Prospects for the detection of gamma rays using Cherenkov telescopes enhanced by a ground array observatory

We consider the Single-Mirror Small-Size imaging atmospheric Cherenkov Telescopes (SST-1M) to be located inside a high-altitude array of Water-Cherenkov Detectors (WCDs) inspired by the Southern Wide-field Gamma-ray Observatory (SWGO). For such a hybrid observatory, using detailed Monte Carlo simulations, we show an improvement in the flux sensitivity of monocular and stereoscopic SST-1M observation by about 60% and 30% above 10 TeV, respectively, due to the improved gamma/hadron separation when additional parameters from the WCD array are used. We also discuss further benefits of the hybrid SWGO concept and its technical challenges.

💡 Research Summary

The paper investigates a hybrid observatory concept that combines Single‑Mirror Small‑Size Imaging Atmospheric Cherenkov Telescopes (SST‑1M) with a dense array of Water‑Cherenkov Detectors (WCDs) inspired by the Southern Wide‑field Gamma‑ray Observatory (SWGO). Using a three‑stage Monte Carlo chain—CORSIKA for air‑shower generation, sim_telarray for the optical and electronic response of the SST‑1M telescopes, and a simplified WCD model derived from previous SWGO studies—the authors simulate a realistic high‑altitude site (4700 m a.s.l., Pampa la Bola, Chile). Two identical SST‑1M telescopes are placed 110 m apart, centered within a 1 km² WCD array consisting of 9 997 cylindrical tanks (12.6 m² each) arranged on a triangular grid with a fill factor of 12.5 %.

The key innovation is the exploitation of two γ/h separation parameters extracted from the WCD array: LCₘ, which quantifies azimuthal fluctuations of the ground signal, and Pα_tail, which measures the fraction of detectors with signals significantly above the average at a given core distance. Both variables are strongly correlated with the muon content of the shower—muons are abundant in hadronic cascades but scarce in electromagnetic (γ‑induced) cascades. The authors incorporate LCₘ and Pα_tail as additional features in a Random Forest (RF) classifier that already uses the standard Hillas‑derived image parameters from the SST‑1M cameras.

Performance is evaluated for both monocular (single‑telescope) and stereoscopic (two‑telescope) observation modes. Receiver Operating Characteristic (ROC) curves show that adding the WCD information raises the area‑under‑curve (AUC) from 0.91 to 0.99 for the monocular case and from 0.93 to 0.98 for the stereoscopic case, indicating a dramatic improvement in background rejection. Feature‑importance (Gini) analysis reveals that LCₘ is the most discriminating WCD variable, followed by Pα_tail, while the true muon count (available only in simulation) would be the ultimate discriminator.

Sensitivity calculations follow the standard CTA/IACT prescription: a 5σ detection, at least ten excess events, and a signal‑to‑background ratio between 0.1 and 10, assuming 50 h of observation at a zenith angle of 20°. With these criteria, the hybrid system improves the differential flux sensitivity above 10 TeV by roughly 60 % for the monocular configuration (from ~2 × 10⁻¹² TeV cm⁻² s⁻¹ to ~8 × 10⁻¹³ TeV cm⁻² s⁻¹) and by about 30 % for the stereoscopic configuration. The gain originates primarily from a more efficient suppression of hadronic background, thanks to the muon‑sensitive WCD parameters.

Beyond the quantitative results, the paper discusses scientific opportunities (e.g., deeper surveys of PeVatrons, improved studies of Galactic supernova remnants, and transient monitoring) and technical challenges. High‑altitude operation demands robust power distribution, reliable data‑link bandwidth, and synchronized triggering between the IACTs and the WCD array. The authors note that the dense WCD layout used in the simulations is deliberately over‑engineered to minimize edge effects; a realistic deployment would likely employ a graded fill factor to balance cost and performance across the energy range.

In conclusion, the study demonstrates that a hybrid SST‑1M + SWGO‑like WCD array can substantially enhance very‑high‑energy gamma‑ray detection capabilities, especially above 10 TeV, by leveraging muon‑based background discrimination. The results provide a compelling physics case for pursuing such a combined instrument, while also outlining the next steps—prototype testing, refined muon‑reconstruction algorithms (potentially using deep learning), and cost‑performance optimization—required to turn the concept into a working observatory.