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Detection of TeV emission during early afterglow from poorly localized GRBs with ground based IACTs

This paper proposes and evaluates optimized rapid tiling follow-up strategies for next-generation Imaging Atmospheric Cherenkov Telescopes, demonstrating that such approaches can significantly increase the detection rate of TeV emission from poorly localized Gamma-Ray Bursts during their early afterglow phase compared to traditional methods.

Original authors: S. Macera, B. Banerjee, M. Seglar-Arroyo, J. Green, G. Oganesyan, P. Tiwari, A. Ierardi, M. Branchesi, F. Aharonian, S. Mohnani, D. Miceli, F. Schüssler, A. Berti

Published 2026-04-10

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CC BY 4.0

Original authors: S. Macera, B. Banerjee, M. Seglar-Arroyo, J. Green, G. Oganesyan, P. Tiwari, A. Ierardi, M. Branchesi, F. Aharonian, S. Mohnani, D. Miceli, F. Schüssler, A. Berti

Original paper licensed under CC BY 4.0 (http://creativecommons.org/licenses/by/4.0/). ✨ This is an AI-generated explanation of the paper below. It is not written or endorsed by the authors. For technical accuracy, refer to the original paper. Read full disclaimer

Imagine the universe is a giant, dark ocean, and Gamma-Ray Bursts (GRBs) are like massive, sudden underwater explosions. They are the brightest things in the cosmos, flashing for a few seconds and then fading away.

For a long time, scientists could only see the "splash" of these explosions in low-energy light (like visible light or X-rays). But recently, we've started catching glimpses of the "shockwave" in super-high-energy light (called TeV radiation). The problem? These shockwaves fade incredibly fast, and we often don't know exactly where the explosion happened.

This paper is a blueprint for how next-generation telescopes can catch these fleeting, high-energy flashes, even when the explosion's location is a mystery.

Here is the breakdown of their plan, using some everyday analogies:

1. The Problem: The "Fuzzy Flashlight"

Imagine you are trying to take a photo of a firefly that just blinked in a massive, pitch-black field.

The Good News: You have a satellite (Fermi/GBM) that sees the blink and shouts, "It's somewhere in this huge field!" But the field is 100 square miles wide.
The Bad News: Your camera (a ground-based telescope) has a very narrow lens. It can only see a tiny patch of sky at a time. If you point it randomly, you'll likely miss the firefly.
The Race: The firefly's light is fading fast. If you spend too much time figuring out where to point, the light will be gone.

2. The Solution: The "Mowing the Lawn" Strategy

The authors propose a new way to hunt these bursts. Instead of waiting for a perfect map of the firefly's location, they suggest rapidly tiling the area.

Think of it like mowing a large, overgrown lawn where you know a rare flower is hiding, but you don't know exactly where.

Old Way: Wait for a botanist to give you the exact GPS coordinates. If they are late, the flower might have wilted.
New Way: Send a team of mowers to cut the grass in a grid pattern across the whole suspected area immediately. You cut a small patch, move, cut the next, and so on. Even if you don't know the exact spot, you cover the whole lawn quickly.

In the paper, this is called "rapid tiling." The telescopes will quickly scan the large, fuzzy error zone provided by the satellite, taking short snapshots (2 to 5 minutes) of different patches until they find the source.

3. The Simulation: A "Practice Run"

Since we can't predict exactly when or where the next explosion will happen, the scientists built a virtual universe (a simulation).

They took data from 16 years of real explosions.
They created 220 "fake" explosions with realistic brightness, distance, and fuzzy locations.
They ran their "mowing the lawn" strategy against these fake explosions to see how many they could catch.

4. The Results: Catching More with Less

The simulation showed that this strategy works surprisingly well:

Speed is Key: The telescopes need to start scanning within about 15–30 minutes of the explosion. After an hour, the light is usually too dim to see.
The Payoff: By scanning the big, fuzzy areas, these telescopes (like the future CTAO, ASTRI, and LACT) could detect 1 to 4 new high-energy bursts every year.
Comparison: If they only waited for perfectly precise locations (which are rare), they would catch far fewer. This strategy doubles or even quadruples their chances of success.

5. Why This Matters

Catching these high-energy flashes is like finding the "smoking gun" of the explosion. It helps scientists understand:

How stars die and black holes are born.
How particles are accelerated to near the speed of light.
The nature of the universe itself.

The Bottom Line

This paper is a game plan for the next generation of telescopes. It says: "Don't wait for the perfect map. Grab a flashlight, run around the whole neighborhood quickly, and you'll catch the firefly before it goes dark."

By being fast and covering a lot of ground, we can unlock secrets about the most violent events in the universe that we would otherwise miss.

1. Problem Statement

Gamma-ray bursts (GRBs) are among the most luminous transients in the universe. While space-based instruments (e.g., Fermi/GBM, Swift/BAT) detect thousands of GRBs annually, the detection of their Very-High-Energy (VHE; $E > 100$ GeV) counterparts by ground-based Imaging Atmospheric Cherenkov Telescopes (IACTs) remains challenging.

Localization Mismatch: Swift/BAT provides precise localization (arcminutes), but detects only $\sim$ 80 GRBs/year. Fermi/GBM detects $\sim$ 240 GRBs/year but provides poor localization (tens to hundreds of square degrees).
Observational Constraints: VHE emission is transient (decaying rapidly), attenuated by the Extragalactic Background Light (EBL), and IACTs have low duty cycles ( $\sim$ 10%) and limited fields of view (FoV).
The Gap: Current follow-up strategies prioritize well-localized events, ignoring the vast majority of poorly localized GRBs detected by MeV monitors, potentially missing a significant fraction of detectable VHE signals.

2. Methodology

The authors developed a comprehensive simulation framework to evaluate the feasibility of detecting VHE emission from poorly localized GRBs using next-generation IACTs (CTAO-N, ASTRI, LACT).

A. Synthetic GRB Population Construction

To create a realistic sample representing one year of Fermi/GBM observations, the authors combined empirical data from three catalogs:

P1 (Redshifts): 697 GRBs with measured redshifts from the Greiner catalog.
P2 (Fluences): 3,807 GRBs detected by Fermi/GBM (2008–2024) to establish fluence ( $S_\gamma$ ) distributions.
P3 (Joint Detections): 370 GRBs jointly detected by Swift/XRT and Fermi/GBM to provide high-precision localization and X-ray light curve templates.

Synthesis Process (P5):

Randomly sampled 500 pairs of ( $S_\gamma$ , $z$ ) from P2 and P1.
Calculated isotropic equivalent energy ( $E_{\gamma, iso}$ ) and applied a detectability cut based on the Fermi/GBM sensitivity limit.
Matched surviving pairs to real events in P3 to inherit realistic sky localization (GBM error circles) and XRT afterglow light curves.
Result: A mock catalog (P5) of 220 long GRBs representing one year of observations, including fluence, redshift, $E_{\gamma, iso}$ , GBM localization, and XRT localization.

B. Afterglow Modeling

Physics: Used the Lepto-Hadronic Modeling Code (LeHaMoC) with a Synchrotron Self-Compton (SSC) framework.
Parameters: Assumed a wind-like circumburst medium, standard microphysical parameters ( $\epsilon_e = 0.1$ , $\epsilon_B \sim 10^{-4}$ , $p \sim 2.3$ ), and 10% prompt emission efficiency.
Attenuation: Applied EBL absorption (Domínguez et al. 2011 model) to intrinsic spectra to generate observed fluxes.
Validation: The model reproduced the correlation between X-ray and VHE light curves observed in known TeV GRBs (e.g., GRB 190114C, GRB 221009A).

C. Observational Strategy & Simulation

Instruments: Simulated CTAO-N (Alpha configuration), ASTRI, and LACT.
Tiling Strategy: Developed a rapid tiling approach to cover large GBM error regions (1 $\sigma$ and 2 $\sigma$ ).
Tools:
- tilepy: For scheduling observation pointings based on GCN alerts (Flight, Ground, Final notices).
- sensipy: For calculating detection significance using Instrument Response Functions (IRFs) and time-dependent spectra.
Variables: Varied latency ( $t_L$ ), exposure time ( $t_{exp}$ ), and FoV radius (2.5° vs. 4°).

3. Key Contributions

Optimized Tiling Strategy: Proposed a systematic method to tile large localization regions (up to hundreds of square degrees) using rapid, short-duration pointings (2–5 minutes) to maximize coverage before flux decay.
Realistic Mock Catalog: Created a statistically robust synthetic population (P5) that integrates real localization uncertainties, alert delays, and afterglow light curves, moving beyond idealized point-source assumptions.
Quantification of "Poorly Localized" Potential: Demonstrated that targeting the 85% of GRBs that are not well-localized by Swift can significantly boost VHE detection rates compared to current strategies.
Latency Sensitivity Analysis: Quantified the critical impact of observation latency, showing that delays $>30$ minutes render nearly the entire population undetectable.

4. Key Results

Detection Rates

CTAO-N: The proposed tiling strategy yields 1 to 4 VHE detections per year for poorly localized GRBs. This is comparable to or exceeds the expected rate from well-localized Swift triggers alone (which is limited by the lower trigger rate of BAT).
ASTRI & LACT: These instruments also show enhanced detection rates (up to a factor of 2 increase compared to strategies limited to well-localized events) due to their larger FoVs.
Energy Dependence: Detection rates are higher at lower energies (200 GeV) compared to 1 TeV due to EBL attenuation and lower energy thresholds.

Observational Constraints

Latency: Detection probability drops sharply after $t_L \approx 30$ minutes. For optimal results, observations must begin within $\sim$ 10–15 minutes.
Exposure Time: Shorter exposures (2 minutes) are preferred over longer ones (5 minutes). Because GRB flux decays faster ( $t^{-1}$ ) than the sensitivity improves ( $t^{-0.5}$ ), rapid coverage of the error region is more efficient than deep integration on a single tile.
Tiling Efficiency: Using a 4° FoV (MSTs on CTAO) significantly outperforms a 2.5° FoV (LSTs) for covering large error regions.
Cuts: Applying cuts (Zenith $< 50^\circ$ , Latency $< 30$ min) eliminates 75% of non-productive follow-ups without losing detectable events.

Light Curve Behavior

The simulated VHE light curves show a strong correlation with X-ray light curves at early times.
At later times ( $>3$ ks), the VHE flux declines more rapidly than X-rays, likely due to the decreasing efficiency of Inverse-Compton scattering as the blast wave slows.

5. Significance

Paradigm Shift: This work argues for a shift in IACT observing strategies from "wait for precise localization" to "rapid tiling of large error regions." This is crucial for maximizing the scientific return of next-generation telescopes like CTAO.
Maximizing TeV Science: By leveraging the high trigger rate of MeV monitors (like Fermi/GBM and future missions like Crystal Eye or GRINTA), the community can increase the discovery rate of TeV GRBs by a factor of 2–4.
Multi-messenger Synergy: Improved detection rates of poorly localized GRBs will enhance the ability to correlate VHE emission with gravitational wave events (e.g., neutron star mergers) where localization is often poor.
Future-Proofing: The methodology provides a general framework applicable to future MeV missions that may offer even higher trigger rates but potentially different localization characteristics.

In conclusion, the paper demonstrates that with optimized rapid-tiling strategies and the superior FoV of next-generation IACTs, the detection of TeV emission from the poorly localized majority of GRBs is not only feasible but essential for a complete understanding of GRB physics.