$\overlineΣ^{\pm}$ production in pp and p-Pb collisions at $\sqrt{s_{\rm NN}}$ = 5.02 TeV with ALICE

The ALICE experiment measured the transverse momentum spectra and integrated yields of anti-$\Sigma$ hyperons in pp and p-Pb collisions at $\sqrt{s_{\mathrm{NN}}}=5.02$ TeV using a novel antineutron reconstruction method, finding that while multiparton-interaction models best describe the high-$p_{\rm T}$ spectra, all tested models reproduce the total yields and nuclear modification factors within uncertainties.

The Gist

Imagine the Large Hadron Collider (LHC) at CERN as the world's most powerful particle smasher. Scientists fire tiny protons at each other at nearly the speed of light to recreate the conditions of the universe just a fraction of a second after the Big Bang.

This paper is a report from the ALICE experiment, a giant detector designed to catch the debris from these collisions. Specifically, the team is looking for a very elusive family of particles called $\Sigma$ hyperons (pronounced "Sigma").

Here is the story of their discovery, explained simply:

1. The Mystery of the "Ghost" Particle

In the chaotic explosion of a proton collision, hundreds of new particles are born. Most of them are easy to spot. But the $\Sigma$ hyperons are tricky. They are unstable and decay (fall apart) almost instantly into a neutron and a pion (a type of light particle).

The problem? Neutrons are ghosts. They have no electric charge, so they don't leave a trail in the standard tracking cameras. They just zip through the detector like invisible ninjas. For a long time, scientists couldn't catch them, which meant they couldn't study the $\Sigma$ hyperons that produced them.

2. The New Detective Trick: The "Flash" Camera

To solve this, the ALICE team developed a clever new method. They used a special part of their detector called PHOS (Photon Spectrometer), which acts like a high-speed camera for energy.

When a neutron (or antineutron) hits the PHOS crystals, it doesn't leave a track, but it does cause a massive, messy explosion of energy—a "shower." The team realized that by looking at the shape of this energy shower and the timing of when it hit, they could distinguish a neutron from other particles (like photons or protons).

Think of it like identifying a person in a dark room not by seeing their face, but by the specific pattern of footprints they leave in the snow and the time it takes them to walk across the room. This allowed them to "reconstruct" the invisible neutron and, by extension, find the $\Sigma$ hyperon that created it.

3. The Experiment: Tiny vs. Big Collisions

The team smashed protons together in two different scenarios:

• pp collisions: Two protons hitting each other (like two billiard balls).
• p–Pb collisions: A proton hitting a heavy Lead nucleus (like a bullet hitting a bowling ball).

They wanted to see how the "soup" of particles created in these collisions behaved. In heavy-ion physics, there's a theory that if you create enough energy and density, the particles stop acting like individuals and start flowing together like a liquid (called the Quark-Gluon Plasma).

4. The Results: Who Got the Recipe Right?

The scientists measured how many $\Sigma$ particles were made and how fast they were moving. Then, they compared their real-world data to predictions made by five different computer models (think of these as different chefs trying to guess the recipe of the particle soup).

• The Winners: The models named EPOS LHC and EPOS4 got it right. They predicted the number and speed of the particles perfectly. These models are special because they account for "multiparton interactions"—basically, they understand that when particles collide, it's not just a simple bounce; it's a complex dance where many parts of the particles interact at once.
• The Losers: Older models like PYTHIA 8 and PHOJET underestimated the number of particles at high speeds. They missed the complexity of the "dance," treating the collision too simply.

5. Why Does This Matter?

You might ask, "Why do we care about these specific particles?"

• Testing the "Liquid" Theory: The fact that the EPOS models (which include fluid-like behavior) worked best suggests that even in small collisions (proton-lead), the matter created behaves somewhat like a fluid. This is a big deal because we used to think this "liquid" state only happened in massive collisions between heavy nuclei.
• Understanding the Universe: By measuring these particles, scientists are learning exactly how nature creates "strange" matter (particles containing strange quarks). This helps us understand the fundamental rules of how the universe builds itself from the bottom up.
• A New Tool: The most exciting part is the technique itself. By successfully "catching" the ghostly neutrons, the ALICE team has opened a new door. They can now study other rare particles and interactions that were previously invisible, much like inventing night-vision goggles for the subatomic world.

The Bottom Line

The ALICE collaboration successfully caught a glimpse of a previously invisible particle family ($\Sigma$ hyperons) by using a clever new trick with their detector. They found that the universe's "recipe" for creating these particles is complex and fluid-like, and only the most sophisticated computer models could predict it correctly. It's a victory for both experimental ingenuity and theoretical physics.

Technical Summary

1. Problem and Motivation

The production of strange hadrons is a key signature for studying the Quark-Gluon Plasma (QGP) and the mechanisms of particle formation in high-energy collisions. While strangeness enhancement has been well-documented in heavy-ion (AA) collisions, its observation in smaller systems (proton-proton and proton-nucleus) challenges traditional models and suggests collective effects may exist even in small systems.

• The Gap: Prior to this work, there was no experimental data on Σ hyperon production in hadron collisions. While Σ hyperons are crucial for understanding strangeness production mechanisms and for constraining the feed-down contribution of Σ⁰ to the Λ yield (relevant for global polarization studies), they had not been measured in pp or p–Pb collisions.
• The Challenge: The decay channels for charged Σ hyperons are $\Sigma^+ \to n\pi^+$ and $\Sigma^- \to n\pi^-$. Reconstructing these decays requires identifying a neutron (n) or antineutron ($\bar{n}$), which is difficult because neutrons do not leave tracks in tracking detectors and deposit energy in hadronic showers that are hard to distinguish from background in high-multiplicity environments.

2. Methodology

The ALICE collaboration developed a novel analysis technique to reconstruct antineutrons using the Photon Spectrometer (PHOS) electromagnetic calorimeter.

• Data Sample:

  • Collisions: pp and p–Pb at $\sqrt{s_{NN}} = 5.02$ TeV.
  • Events: Inelastic (INEL) pp and non-single diffractive (NSD) p–Pb events.
  • Detectors:
    • PHOS: Used for antineutron ($\bar{n}$) reconstruction. It is a PbWO$_4$ crystal calorimeter with high granularity.
    • ITS & TPC: Used for reconstructing and identifying the charged pion ($\pi^\pm$) tracks.

• Antineutron Identification Strategy:
  Since PHOS cannot fully contain the energy of an antineutron (due to limited hadronic thickness) and energy resolution is insufficient for resonance peaks, the analysis relies on cluster properties and timing:

  1. Shower Shape: Antineutron-induced hadronic showers are more dispersed than electromagnetic showers (photons). Variables $\lambda_{short}$ and $\lambda_{long}$ (eigenvalues of the dispersion matrix) are used to select clusters with large dispersion.
  2. Neutrality: A cluster is identified as a neutral particle if no charged track from the central tracking system (ITS/TPC) extrapolates to the cluster position within a specific window ($10\sigma$).
  3. Cluster Size: Antineutron clusters typically contain more cells ($\sim 15$) compared to photon clusters ($\sim 2$).
  4. Timing: The time-of-flight (ToF) information from PHOS is used to estimate the antineutron momentum ($p_{rec}$) based on the distance from the primary vertex and the measured time.
  5. Selections: A minimum cluster energy ($E_{clu} > 0.5 - 0.6$ GeV) and specific dispersion cuts are applied to enhance the $\bar{n}$ purity to 50–60%.

• Decay Topology:

  • The $\Sigma^\pm$ decay vertex is reconstructed as the point of closest approach between the charged pion track and the straight line connecting the Primary Vertex (PV) to the PHOS cluster (approximating the $\bar{n}$ trajectory).
  • Topological variables (Distance of Closest Approach, Pointing Angle, decay radius) are used to separate signal from combinatorial background.

• Yield Extraction:

  • Raw yields are extracted from the invariant mass distribution of $n\pi^\pm$ pairs using the Event Mixing technique to estimate the combinatorial background.
  • The signal peak shape is modeled using a combination of two exponential distributions to account for the non-Gaussian nature of the PHOS timing response.
  • Reconstruction efficiencies are calculated using Monte Carlo (PYTHIA 8 for pp, DPMJET for p–Pb) with an iterative weighting procedure to match the measured $p_T$ spectra.

3. Key Contributions

1. First Measurement: This is the first measurement of $\Sigma^\pm$ production in pp and p–Pb collisions.
2. Novel Reconstruction Technique: It demonstrates the feasibility of reconstructing antineutrons in an electromagnetic calorimeter (PHOS) using shower shape and timing information, opening a new channel for studying neutral baryons.
3. Model Constraints: It provides critical data to test theoretical models (PYTHIA 8, EPOS, DPMJET, PHOJET) regarding multiparton interactions and collective flow in small systems.

4. Results

A. Transverse Momentum ($p_T$) Spectra

• Range: Measured in $0.5 < p_T < 3$ GeV/c.
• Model Comparison:
  • EPOS LHC and EPOS4: These models, which include collective effects and multiparton interactions, provide the best description of the measured spectra in both pp and p–Pb collisions.
  • PYTHIA 8: Reproduces low-$p_T$ yields but underestimates yields by a factor of 2–2.5 at $p_T > 1$ GeV/c.
  • PHOJET/DPMJET: Fail to describe the spectral shape; they agree at low $p_T$ but underestimate high-$p_T$ yields by a factor of ~3 (PHOJET) or overestimate low-$p_T$ and underestimate high-$p_T$ significantly (DPMJET).
• $\Sigma^+/\Sigma^-$ Ratio: The ratio is consistent with unity in both systems, indicating isospin symmetry. Models generally agree, though EPOS LHC predicts a slight excess of $\Sigma^+$ in pp.

B. Ratios to Other Hadrons

• $\Sigma/\pi$ and $\Sigma/p$ Ratios: In p–Pb collisions, the $\Sigma/\pi$ ratio continues to rise up to $p_T = 3$ GeV/c, unlike the $p/\pi$ ratio which saturates. This suggests participation in radial collective expansion.
• Model Performance: EPOS models describe these ratios well. PYTHIA 8 and DPMJET fail to reproduce the rising trend of baryon-to-meson ratios at high $p_T$.

C. Integrated Yields ($dN/dy$)

• Extrapolation: Integrated yields were calculated by extrapolating the measured spectra using Lévy-Tsallis and other functions.
• Comparison:
  • Thermal-FIST (Statistical Model): Successfully reproduces the integrated yields and the $\Sigma/\Lambda$ ratio.
  • Dynamical Models (PYTHIA, EPOS, DPMJET): Generally reproduce the total yields within uncertainties. However, they struggle to describe the absolute $\Lambda$ yield and the $\Sigma/\Lambda$ ratio (often overestimating it), whereas the Thermal-FIST model describes the $\Sigma/\Lambda$ ratio well.
  • $\Sigma/K$ Ratio: Dynamical models describe this ratio better than the Thermal-FIST model (which underestimates it).

D. Nuclear Modification Factor ($R_{pPb}$)

• Definition: $R_{pPb} = \frac{dN_{pPb}/dp_T}{\langle N_{coll} \rangle dN_{pp}/dp_T}$.
• Observation: The $R_{pPb}$ for $\Sigma^\pm$ coincides with that of protons, $\Lambda$, and $\Xi$ hyperons within uncertainties.
• Implication: There is no significant dependence on strangeness content. The low-$p_T$ deviation from unity is attributed to nuclear shadowing or soft processes, while the high-$p_T$ behavior is consistent with binary collision scaling.
• Models: Both EPOS LHC and EPOS4 successfully reproduce the measured $R_{pPb}$.

5. Significance

• Validation of Small System Collectivity: The success of EPOS models (which include hydrodynamic-like collective expansion) in describing $\Sigma^\pm$ spectra and ratios supports the hypothesis that collective effects are present in high-multiplicity pp and p–Pb collisions.
• Constraint on Hadronization: The data highlights the limitations of standard string fragmentation models (PYTHIA, PHOJET) at high $p_T$ in small systems and underscores the importance of multiparton interactions and radial flow.
• Feed-Down Control: By measuring $\Sigma^\pm$, the collaboration can better constrain the feed-down contribution of $\Sigma^0$ to $\Lambda$, which is essential for precise measurements of global polarization in future studies.
• Methodological Breakthrough: The successful reconstruction of antineutrons in PHOS establishes a new technique for measuring neutral baryons in high-energy physics, enabling future studies of neutron production and correlations.