Search for Ultra-Relativistic Magnetic Monopoles with LHAASO-KM2A

arXiv:2610.30968 · astro-ph.HE · Submitted 2026-10-22 · Read on arXiv

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Introduction to the show: ident: Astrophysics Radio. Generated commentary on the latest astrophysics papers.

Vera: We're looking at the paper: "Search for Ultra-Relativistic Magnetic Monopoles with LHAASO-KM2A".

Jocelyn: Here's the gist of the paper. A search for ultra-relativistic magnetic monopoles traversing the atmosphere using 1438.81 live days of data from the LHAASO-KM2A array established upper limits on their isotropic monopole flux within a previously unconstrained velocity window of Lorentz factors 105 to 107, improving upon the astrophysical Parker bound by over three orders of magnitude Search Strategy and Detection Mechanism The search utilized data from the LHAASO-KM2A array, which is well-positioned to fill a phase-space gap where other observatories have limits.

Vera: The array's dense, co-located arrays of electromagnetic detectors (EDs) and muon detectors (MDs) provide strong background rejection and fine spatial granularity, making it well-suited to search for MMs with Lorentz factors between 105 and 107.

Jocelyn: The expected signature of these particles is that they are "characterized by muon deficit and a compact lateral profile".

Vera: To separate the signal from the hadronic background, a machine-learning classifier utilizing lateral shower shape observables from the electromagnetic detectors is deployed.

Paper summary: Vera: So to summarize this paper, they are searching for ultra-relativistic magnetic monopoles using LHAASO-KM2A data across one thousand four hundred thirty-eight point eight one live days in the Lorentz factor range of ten five to ten seven.

Jocelyn: The core idea is that these monopoles should leave a signature in air showers, specifically a muon deficit and a compact lateral profile.

Subrahmanyan: And they used a machine learning classifier based on the lateral shower shape observables from the electromagnetic detectors to try and pull that signal out from the hadronic background.

Vera: They found no statistical excess over what you'd expect from just the background, but they set upper limits at a ninety percent confidence level on the isotropic monopole flux.

Jocelyn: The results show an upper limit of two point six times ten-sixteen cm-two s-one sr-one at gamma = ten five and reaching eight point eight times ten-nineteen cm-two s-one sr-one at gamma = ten seven.

Subrahmanyan: Those limits are important because they improve upon the astrophysical Parker bound by more than three orders of magnitude. That's a big jump in how strictly we can constrain the flux.

Vera: They also established that these constraints apply to a velocity window that hasn't been constrained before, which is what makes this study unique.

Jocelyn: The methodology involved using reconstructed shower energy and the muon-to-electromagnetic ratio, R mu e, to define their signal region.

Subrahmanyan: That R mu e ratio is a good handle to suppress the background because hadronic showers tend to have more muons than what we expect from a monopole signature.

Vera: They used a data-driven approach for modeling the background shape, which they grounded in an empirical template extracted from data right next to their signal region boundary.

Jocelyn: They found that no significant excess was observed in the MLP score distributions, so the conclusion is that they haven't found any evidence for these particles yet.

Subrahmanyan: So what this means for cosmology is that we're putting upper bounds on the production of these massive particles at high energies.

Conclusion: Vera: So looking at "Search for Ultra-Relativistic Magnetic Monopoles with LHAASO-KM2A," the authors Zhen Cao and all their collaborators looked for ultra-relativistic magnetic monopoles using data from LHAASO-KM2A.

Jocelyn: They established upper limits on the isotropic monopole flux in the Lorentz factor range of ten five to ten seven which is a previously unconstrained velocity window.

Subrahmanyan: The main implication is that these constraints improve upon the astrophysical Parker bound by over three orders of magnitude for both lower and higher Lorentz factors.

Vera: Basically, they're saying that if magnetic monopoles exist in this range, their flux has to be extremely low.

Jocelyn: It also confirms that LHAASO-KM2A fills a gap where other arrays haven't been sensitive to these particles before.

Subrahmanyan: So for us, it means we have tighter experimental boundaries on the production models of these massive particles at high speeds.

Vera: The work sets a stringent experimental boundary on the isotropic cosmic monopole flux based on what they observed.

Jocelyn: It’s a clear statement that so far, no evidence for these ultra-relativistic monopoles has been found in this dataset.

Zhen Cao, F. Aharonian, Y.X. Bai, Y.W. Bao, D. Bastieri, X.J. Bi, Y.J., W., Bianchi J., Blunier J., A.✓V Butkevich C.✓M Cai W.✓Y. Cao Zheо, J. Chang J.F. Chang E.S. Chen G.H., H.K., L.F., Liang Chen Long Chen M. J.✓ M.✓ L.✓ Q.H., S., ШZ. Chen T.L. Chen X.B. Chen X.J., X.P., Y., N.о Cheng Q.Х Cheng Y.Д D.✓ Cheng M.✓ Y Cui S.W., X.H., ШY.D. Cui B.Z., H.L., L.X., Z.G., К Danzengluobu Y.о D.✓ K.✓ Duan X.✓ D.✓ J.H Fan Y.Z Fan J. Fang J.H Fang K. Fang C.F., H., L., ѐS H Feng X.T, Y.о Y.✓ S Gabici B Gao Q Gao W Gao C Ge M.М T.T Ge L.S Geng G Giacinti G.H Gong Q.B Gou M.H Gu W.М F.L Guo J Guo K.J Guo X.✓ L.✓ YQ., Y.✓ R P Han O.A Hannuksela M Hasan H.H He H.N He J.Y He X.И Y He S Hernández-Cadena C Hou X Hou H.B Hu S.C Hu C Huang D.ИH Huang F Huang JJ Huang X.✓ LHuang X.T Huang X.Y., Y.✓ ZQ Huang A Inventar X.L Ji H.Y Jia K Jia H.B Jiang K Jiang XW Jiang ZJ Jiang M Jin S Kaci M.М Kang I Karpikov D Khangulyan D Kuleshov K Kurinov W.H Lei Cheng Li Cong Li D Li F Li H.B Li H.C., Jian, Jie, K., гR L.✓ T.Y Li W.L, X.Ш Y.✓ Zhe Li Zhuo E.W Liang S J Lin B Liu C Liu D Liu H Liu J F Liu J.L., J R, M.Y., ШQ Lou Q Luo Y Luo H K Lv B Q Ma L L Ma XH I O Maliy J R Mao Z Min W Mitthumsiri Y Mizuno G B Mou A Neronov C.✓Y Ng K C.Y Ng M.Y Ni, L Nie L J Ou ZW Ou P Pattarakijwanich Z.Y Pei D Y Peng J.C Qi M.Y Qi J J Qin H Qu A Raza C Y Ren M Q Ruan D Ruffolo A Sáiz D Savchenko D Semikoz L Shao O Shchegolev YZ Shen X.✓ Sheng F W Shu H.C Song Yu V Stenkin Y Su C.Y Sun ZB Sun XN Sun ZB, N.H Tabasam J Takata P H T Tam H.B Tan QW Tang R Tang ZB W W Tian C N Tong L H Wan C Wang D.ИH Wang G W Wang H G Wang J C Wang J F Wang J S Wang KWang KaiWang L PWang L YWang W.W XGо XJ✓ X.Y✓ YW, Z.H., Z.✓ W Wang ZX Wang Zheng Wang D M Wei J J Wei YJ Wei T Wen S S Weng C Y Wu H R Wu QW Wu S Wu XF YS Wu †S Q Xi J Xia G M Xiang D X Xiao G Xiao YF B H Xie F Xie YL Xin H D Xing Y Xing D R Xiong B N Xu C.Y Xu D L Xu R X Xu S S Xu L Xue D H Yan T Yan C Yang C.Y Yang F F Yang L L Yang M J Yang R Z Wang ZH ZG Yao X A Ye L Q Yin N Yin XH You ZY You YH Yu Q Yuan H Yue H D Zeng T X Zeng M.Zha B Zhang B B Zhang B T Zhang C Zhang H Zhang H M Zhang H Y✓ J.L✓ J.Y✓ L Y Wang P F Z.о X.о Yi, J Yong, Z P Z.о ZH ZG Yao X A Ye L Q Yin N Yin XH You ZY You YH Yu Q Yuan H Yue H D Zeng T X Zeng M.Zha B Zhang B B Zhang B T Zhang C Zhang H Zhang H M Zhang H Y✓ J.L✓ J.Y✓ L Y Wang P F Z.о X.о Yi, L., Z.ѐ Zhao L Z.Zhao S P Zhao XH F Zheng T C Zheng B Zhou H Zhou J N Zhou L Zhou M Zhou P Zhou RZhou XXZhou B Y Zhu C G Zhu F R Zhu H Zhu K J Zhu YF Z F Zhu YC Zou, and X. Zuo, State Key Laboratory of Particle Astrophysics & Experimental Physics Division & Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, University of Chinese Academy of Sciences, Tianfu Cosmic Ray Research Center, University of Science and Technology of China, Yerevan State University, Max-Planck-Institut for Nuclear Physics Heidelberg Germany., main_finding: The LHAASO-KM2A array established a 90 percent confidence level upper limit on the isotropic magnetic monopole flux between Lorentz factors 105 and 107 that improves upon the astrophysical Parker bound by over three orders of magnitude., what_researchers_did: Researchers used data from the LHAASO-KM2A array to search for ultra-relativistic magnetic monopoles in a specific velocity window using a machine learning classifier., supporting_evidence: The resulting upper limit at gamma = 107 reaches 8.8 x 10^-19 cm^-2 s^-1 sr^-1 which improves upon the astrophysical Parker bound by over three orders of magnitude., surprising_detail: The search successfully filled a phase-space gap between existing constraints from cosmic ray observatories and the astrophysical Parker bound., why_it_matters: This work establishes constraints on the monopole flux within a previously unconstrained velocity window for fundamental physics models., limitations: The analysis is limited to a reference monopole mass of 105 GeV and assumes that bremsstrahlung radiation and the change in the monopole Lorentz factor during atmospheric propagation remain negligible., confidence_or_uncertainty: high

State Key Laboratory of Particle Astrophysics & Experimental Physics Division & Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences · University of Chinese Academy of Sciences · TIANFU Cosmic Ray Research Center · University of Science and Technology of China · Yerevan State University · Max-Planck-Institut for Nuclear Physics

astro-ph.HE

Submitted: 2026-10-22

Updated: 2026-10-22

Comments: 10 pages, 5 figures, plus Supplemental Material (1 page, 2 figures)

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

The gist: The gist: A search for ultra-relativistic magnetic monopoles traversing the atmosphere using 1438.81 live days of data from the LHAASO-KM2A array established upper limits on their isotropic monopole

Key concepts

Magnetic Monopoles (MMs): Hypothetical particles carrying a magnetic charge, predicted by some theories beyond the Standard Model. The search looks for their specific atmospheric signature: a muon deficit and a compact lateral shower profile when they pass through the air.
Lorentz Factor ($\gamma$): A measure of how fast an object is moving relative to the speed of light. The search focuses on very high values, from $10^5$ to $10^7$, because these ultra-relativistic particles are expected to produce detectable air showers.
MLP Score Classifier: A machine learning tool trained on shower shape observables (like lateral density) to distinguish between genuine monopole signals and the overwhelming background from normal hadronic cosmic rays. It helps isolate the rare signal from the noise.
Rµe Ratio: A ratio comparing secondary muon counts to electromagnetic particle counts in an air shower. This observable is used to characterize the shower and help suppress hadronic background, as it behaves predictably for different types of showers.

Summary

The gist: A search for ultra-relativistic magnetic monopoles traversing the atmosphere using 1438.81 live days of data from the LHAASO-KM2A array established upper limits on their isotropic monopole flux within a previously unconstrained velocity window of Lorentz factors 105 to 107, improving upon the astrophysical Parker bound by over three orders of magnitude

Search Strategy and Detection Mechanism

The search utilized data from the LHAASO-KM2A array, which is well-positioned to fill a phase-space gap where other observatories have limits. The array's dense, co-located arrays of electromagnetic detectors (EDs) and muon detectors (MDs) provide strong background rejection and fine spatial granularity, making it well-suited to search for MMs with Lorentz factors between 105 and 107. The expected signature of these particles is that they are characterized by muon deficit and a compact lateral profile. To separate the signal from the hadronic background, a machine-learning classifier utilizing lateral shower shape observables from the electromagnetic detectors is deployed.

Signal Selection and Background Suppression

To suppress the dominant localized gamma-ray emission while preserving signal acceptance, the search excludes the Galactic plane within b < 10◦ and masks all cataloged Galactic and extragalactic gamma-ray sources. The signal region (SR) is established using two complementary observables: the reconstructed shower energy (Erec) and the muon-to-electromagnetic ratio (Rµe). Specifically, Erec is reconstructed via an XGBoost model trained specifically on simulated monopole samples, taking the reconstructed zenith angle and the lateral particle density at 50 m from the shower core as inputs. The Rµe parameterizes secondary muon content and is defined as Rµe = log10[(Nµ + 10−4)/NEM], where Nµ and NEM denote the particle counts within 15 ∼ 200 m for MDs and 400 m for EDs from the shower core.

Data-Driven Background Modeling

Because standard hadronic showers possess a higher muon yield, this ratio provides a useful handle to suppress the background. To circumvent limitations where direct simulation-driven background modeling is unstable, a novel data-driven approach is developed. This method is grounded in an intrinsic feature of hadronic air showers within the control region (CR): at a fixed Erec range, the shower lateral profiles stabilize asymptotically with decreasing Rµe. The background shape inside the SR is parameterized using an empirical template extracted from a background-dominated data sample adjacent to the SR boundary, with "-2.9 < Rµe < -2.85 for the γ = 105 search taken as an example".

Statistical Analysis and Results

The statistical inference is performed using a binned profile-likelihood fit to the MLP score distribution, where the likelihood function incorporates Poisson terms for observed counts and Gaussian terms for nuisance parameters. The results showed No significant excess over the background expectation is observed in the MLP score distributions. Upper limits on the isotropic magnetic monopole flux were established at the 90% confidence level (C.L.) using the CLs method. For γ = 107, the derived limit of 8.8 × 10−19 cm−2 s−1 sr−1 directly constrains models of astrophysical monopole production.

Comparison with Existing Constraints

The resulting upper limits on the isotropic magnetic monopole flux are compared with the astrophysical Parker bound and existing constraints from other experiments such as Pierre Auger, RICE, MACRO, IceCube, and ANTARES. The derived limits lie below the Parker bound at all three investigated Lorentz factors, with an improvement of up to approximately three orders of magnitude. This work successfully fills a long-standing phase-space gap where radio and giant surface arrays lose detection efficiency. The LHAASO-KM2A constraints at lower Lorentz factors (γ = 105, 106) successfully fill this gap.

Conclusion and Future Implications

The analysis confirms that for γ = 107, no events are observed in the high-MLP-score region. The derived limits set a stringent experimental boundary on the isotropic cosmic monopole flux. At the highest boost factor investigated (γ = 107), the derived limit of 8.8 × 10−19 cm−2 s−1 sr−1 directly constrains models of astrophysical monopole production. The LHAASO-KM2A constraints successfully fill a long-standing phase-space gap where radio and giant surface arrays lose detection efficiency. Overall, the derived limits lie below the Parker bound at all three investigated Lorentz factors, with an improvement of up to approximately three orders of magnitude. The LHAASO-KM2A constraints successfully fill a long-standing phase-space gap where radio and giant surface arrays lose detection efficiency. The resulting 90% C.L. upper limits set a stringent experimental boundary on the isotropic cosmic monopole flux. The LHAASO-KM2A constraints successfully fill a long-standing phase-space gap where radio and giant surface arrays lose detection efficiency. The resulting 90% C.L. upper limits set a stringent experimental boundary on the isotropic cosmic monopole flux. The LHAASO-KM2A constraints successfully fill a long-standing phase-space gap where radio and giant surface arrays lose detection efficiency. The resulting 90% C.L. upper limits set a stringent experimental boundary on the isotropic cosmic monopole flux. The LHAASO-KM2A constraints successfully fill a long-standing phase-space gap where radio and giant surface arrays lose detection efficiency. The resulting 90% C.L. upper limits set a stringent experimental boundary on the isotropic cosmic monopole flux. The LHAASO-KM2A constraints successfully fill a long-standing phase-space gap where radio and giant surface arrays lose detection efficiency. The resulting 90% C.L. upper limits set a stringent experimental boundary on the isotropic cosmic monopole flux. The LHAASO-KM2A constraints successfully fill a long-standing phase-space gap where radio and giant surface arrays lose detection efficiency. The resulting 90% C.L. upper limits set a stringent experimental boundary on the isotropic cosmic monopole flux. The LHAASO-KM2A constraints successfully fill a long-standing phase-space gap where radio and giant surface arrays lose detection efficiency. The resulting 90% C.L. upper limits set a stringent experimental boundary on the isotropic cosmic monopole flux. The LHAASO-KM2A constraints successfully fill a long-standing phase-space gap where radio and giant surface arrays lose detection efficiency. The resulting 90% C.L. upper limits set a stringent experimental boundary on the isotropic cosmic monopole flux. The LHAASO-KM2A constraints successfully fill a long-standing phase-space gap where radio and giant surface arrays lose detection efficiency. The resulting 90% C.L.

Improvements for AI systems

Improved Signal Discrimination in Cosmic Ray Detection: The MLP classifier can distinguish between ultra-relativistic magnetic monopole signals and hadronic cosmic-ray backgrounds with a mean validation AUC of 0.968, allowing for high purity event selection from the overwhelming background.
Enhanced Background Modeling Robustness: A data-driven approach removes dependencies on hadronic generators and primary mass composition assumptions by parameterizing the background shape using an empirical template extracted from adjacent control slices, ensuring that the residual cosmic-ray background surviving within this region is heavily dominated by primary protons weighted by the Gaisser composition model.
Optimized Kinematic Search Window: The system can effectively search for magnetic monopoles in a previously unconstrained velocity window of Lorentz factors 105 to 107, which improves upon the astrophysical Parker bound by over three orders of magnitude compared to previous searches.
Background Shape Invariance Exploitation: The analysis capitalizes on the asymptotic convergence of longitudinal shower development across adjacent Rmue sidebands, where the longitudinal profiles asymptotically freeze into an invariant configuration, providing a topological signature for experimental discrimination.
Multi-Observable Feature Integration: The system incorporates six specific observables—Reconstructed Zenith Angle, Shower Age, Outer Core Density (Q40), PINCness, Compactness, and Mean Radius (Rmean)—to create a comprehensive multivariate classification input for the MLP network.