suggests that direct dark matter detection with gravitational waves may be possible, specifically in the case of dark photons. 

Dark photons are hidden sector particles in the ultralight regime of dark matter candidates. Theorized as the gauge boson of a U(1) gauge group, meaning the particle is a force-carrier akin to the photon of quantum electrodynamics, dark photons either do not couple or very weakly couple to Standard Model particles in various formulations. Unlike a regular photon, dark photons can acquire a mass via the Higgs mechanism. Since dark photons need to be non-relativistic in order to meet cosmological dark matter constraints, we can model them as a coherently oscillating background field: a plane wave with amplitude determined by dark matter energy density and oscillation frequency determined by mass. In the case that dark photons weakly interact with ordinary matter, this means an oscillating force is imparted. This sets LIGO up as a means of direct detection due to the mirror displacement dark photons could induce in LIGO detectors.

Figure 1: The experimental setup of the Advanced LIGO interferometer. We can see that light leaves the laser and is reflected between a few power recycling mirrors (PR), split by a beam splitter (BS), and bounced between input and end test masses (ITM and ETM). The entire system is mounted on seismically-isolated platforms to reduce noise as much as possible. Source: https://arxiv.org/pdf/1411.4547.pdf

LIGO consists of a Michelson interferometer, in which a laser shines upon a beam splitter which in turn creates two perpendicular beams. The light from each beam then hits a mirror, is reflected back, and the two beams combine, producing an interference pattern. In the actual LIGO detectors, the beams are reflected back some 280 times (down a 4 km arm length) and are split to be initially out of phase so that the photodiode detector should not detect any light in the absence of a gravitational wave. A key feature of gravitational waves is their polarization, which stretches spacetime in one direction and compresses it in the perpendicular direction in an alternating fashion. This means that when a gravitational wave passes through the detector, the effective length of one of the interferometer arms is reduced while the other is increased, and the photodiode will detect an interference pattern as a result. 

LIGO has been able to reach an incredible sensitivity of one part in 10^{23} in its detectors over a 100 Hz bandwidth, meaning that its instruments can detect mirror displacements up to 1/10,000th the size of a proton. Taking advantage of this number, Guo et. al. demonstrated that the differential strain (the ratio of the relative displacement of the mirrors to the interferometer’s arm length, or h = \Delta L/L) is also sensitive to ultralight dark matter via the modeling process described above. The acceleration induced by the dark photon dark matter on the LIGO mirrors is ultimately proportional to the dark electric field and charge-to-mass ratio of the mirrors themselves.

Once this signal is approximated, next comes the task of estimating the background. Since the coherence length is of order 10^9 m for a dark photon field oscillating at order 100 Hz, a distance much larger than the separation between the LIGO detectors at Hanford and Livingston (in Washington and Louisiana, respectively), the signals from dark photons at both detectors should be highly correlated. This has the effect of reducing the noise in the overall signal, since the noise in each of the detectors should be statistically independent. The signal-to-noise ratio can then be computed directly using discrete Fourier transforms from segments of data along the total observation time. However, this process of breaking up the data, known as “binning,” means that some signal power is lost and must be corrected for.

Figure 2: The end result of the Guo et. al. analysis of dark photon-induced mirror displacement in LIGO. Above we can see a plot of the coupling of dark photons to baryons as a function of the dark photon oscillation frequency. We can see that over further Advanced LIGO runs, up to O4-O5, these limits are expected to improve by several orders of magnitude. Source: https://www.nature.com/articles/s42005-019-0255-0

In applying this analysis to the strain data from the first run of Advanced LIGO, Guo et. al. generated a plot which sets new limits for the coupling of dark photons to baryons as a function of the dark photon oscillation frequency. There are a few key subtleties in this analysis, primarily that there are many potential dark photon models which rely on different gauge groups, yet this framework allows for similar analysis of other dark photon models. With plans for future iterations of gravitational wave detectors, further improved sensitivities, and many more data runs, there seems to be great potential to apply LIGO to direct dark matter detection. It’s exciting to see these instruments in action for discoveries that were not in mind when LIGO was first designed, and I’m looking forward to seeing what we can come up with next!

Learn More:

  1. An overview of gravitational waves and dark matter: https://www.symmetrymagazine.org/article/what-gravitational-waves-can-say-about-dark-matter
  2. A summary of dark photon experiments and results: https://physics.aps.org/articles/v7/115 
  3. Details on the hardware of Advanced LIGO: https://arxiv.org/pdf/1411.4547.pdf
  4. A similar analysis done by Pierce et. al.: https://journals.aps.org/prl/pdf/10.1103/PhysRevLett.121.061102

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.

A summary of searches the ATLAS collaboration has performed. The left columns show model being searched for, what experimental signature was looked at and how much data has been analyzed so far. The color bars show the regions that have been ruled out based on the null result of the search. As you can see, we have already covered a lot of territory.

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.

A histogram showing the invariant mass of photon pairs. The Higgs boson shows up as a bump at 125 GeV. Plot from here

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.

An illustration of the CWoLa method. A classifier trained to distinguish between two mixed samples of signal and background events learns can learn to classify signal versus background. Taken from here

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.

Demonstration of the bump hunt search. The shaded histogram is the amount of signal in the dataset. The different levels of blue points show the data remaining after applying tighter and tighter selection based on the neural network classifier score. The red line is the predicted amount of background events based on fitting the sideband regions. One can see that for the tightest selection (bottom set of points), the data forms a clear bump over the background estimate, indicating the presence of a new particle

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!

Read More:

  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”

Quark nuggets of wisdom

Article title: “Dark Quark Nuggets”

Authors: Yang Baia, Andrew J. Long, and Sida Lu

Reference: arXiv:1810.04360

Information, gold and chicken. What do they all have in common? They can all come in the form of nuggets. Naturally one would then be compelled to ask: “what about fundamental particles? Could they come in nugget form? Could that hold the key to dark matter?” Lucky for you this has become the topic of some ongoing research.

A ‘nugget’ in this context refers to large macroscopic ‘clumps’ of matter formed in the early universe that could possibly survive up until the present day to serve as a dark matter candidate. Much like nuggets of the edible variety, one must be careful to combine just the right ingredients in just the right way. In fact, there are generally three requirements to forming such an exotic state of matter:

  1. (At least) two different vacuum states separated by a potential ‘barrier’ where a phase transition occurs (known as a first-order phase transition).
  2. A charge which is conserved globally which can accumulate in a small part of space.
  3. An excess of matter over antimatter on the cosmological scale, or in other words, a large non-zero macroscopic number density of global charge.

Back in the 1980s, before much work was done in the field of lattice quantum chromodynamics (lQCD), Edward Witten put forward the idea that the Standard Model QCD sector could in fact accommodate such an exotic form of matter. Quite simply this would occur at the early phase of the universe when the quarks undergo color confinement to form hadrons. In particular Witten’s were realized as large macroscopic clumps of ‘quark matter’ with a very large concentration of baryon number, N_B > 10^{30}. However, with the advancement of lQCD techniques, the phase transition in which the quarks become confined looks more like a continuous ‘crossover’ (i.e. a second-order phase transition), making the idea in the Standard Model somewhat unfeasible.

Theorists, particularly those interested in dark matter, are not confined (for lack of a better term) to the strict details of the Standard Model and most often look to the formation of sometimes complicated ‘dark sectors’ invisible to us but readily able to provide the much needed dark matter candidate.

Dark QCD?

The problem of obtaining a first-order phase transition to form our quark nuggets need not be a problem if we consider a QCD-type theory that does not interact with the Standard Model particles. More specifically, we can consider a set of dark quarks, dark gluons with arbitrary characteristics like masses, couplings, numbers of flavors or numbers of colors (which of course are quite settled for the Standard Model QCD case). In fact, looking at the numbers of flavors and colors of dark QCD in Figure 1, we can see in the white unshaded region a number of models that can exist with a first-order phase transition, as required to form these dark quark nuggets.

Figure 1: The white unshaded region corresponds to dark QCD models which may permit a first-order phase transition and thus the existence of ‘dark quark nuggets’.

As with normal quarks, the distinction between the two phases actually refers to a process known as chiral symmetry breaking. When the temperature of the universe cools to this particular scale, color confinement of quarks occurs around the same time, such that no single-color quark can be observed on its own – only in colorless bound states.

Forming a nugget

As we have briefly mentioned so far, the dark nuggets are formed as the universe undergoes a ‘dark’ phase transition from a phase where the dark color is unconfined to a phase where it is confined. At some critical temperature, due to the nature of first-order phase transitions, bubbles of the new confined phase (full of dark hadrons) begin to nucleate out of the dark quark-gluon plasma. The growth of these bubbles are driven by a difference in pressure, characteristic of the fact that the unconfined and confined phase vacuums states are of different energy. With this emerging bubble wall, the almost massless particles from the dark plasma scatter from the wall containing heavy dark (anti)baryons and hence a large amount of dark baryon number accumulates in this phase. Eventually, as these bubbles merge and coalesce, we would expect local regions of remaining dark quark-gluon plasma, unconfined and stable from collapse due to the Fermi degeneracy pressure (see reference below for more on this). An illustration is shown in Figure 2. Calculations with varying energy scales of confinement estimate their masses are anywhere between 10^{-7} to 10^{23} grams with radii from 10^{-15} to 10^8 cm and so can truly be classed as macroscopic dark objects!

Figure 2: Dark Quark Nuggets are a phase of unconfined dark quark-gluon plasma kept stable by the balance between Fermi degeneracy pressure and vacuum pressure from the separation between the unconfined and confined phases.

How do we know they could be there? 

There are a number of ways to infer the existence of dark quark nuggets, but two of the main ones are: (i) as a dark matter candidate and (ii) through probes of the dark QCD model that provides them. Cosmologically, the latter can imply the existence of a dark form of radiation which ultimately can lead to effects on the Cosmic Microwave Background Radiation (CMB). In a similar vein, one recent avenue of study today is the production of a steady background of gravitational waves emerging from the existence of a first-order phase transition – one of the key requirements for dark quark nugget formation. More importantly, they can be probed through astrophysical means if they share some coupling (albeit small) with the Standard Model particles. The standard technique of direct detection with Earth-based experiments could be the way to go – but furthermore, there may be the possibility of cosmic ray production from collisions of multiple dark quark nuggets. Among these are a number of other observations over the massive range of nugget sizes and masses shown in Figure 3.

Figure 3: Range of dark quark nugget masses and sizes and their possible detection methods.

To conclude, note that in such a generic framework, a number of well-motivated theories may predict (or in fact have unavoidable) instances of quark nuggets that may serve as interesting dark matter candidates with a lot of fun phenomenology to play with. It is only up to the theorist’s imagination where to go from here!

References and further reading:

Discovering the Top Quark

This post is about the discovery of the most massive quark in the Standard Model, the Top quark. Below is a “discovery plot” [1] from the Collider Detector at Fermilab collaboration (CDF). Here is the original paper.

This plot confirms the existence of the Top quark. Let’s understand how.

For each proton collision that passes certain selection conditions, the horizontal axis shows the best estimate of the Top quark mass. These selection conditions encode the particle “fingerprint” of the Top quark. Out of all possible proton collisions events, we only want to look at ones that perhaps came from Top quark decays. This subgroup of events can inform us of a best guess at the mass of the Top quark. This is what is being plotted on the x axis.

On the vertical axis are the number of these events.

The dashed distribution is the number of these events originating from the Top quark if the Top quark exists and decays this way. This could very well not be the case.

The dotted distribution is the background for these events, events that did not come from Top quark decays.

The solid distribution is the measured data.

To claim a discovery, the background (dotted) plus the signal (dashed) should equal the measured data (solid). We can run simulations for different top quark masses to give us distributions of the signal until we find one that matches the data. The inset at the top right is showing that a Top quark of mass of 175GeV best reproduces the measured data.

Taking a step back from the technicalities, the Top quark is special because it is the heaviest of all the fundamental particles. In the Standard Model, particles acquire their mass by interacting with the Higgs. Particles with more mass interact more with the Higgs. The Top mass being so heavy is an indicator that any new physics involving the Higgs may be linked to the Top quark.

References / Further Reading

[1] – Observation of Top Quark Production in pp Collisions with the Collider Detector at Fermilab – This is the “discovery paper” announcing experimental evidence of the Top.

[2] – Observation of tt(bar)H Production – Who is to say that the Top and the Higgs even have significant interactions to lowest order? The CMS collaboration finds evidence that they do in fact interact at “tree-level.”

[2] – The Perfect Couple: Higgs and top quark spotted together – This article further describes the interconnection between the Higgs and the Top.