Illuminating Light Dark Matter with Fixed-Target Experiments
This thesis advances the theoretical and statistical toolkit for searching for light dark matter (MeV–GeV) at fixed-target experiments like LDMX and SHiP by developing comprehensive frameworks for model building, likelihood-based inference, global fits, and improved signal simulations that incorporate advanced nuclear modeling.
Original paper licensed under CC BY 4.0 (http://creativecommons.org/licenses/by/4.0/). This is an AI-generated explanation of the paper below. It is not written or endorsed by the authors. For technical accuracy, refer to the original paper. Read full disclaimer
For decades, astronomers have known that the visible stars and galaxies in the universe are only a small fraction of what is actually there. The rest is an invisible substance called dark matter, which reveals itself only through its gravity, holding galaxies together and bending the path of light. While we can map where this invisible mass is, we have no idea what it is made of. For a long time, scientists looked for heavy, slow-moving particles, but the most sensitive detectors have found nothing. This has led researchers to consider a different possibility: that dark matter might be made of much lighter particles, with masses ranging from a few million electron-volts (MeV) to a few billion electron-volts (GeV). If these light particles exist, they would be too faint to knock heavy atomic nuclei around, which is how traditional detectors work. Instead, they would interact with the tiny electrons orbiting atoms, or be created in high-energy collisions inside particle accelerators.
To find these elusive particles, a researcher has developed a new way to look for them using a specific type of experiment called a fixed-target search. In these experiments, a beam of high-speed electrons is fired at a solid block of metal, like tungsten. When the electrons hit the metal, they can create new particles. If dark matter exists and is light enough, it could be produced in these collisions. Because dark matter does not interact with light or ordinary matter, it would fly right through the detectors without leaving a trace. The trick is to measure the energy and momentum of the electron that bounces off the target. If the electron loses more energy than expected, or bounces off at an angle that doesn't match standard physics, it suggests that something invisible, like dark matter, was created and carried that energy away. This missing energy is the signature scientists are hunting for.
The work presented in this thesis focuses on refining the tools needed to find these light particles, specifically for an upcoming experiment called the Light Dark Matter eXperiment, or LDMX. The researcher built a sophisticated statistical framework to analyze the data from these collisions. Instead of just counting how many times a signal appears, their new method looks at the detailed shape of the data, examining both the energy of the bouncing electron and the angle at which it flies. By comparing the actual data against what standard physics predicts, they can determine if a signal is real or just a random fluctuation. They tested this framework to see how well LDMX could distinguish between different theories of dark matter, finding that the experiment is powerful enough to not only detect a signal but also to tell the difference between various types of dark matter particles.
Beyond just looking for the simplest version of dark matter, the researcher expanded their search to include more complex theories. They explored scenarios where dark matter is a vector particle, similar to a photon but with mass, and investigated how these particles might interact through higher-order electromagnetic effects. They also looked at how dark matter could be produced in other types of experiments that use proton beams instead of electron beams, such as the proposed SHiP experiment. In these simulations, they found that new production methods, like the decay of heavy particles called vector mesons, could significantly increase the chances of finding dark matter in certain mass ranges. This work helps define the "thermal target," a specific range of properties where dark matter would naturally exist if it was created in the early universe, giving future experiments a clear goal to aim for.
A crucial part of this research involved improving how scientists model the target material itself. In previous calculations, the metal target was often treated as a simple, uniform object. However, the researcher used advanced nuclear physics methods to model the target as a complex system of protons and neutrons interacting with each other. They found that this detailed view of the atomic nucleus changes the predictions for how often dark matter is produced, especially for heavier dark matter particles. By incorporating these realistic nuclear structures into their simulations, they showed that the expected signal could be stronger than previously thought. This means that future experiments might be even more sensitive than earlier estimates suggested, provided they use these more accurate models to interpret their data.
The ultimate goal of this work is to prepare for the moment when an experiment might finally see a signal. The researcher developed a complete toolkit to handle that moment, from simulating the creation of dark matter to analyzing the data and comparing different theories. They showed how to confirm a discovery by checking if the same dark matter particles could also be seen in other types of detectors, creating a consistent picture across different experiments. While no dark matter has been found yet, this thesis provides the essential roadmap for the next generation of searches. It bridges the gap between complex theoretical models and the practical reality of running an experiment, ensuring that when the data comes in, scientists will know exactly how to read it and what it means for our understanding of the universe.
Drowning in papers in your field?
Get daily digests of the most novel papers matching your research keywords — with technical summaries, in your language.