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The Elusive Higgsino: A Particle Physics Quest

Scientists hunt for the mysterious higgsino particle, revealing secrets of the universe.

Rajneil Baruah, Arghya Choudhury, Kirtiman Ghosh, Subhadeep Mondal, Rameswar Sahu

― 6 min read


Chasing the Higgsino Chasing the Higgsino particles in physics. Unraveling the mysteries of elusive
Table of Contents

In the world of particle physics, scientists are always on the lookout for new particles that could change our understanding of the universe. One of the intriguing candidates in this search is the higgsino, a particle related to supersymmetry. Supersymmetry is a theory that suggests every known particle has a heavier partner. Imagine a world where your favorite superhero has an equally powerful sidekick! In this case, the higgsino might just be that sidekick, but so far, it's been a bit elusive.

What Are Higgsinos?

Higgsinos are theoretical particles that emerge from supersymmetry. Think of them as the cousins of the Higgs boson, which scientists discovered in 2012. The Higgs boson is crucial because it gives mass to particles. Higgsinos can potentially be lighter than the Higgs boson itself, making them an exciting target for scientists at particle colliders like the Large Hadron Collider (LHC).

In simpler words, if the Higgs boson is like a celebrity at a party, the higgsinos are the lesser-known guests trying to sneak into the spotlight.

Why Are Higgsinos Important?

Higgsinos are important for a couple of reasons. Firstly, they can help explain dark matter, the mysterious substance that makes up most of the universe but is invisible to us. Secondly, studying higgsinos can give us insights into the fundamental workings of nature, including how particles acquire mass and how they interact.

So, why should you care? Because understanding these particles helps us understand the universe, and that’s something we can all get behind!

The Challenge of Finding Higgsinos

Finding higgsinos has been likened to searching for a needle in a haystack. The problem is that they have a low production rate in particle collisions, meaning they are not created very often. To make things more complicated, their decay patterns are rather tricky to track. It's like trying to spot a tiny chameleon in a vibrant jungle—it’s there, but good luck seeing it!

Production Cross-sections

In particle physics, the “cross-section” refers to the likelihood of a particular reaction occurring. For higgsinos, this cross-section is quite small compared to their more famous cousins, the bino and wino particles. As a result, scientists have had a hard time pinning down the mass of higgsinos.

The Role of R-parity

R-parity is a crucial concept in supersymmetry. It is a way to categorize particles and helps in predicting their behavior. When R-parity is conserved, particles behave in a more straightforward manner. If R-parity is violated, which is the case for the scenarios scientists are currently studying, things get a lot more interesting—and complicated!

R-Parity Violation and Higgsinos

When R-parity is violated, the decay patterns of higgsinos change. Instead of lingering around like a shy guest at a party, they can transform into other particles more rapidly. This makes them more challenging to detect but also opens up new avenues for research. Scientists are focusing on scenarios where baryon number violation occurs, which means that certain types of particles can decay in ways that normally wouldn’t be possible.

Advancements in Detection Techniques

As scientists at the LHC gear up for new rounds of experiments, they are employing advanced techniques to increase their chances of finding higgsinos. One of the most exciting developments involves machine learning—a technology often associated with self-driving cars and smart assistants.

Machine Learning in Top Tagging

In particle physics, “top tagging” is a method used to identify top quarks, which are hefty particles that can decay into multiple lighter particles. By using machine learning algorithms, scientists can better analyze the data from collisions and efficiently identify these top jets, which might be associated with higgsino production.

Imagine training a robot to distinguish between various fruits. After a while, that robot becomes excellent at spotting an apple among a basket of oranges. Similarly, machine learning helps physicists become better at identifying the faint signals of higgsinos among the noise of other particle events.

Collider Analysis

To search for higgsinos effectively, scientists need to conduct a comprehensive collider analysis. This involves simulating particle collisions and analyzing the resulting data to find possible signals of these elusive particles.

Signal Regions

In collider analysis, researchers define “signal regions” to target their searches. Think of signal regions as specific zones in a treasure hunt where the treasure (in this case, higgsinos) is most likely to be found. Scientists combine two different regions characterized by the presence of top jets and various other particle jets to improve their chances of success.

Event Simulation and Object Reconstruction

A lot of groundwork happens before physicists can even think about detecting higgsinos. They perform event simulations to understand what might happen in a collision. This is like rehearsing for a play to make sure they know where everyone should be!

During these simulations, scientists generate signal events that represent potential higgsino production and decay. They also account for background events—these are the common particles produced that can obscure the signals scientists are looking for.

Reconstruction Techniques

Once data is collected, the real work begins. Scientists must reconstruct the events from the data, identifying the various particles produced in each collision. This is a bit like piecing together parts of a jigsaw puzzle where some pieces may be missing.

The Importance of Kinematic Variables

Kinematic variables play a vital role in distinguishing between signal and background events. These variables describe the motion and energy of the particles involved. By analyzing this data, scientists can determine where their higgsino might be hiding.

Effective Mass and Pseudo-Top Mass

Two important kinematic variables are the effective mass and pseudo-top mass. They help scientists distinguish between different types of particle events, allowing them to identify signals of higgsinos more effectively.

Results and Future Projections

As scientists continue their analysis, they are generating results that could help them probe the mass of higgsinos up to around 925 GeV. This is a significant advancement because previously they could only explore masses up to 320 GeV. It’s like finally getting access to an entire wing of a museum after years of viewing one small exhibit!

Conclusion

The hunt for higgsinos is a compelling tale of science, technology, and a bit of luck. While they may be challenging to detect, advancements in machine learning and collider techniques are pushing the boundaries of what scientists can achieve. As we look to the future, the potential discoveries await at the high-luminosity LHC, where scientists hope to solve the mystery of these enigmatic particles. Who knows? Higgsinos may one day reveal secrets about the universe, allowing us to understand our cosmic neighborhood even better!

Original Source

Title: Probing sub-TeV Higgsinos aided by a ML-based top tagger in the context of Trilinear RPV SUSY

Abstract: Probing higgsinos remains a challenge at the LHC owing to their small production cross-sections and the complexity of the decay modes of the nearly mass degenerate higgsino states. The existing limits on higgsino mass are much weaker compared to its bino and wino counterparts. This leaves a large chunk of sub-TeV supersymmetric parameter space unexplored so far. In this work, we explore the possibility of probing higgsino masses in the 400 - 1000 GeV range. We consider a simplified supersymmetric scenario where R-Parity is violated through a baryon number violating trilinear coupling. We adopt a machine learning-based top tagger to tag the boosted top jets originating from higgsinos, and for our collider analysis, we use a BDT classifier to discriminate signal over SM backgrounds. We construct two signal regions characterized by at least one top jet and different multiplicities of $b$-jets and light jets. Combining the statistical significance obtained from the two signal regions, we show that higgsino mass as high as 925 GeV can be probed at the high luminosity LHC.

Authors: Rajneil Baruah, Arghya Choudhury, Kirtiman Ghosh, Subhadeep Mondal, Rameswar Sahu

Last Update: 2024-12-16 00:00:00

Language: English

Source URL: https://arxiv.org/abs/2412.11862

Source PDF: https://arxiv.org/pdf/2412.11862

Licence: https://creativecommons.org/licenses/by/4.0/

Changes: This summary was created with assistance from AI and may have inaccuracies. For accurate information, please refer to the original source documents linked here.

Thank you to arxiv for use of its open access interoperability.

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