Characterizing Heavy Neutral Leptons: Measuring Parameters, Discriminating Majorana versus Dirac, and Using FASER2 as a Trigger for ATLAS

TL;DR

The paper analyzes how FASER2, a forward LHC detector, can measure the mass $m_N$ and mixing $U_\mu$ of a discovered heavy neutral lepton (HNL) and, crucially, determine whether it is Majorana or Dirac. Using two benchmark models, Model 1 with $m_N=1.84$ GeV and Model 2 with $m_N=2.00$ GeV, the study demonstrates that FASER2 alone can achieve sub-percent to percent-level precision on $m_N$ and a few-percent to tens-of-percent precision on $U_\mu$, with strong but flux-uncertainty-limited capability to distinguish Majorana from Dirac in Model 1. For the lower-yield Model 2, FASER2 as a trigger for ATLAS, enabling correlated measurements of forward and ATLAS muons, substantially improves spinor discrimination, especially if forward-hadron flux uncertainties are reduced. The results highlight a cradle-to-grave, multi-detector strategy that can yield precise HNL properties and robust Majorana versus Dirac discrimination, with meaningful implications for neutrino masses, baryogenesis, and fundamental symmetries.

Abstract

This work explores the potential of the proposed FASER2 experiment at the LHC to determine the properties of a discovered heavy neutral lepton (HNL), including its mass, couplings, and whether it is a Majorana or Dirac fermion. We first consider a Majorana HNL with mass $m_N = 1.84\,\rm{GeV}$ that is primarily produced through decays $D \to μN$ at the ATLAS interaction point. Such HNLs may travel macroscopic distances in the far-forward direction and then decay, yielding approximately 8600 $N \to μπ$ decays in FASER2 at the High-Luminosity LHC. With FASER2 measurements alone, the HNL's mass and couplings can be measured to fractional uncertainties of approximately 0.1% and 3% at 95% CL, respectively, and the Dirac fermion hypothesis can be rejected at 99.8% CL. We then consider a second, more difficult, case of a Majorana HNL with mass $m_N = 2.00\,\rm{GeV}$, yielding only 80 $N \to μπ$ decays in FASER2. With FASER2 alone, measurements of HNL properties are still possible, but somewhat less precise. However, by using FASER2 as a trigger for ATLAS and measuring the charge of the muon produced in association with the HNL at ATLAS to search for lepton number violation, one can precisely measure the HNL's properties and reject the Dirac fermion hypothesis at 99.7% CL. These results show that FASER2, sometimes in coordination with ATLAS, can precisely determine HNL properties, with far-reaching implications for our understanding of neutrino masses, baryogenesis, and the fundamental symmetries of nature.

Figures (11)

• Figure 1: Schematic diagrams showing example HNL events at ATLAS and FASER2 Salin:2927003. A charged $B$ meson decays through $B^{\mp} \to \overset{(-)}{D}{}^{*0}\mu^{\mp} N$ at the ATLAS IP. The HNL then travels approximately 650 m and decays through $N \to \mu^{\mp} \pi^{\pm}$ in FASER2. For Majorana HNLs, both the top (lepton-number conserving) and bottom (lepton-number-violating) processes are allowed. For Dirac HNLs, only the top process is allowed.
• Figure 2: Schematic diagram illustrating the major steps in simulating HNL events in the ATLAS and FASER2 detectors. The parameter $w$ is the event weight; it starts with the number of hadrons produced for a particular range of energy and angle, and then is reduced by branching fractions and detector acceptance to determine the number of HNL events detected in FASER2.
• Figure 3: Kinematic distributions for HNLs that decay through $N \to \mu^{\pm} \pi^{\mp}$ in FASER2. The HNLs are produced in meson decays at the HL-LHC with $E_{\text{COM}} = 14~\text{TeV}$ and an integrated luminosity of $3~\text{ab}^{-1}$. Results are shown for Model 1, a Majorana HNL with $m_N = 1.84~\text{GeV}$ and $U_\mu = 0.0036$, as well as for variations from this model with Dirac HNLs, and with $U_\mu = 0.0040$, as indicated. The shaded error bars illustrate the envelope of expected outcomes in each bin, given by $\sqrt{\mu_i}$, where $\mu_i$ is the number of events in bin $i$.
• Figure 4: Two-dimensional kinematic distributions for the Model 1 Asimov dataset, a Majorana HNL with $m_N = 1.84$ GeV and $U_\mu = 0.0036$ (left), and the best-fit Dirac model with $\hat{m}_N = 1.84$ GeV and $\hat{U}_\mu = 0.0051$, obtained by minimizing $t(\vec{d}_A\,|\,m_N,U_\mu)$ (right). The Dirac best-fit point recreates the signal shape well, but at the expense of a significantly larger event rate.
• Figure 5: Expected parameter estimation contours, assuming the underlying data corresponds to Model 1, a Majorana HNL with $m_N = 1.84~\text{GeV}$ and $U_{\mu} = 0.0036$ (left), and Model 2, a Majorana HNL with $m_N = 2.0~\text{GeV}$ and $U_{\mu} = 0.002$ (right). The HNL flux normalization and shape uncertainties are assumed to be $\sigma_{\eta} = 60\%$ and $\sigma_{\gamma} = 10\%$, respectively.