⚛️ phenomenology

Dijet acoplanarity as a function of flattenicity in proton-proton collisions at $\sqrt{s}=13$ TeV

This paper demonstrates that using flattenicity as an event classifier in proton-proton collisions at 13 TeV significantly reduces selection biases inherent in multiplicity-based estimators, thereby providing a more reliable tool for investigating potential jet quenching effects and quark-gluon plasma formation in small systems.

Original authors: Jesus Eduardo Muñoz Mendez, Antonio Ortiz

Published 2026-10-06

📖 4 min read🧠 Deep dive

CC BY 4.0

Original authors: Jesus Eduardo Muñoz Mendez, Antonio Ortiz

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

In the heart of the Large Hadron Collider, where protons smash together at nearly the speed of light, physicists are searching for a state of matter that existed only fractions of a second after the Big Bang. This substance, known as a quark-gluon plasma, is a super-hot, ultra-dense soup where the fundamental building blocks of atoms, usually locked inside protons and neutrons, are free to roam. While scientists have long observed this plasma forming in collisions between heavy lead atoms, a more puzzling question has emerged: can it also form when two tiny protons collide? If it does, it would mean that even the smallest systems can create the extreme conditions necessary for this exotic state. To find out, researchers look for signs that particles lose energy as they travel through this potential soup, a process called jet quenching. However, detecting this subtle effect in proton collisions is notoriously difficult because the methods used to pick out the most interesting crashes often introduce their own distortions, making it hard to tell if a signal is real or just an artifact of the measurement.

A recent study by researchers at the National Autonomous University of Mexico tackles this problem by re-examining how scientists select these high-energy proton collisions. The team used a sophisticated computer simulation to recreate billions of proton-proton collisions at an energy level of 13 TeV, a scale that mimics the conditions found in the actual experiments at the Large Hadron Collider. Their goal was to test various tools, known as event classifiers, which are used to sort through the chaos of a collision and identify the most active, high-energy events. Some of these tools simply count the number of particles produced, while others measure the shape of the spray of debris or the distribution of energy. The researchers wanted to see if these different selection methods were actually looking at the same kind of collisions or if they were inadvertently picking out different types of events, thereby skewing the results.

The investigation revealed that the most common tools used to select these events are deeply flawed for this specific purpose. When researchers use a simple count of particles or a measure of activity in the forward direction to select the top 0.1 percent of the most violent collisions, they inadvertently bias the sample. These methods tend to select collisions that are naturally "harder" or more energetic than average, and they also favor events where the debris is aligned in a specific way. This bias creates a false impression that the particles are spreading out more than they should, a phenomenon that looks like jet quenching but is actually just a side effect of how the events were chosen. The simulation showed that even when the computer model did not include any quark-gluon plasma or energy loss, these traditional selection methods still produced a broadening of the particle spray, mimicking the very signal scientists are trying to find.

To solve this, the researchers turned to a newer, more refined tool called flattenicity. Unlike the older methods that rely on simple counts or broad energy measurements, flattenicity looks at how evenly the energy is distributed around the collision point. When the team applied this new tool to select their high-energy events, the results changed dramatically. The artificial broadening disappeared. The distribution of particles in the events selected by flattenicity looked exactly like the distribution in a random, unselected sample of collisions. This suggests that the strange spreading effect seen in previous studies using traditional classifiers was likely an artifact of the measurement tools themselves rather than a physical signal. By using flattenicity, the researchers effectively removed the noise that was hiding the true signal.

The findings offer a clear path forward for the search for quark-gluon plasma in small systems. The study demonstrates that the apparent broadening observed in earlier simulations using traditional classifiers was driven by selection bias rather than jet quenching. When the bias is removed using flattenicity, no such broadening is observed in the simulations, which do not incorporate jet quenching. This does not prove that quark-gluon plasma cannot form in proton collisions, but it strongly suggests that previous claims of its detection based on these specific selection methods may have been premature. The researchers conclude that flattenicity is a superior tool for this work, as it allows scientists to see the collisions without the distortion of selection bias. If future experiments using this method still find signs of energy loss, it would provide much stronger evidence that a tiny droplet of the primordial soup is indeed forming in the smallest of collisions.

Technical Summary: Dijet Acoplanarity as a Function of Flattenicity in pp Collisions

Problem Statement
The observation of Quark-Gluon Plasma (QGP) formation in small systems, such as proton-proton (pp) collisions, remains a subject of debate. While jet quenching is well-established in heavy-ion (Pb–Pb) collisions, its direct evidence in small systems is elusive. A significant challenge in pp studies is the potential for selection biases introduced by the event classifiers used to define "high-activity" collisions. Previous work by ALICE suggested that the azimuthal broadening observed in dijet studies—often interpreted as a signature of jet quenching—may be an artifact of these biases rather than a genuine medium effect. Specifically, multiplicity-based estimators may inadvertently select events with specific geometric or fluctuation properties that mimic quenching signals.

Methodology
To investigate the impact of different event classifiers, the authors utilized PYTHIA 8 simulations (Monash tune) of 6 billion inelastic pp collisions at $\sqrt{s} = 13$ TeV. The study focused on comparing the top 0.1% of events selected by various widely used LHC classifiers:

Multiplicity-based: Charged-particle multiplicity at mid-pseudorapidity ( $N_{ch}$ ), forward pseudorapidity multiplicity (V0M), and the relative transverse activity classifier ( $R_T$ ).
Event-shape based: Transverse sphericity ( $S_T$ ), spherocity ( $S_0$ ), and flattenicity ( $1-\rho_{n_{ch}}$ ).

The primary observable used to assess jet quenching effects was the $\Delta_{recoil}$ distribution. This observable measures the azimuthal distribution of charged-particle jets recoiling from a high- $p_T$ trigger hadron. To isolate the signal from uncorrelated background, the authors employed a subtraction method comparing a signal class ( $TT_{sig}$ , $20 < p_{T,trig} < 30$ GeV/ $c$ ) against a reference class ( $TT_{ref}$ , $6 < p_{T,trig} < 7$ GeV/ $c$ ). The analysis examined the resulting distributions of jet pseudorapidity ( $\eta_{jet}$ ), transverse momentum spectra, and the phase space coverage in the $\hat{p}_T$ - $N_{mpi}$ plane (where $\hat{p}_T$ is the transverse momentum of the main parton-parton scattering).

Key Contributions and Results
The study provides a comparative analysis of how different classifiers select collision events, revealing distinct biases:

Bias in Multiplicity-Based Estimators: Classifiers such as $N_{ch}$ , V0M, and $R_T$ were found to select events that are "harder-than-average."
- Local Fluctuations: $N_{ch}$ selection showed an enhancement in the pseudorapidity region where it is calculated, indicating an autocorrelation bias toward coplanar jets. V0M selection exhibited asymmetry, preferentially selecting events with recoil jets in the V0C acceptance.
- Harder Spectra: The transverse momentum spectra for events selected by these multiplicity-based estimators showed a bias toward higher $p_T$ compared to minimum-bias (MB) events.
- Phase Space: In the $\hat{p}_T$ - $N_{mpi}$ plane, these classifiers covered a very broad range of $\hat{p}_T$ , deviating significantly from the average trend where $\hat{p}_T$ increases with $N_{mpi}$ .
Bias in Event-Shape Estimators: Sphericity ( $S_T$ ) and spherocity ( $S_0$ ) were found to suppress high- $p_T$ particles and recoil jets, selecting a different subset of the collision landscape compared to multiplicity-based methods.
Performance of Flattenicity: The classifier based on flattenicity ( $1-\rho_{n_{ch}}$ ) demonstrated behavior distinct from the biased multiplicity estimators.
- Reduced Bias: Events selected by flattenicity closely resembled the distributions of MB events and the Monte Carlo generator's intrinsic multi-parton interaction ( $N_{mpi}$ ) selection. They showed no enhancement in specific pseudorapidity regions and no significant deviation in the $\hat{p}_T$ - $N_{mpi}$ phase space.
- Dijet Acoplanarity: When applied to the $\Delta_{recoil}(\Delta\phi)$ observable, events triggered with flattenicity showed no azimuthal broadening compared to MB events. In contrast, the V0M selection reproduced the broadening previously observed in experimental data.

Significance and Claims
The paper concludes that the azimuthal broadening observed in dijet studies using multiplicity-based estimators (like V0M) in PYTHIA 8—which does not incorporate jet quenching physics—is an artifact of selection bias rather than a physical medium effect.

The authors claim that flattenicity significantly reduces these biases. Consequently, they posit that flattenicity is a promising tool for LHC experiments to refine jet-quenching searches in small systems. The paper argues that if a broadening effect is observed in future studies using flattenicity, it would provide a stronger indication of genuine medium-induced jet quenching, as the confounding selection biases associated with multiplicity-based estimators would be minimized. The study does not claim to have observed jet quenching in pp collisions but rather establishes a methodological framework to distinguish between bias and physical phenomena.

Technical Summary: Dijet Acoplanarity as a Function of Flattenicity in pp Collisions

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