Atmospheric Neutrino Charged-Current Interactions at Large Liquid-Scintillator Detectors: I. Physics of Neutrino-Antineutrino Discrimination

This paper presents a systematic study of atmospheric neutrino-antineutrino discrimination in large liquid-scintillator detectors by analyzing event characteristics such as inelasticity and neutron multiplicity, thereby establishing a foundation for determining the neutrino mass ordering.

The Gist

Imagine the Earth is constantly being pelted by invisible cosmic rain. These aren't water droplets, but neutrinos—ghostly, tiny particles that pass through everything, including you, the Earth, and the core of the sun, without leaving a trace.

Scientists have a big mystery to solve: Is the heaviest neutrino the "big brother" or the "little brother" in the family? This is called the "Neutrino Mass Ordering." To solve it, they need to catch these ghosts and figure out if they are neutrinos or their antimatter twins, antineutrinos.

This paper is like a detective's guidebook for a specific type of high-tech "net" called a Liquid Scintillator Detector (think of a giant, ultra-sensitive swimming pool filled with glowing oil). Here is how the authors explain the physics in simple terms:

1. The Setup: A Giant Glowing Pool

Imagine a massive sphere filled with 20,000 tons of liquid that glows when particles hit it. Inside this pool, neutrinos occasionally crash into atoms (mostly Carbon and Hydrogen). When they do, they create a splash of energy.

The problem? Neutrinos and antineutrinos look almost identical when they first hit the water. It's like trying to tell the difference between a left-handed and a right-handed baseball player just by looking at the shadow they cast.

2. The Clues: Two Main "Fingerprints"

The authors discovered that while the initial crash looks similar, the aftermath is very different. They found two main clues to tell them apart:

Clue A: The "Spending Habits" (Inelasticity)

Think of a neutrino as a customer with a fixed budget of energy.

• Neutrinos are generous spenders. When they crash, they give a large chunk of their energy to the debris (the "hadrons" or broken pieces of the atom). They keep less for themselves.
• Antineutrinos are tightfisted. They keep most of their energy for the outgoing particle (the "lepton" or the main survivor) and give very little to the debris.

By measuring how much energy is left in the "debris pile" versus the "survivor," scientists can guess who the customer was.

Clue B: The "Ghostly Guests" (Neutron Multiplicity)

This is the paper's superpower. When the crash happens, it often kicks out tiny, invisible particles called neutrons.

• Antineutrinos are like chaotic party hosts. They tend to kick out more neutrons right at the start of the crash.
• Neutrinos are more orderly. At lower energies, they kick out fewer neutrons initially.

However, there's a twist! If the crash is very energetic (high energy), the debris from a neutrino crash starts bouncing around the pool, hitting other things, and creating a secondary explosion of neutrons. So, at high energies, neutrinos actually end up with more neutrons than antineutrinos.

3. The Detective Tool: The "BDT" (Boosted Decision Tree)

The scientists didn't just look at one clue; they used a computer brain (an algorithm called a Boosted Decision Tree) to look at both clues at the same time.

• It asks: "Did the debris get a lot of energy? Did we catch a lot of neutrons?"
• By combining these two factors, the computer can draw a map that separates the neutrinos from the antineutrinos with over 70% accuracy.

4. The "Swimming Pool" Problem (Detector Size)

Here is the catch: The size of the pool matters.

• Electrons (from electron-neutrinos) are like firecrackers; they explode in a tiny spot. The pool size doesn't matter much.
• Muons (from muon-neutrinos) are like long, straight arrows. They travel far. If the pool is too small, the arrow flies out of the water before it stops.

If the arrow flies out, the scientists can't measure its full energy. This makes it harder to tell the difference between a neutrino and an antineutrino. The paper shows that for the "sweet spot" of energies needed to solve the mass mystery, the JUNO detector (the 20,000-ton pool) is just the right size to catch most of these arrows completely.

The Big Picture: Why Does This Matter?

This research is the foundation for a new era of neutrino physics. By teaching detectors how to spot the difference between neutrinos and antineutrinos using these "debris" and "neutron" clues, we can:

1. Solve the Mass Ordering Mystery: Determine if the heaviest neutrino is the 3rd or 2nd in line.
2. Understand the Universe: This helps explain why the universe is made of matter instead of antimatter.

In a nutshell: The paper teaches us how to play "spot the difference" with invisible cosmic particles by looking at how much energy they spill and how many ghostly neutrons they leave behind in a giant, glowing pool.

Technical Summary

1. Problem Statement

Determining the Neutrino Mass Ordering (NMO) is a critical open question in particle physics. While the JUNO experiment (a 20-kton liquid scintillator detector) aims to solve this using reactor antineutrinos, it also possesses the capability to detect atmospheric neutrinos. Combining reactor and atmospheric data could significantly enhance NMO sensitivity.

However, a major challenge in using atmospheric neutrinos for this purpose is the inability to distinguish between neutrinos ($\nu$) and antineutrinos ($\bar{\nu}$). Current methods often lack robust physical mechanisms for this discrimination in liquid scintillator (LS) environments. This paper addresses the gap in understanding the fundamental physics of event characteristics that can differentiate $\nu$ from $\bar{\nu}$ in large LS detectors, specifically focusing on Charged-Current (CC) interactions.

2. Methodology

The authors employed a comprehensive Monte Carlo simulation framework to model atmospheric neutrino interactions:

• Event Generation: Used GENIE (v3.2.0) to simulate CC interactions of atmospheric neutrinos ($\nu_e, \bar{\nu}_e, \nu_\mu, \bar{\nu}_\mu$) with $^{12}\text{C}$ (88%) and $^1\text{H}$ (12%) nuclei. A total of 4 million events were generated, covering Quasi-Elastic (QEL), Resonance (RES), and Deep Inelastic Scattering (DIS) processes.
• Detector Simulation: Used Geant4 (v10.4.2) to simulate secondary interactions of final-state particles within the LS medium.
  • Detector Model: A simplified JUNO-like geometry (20 kton LS in a 17.7m radius acrylic sphere, surrounded by 17,612 20-inch PMTs).
  • Physics Models: Customized Livermore models for electromagnetic interactions and QGSP_BERT_HP with modified neutron capture processes for hadronic interactions.
• Analysis Variables: The study focused on two primary observables:
  1. Inelasticity ($y$): The ratio of energy transferred to the hadronic system to the total energy transfer ($y = (E_\nu - E_l)/E_\nu$).
  2. Captured Neutron Multiplicity ($N_n$): The number of neutrons produced and subsequently captured (emitting 2.2 MeV gammas).
• Machine Learning: A Boosted Decision Tree (BDT) classifier was trained using inelasticity and neutron multiplicity to quantify discrimination performance.

3. Key Contributions & Physical Insights

A. Topological Differences in Leptons and Hadrons

The paper establishes distinct light topologies in LS:

• Muons: Produce long, straight ionization tracks (bright, concentrated signals).
• Electrons: Initiate electromagnetic showers (diffuse, multi-point light distribution).
• Hadrons: Create complex, clustered shower patterns due to secondary interactions.
• Note: Flavor identification (e vs. $\mu$) relies on these topologies, while $\nu$ vs. $\bar{\nu}$ discrimination relies on hadronic properties.

B. Inelasticity Distribution Asymmetry

There is a fundamental asymmetry in how neutrinos and antineutrinos transfer energy to hadrons:

• Neutrinos: Tend to transfer a larger fraction of energy to the hadronic system, resulting in a broader, flatter inelasticity distribution.
• Antineutrinos: Transfer less energy to hadrons, resulting in a distribution that peaks at lower inelasticity values and declines rapidly.
• This difference is driven by coupling constants and parton distribution functions within nucleons.

C. Neutron Multiplicity Dynamics

The production of neutrons is the most powerful discriminator, but it is energy-dependent:

• Low Energy (< 5 GeV): Antineutrinos produce more captured neutrons than neutrinos. This is because antineutrinos transfer less energy to the primary lepton, leaving more energy for the hadronic system to generate neutrons via primary interactions (though the paper notes antineutrinos are more efficient at generating neutrons in primary processes at low energies).
• High Energy (> 5 GeV): The trend reverses. Neutrinos produce more neutrons. At these energies, Deep Inelastic Scattering (DIS) dominates, and the higher inelasticity of neutrinos leads to more energetic hadronic showers, which generate more secondary neutrons via inelastic scattering and $\pi^-$ capture.
• Mechanism: Neutron inelastic scattering is the dominant source of neutrons across all energies, followed by inelastic interactions from protons and charged pions.

D. Detector Size Effects (Geometric Containment)

The finite size of the detector (e.g., JUNO's 35.4m diameter) significantly impacts muon events:

• Fully Contained (FC) Events: At low energies (relevant for NMO), muons are fully contained. This preserves the intrinsic energy partition between the lepton and hadron, maintaining the separation power of inelasticity and neutron multiplicity.
• Partially Contained (PC) Events: At high energies (> 7 GeV), muons escape the detector. This forces the reconstruction to assign more energy to the hadronic system artificially, inflating inelasticity and neutron multiplicity. This "geometric cutoff" washes out the intrinsic differences between $\nu$ and $\bar{\nu}$, degrading discrimination performance at high energies.

4. Results

• Discrimination Performance: Using a BDT classifier with inelasticity and neutron multiplicity:
  • Low Energy (< 5 GeV):** The discrimination is highly effective, with **AUC (Area Under Curve) values > 0.8. The neutron multiplicity provides significant additional separating power here.
  • High Energy: Performance degrades. For fully-contained muon events, the AUC drops toward 0.5 (random guessing) around 20 GeV due to geometric containment limits.
  • Flavor Dependence: $\nu_e/\bar{\nu}_e$ and $\nu_\mu/\bar{\nu}_\mu$ discrimination show similar trends, though $\nu_\mu$ is more sensitive to detector size effects due to track length.
• BDT Score Distribution:
  • Samples with zero captured neutrons show the highest BDT scores (~0.9), dominated by neutrino events.
  • Samples with captured neutrons show a peak at ~0.6, where higher inelasticity correlates with higher scores.
• Comparison with Other Detectors: The paper notes that while Water Cherenkov (Super-K, IceCube) and Liquid Argon (DUNE) detectors have different strengths, LS detectors offer unique advantages due to their low energy thresholds and high light yield, which allow for precise reconstruction of hadronic components and neutron tagging.

5. Significance

This work provides the theoretical and phenomenological foundation for using atmospheric neutrinos in large liquid scintillator detectors to determine the Neutrino Mass Ordering.

1. New Discrimination Strategy: It validates that neutron multiplicity combined with inelasticity is a robust, physics-driven method for $\nu$/$\bar{\nu}$ discrimination, superior to relying solely on lepton topology.
2. Optimization for JUNO: It identifies the optimal energy window (few GeV range) where detector size effects are minimal and discrimination power is maximal, guiding the analysis strategy for JUNO.
3. Future Physics: By enabling better flavor and particle-antiparticle identification, this research paves the way for more precise measurements of CP violation and mass ordering using atmospheric neutrinos, complementing reactor neutrino data.
4. Methodological Framework: The study establishes a benchmark for simulating secondary hadronic interactions and neutron captures in LS, which is crucial for future detector designs and analysis pipelines.