University of Edinburgh
boosted di-boson resonance search

The analysis

Boosted di-boson resonance search with mass-decorrelated jet tagging

The search targets a heavy resonance X decaying to a pair of electroweak bosons (H → ZV, W′ → WZ, Z′ → ZH) in the boosted regime, where the decay products of each boson merge into a single large-radius fat jet. The analysis reconstructs two fat jets and searches for a bump in the reconstructed di-jet invariant mass m_jj above a smoothly-falling QCD background.

The mass sculpting problem is acute in this topology. A boosted-object tagger trained to separate W/Z/H jets from QCD will learn the jet mass as a discriminating feature: signal jets have a characteristic mass around 80–125 GeV; QCD jets have a steeply falling mass distribution. Any cut on the tagger score preferentially removes low-mass QCD background, carving a bump-shaped enhancement into the m_jj spectrum at the signal hypothesis. A bump-hunt that treats the tagger as mass-decorrelated will attribute this enhancement to a new resonance. The significance is fake.

The existing analysis pipeline uses a neural network tagger trained on eight high-level kinematic variables per fat jet: lep1_pt, lep2_pt, fatjet_pt, fatjet_eta, fatjet_D2, Zll_mass, Zll_pt, MET. Vikshep replaces this tagger with constituent-level wavelet scattering features plus the DisCo mass-decorrelation penalty. The physics reach is measured by two numbers against the same Wilks Δχ² significance pipeline the analysis already uses.

The data

Geant4-simulated ATLAS samples

The pilot runs on Geant4-simulated ATLAS detector response with full detector geometry. Signal samples cover gg → H → ZV at four resonance mass points (700, 1000, 1500, 2000 GeV). Background samples include diboson production (WW, WZ, ZZ), Z+jets (with up to four additional partons), and semi-leptonic top-quark pair production.

Each fat jet is reconstructed with the anti-k_T algorithm at R=1.0 and trimmed with f_cut=0.05. Constituent four-vectors are read directly from the ROOT TTree output of the ATLAS analysis framework. The jet image rasterisation is handled by the Vikshep ingest step at 64×64 bins in (η, φ) centred on the fat jet axis.

The benchmark

Two numbers. Apples-to-apples.

The benchmark is computed against the analysis’s own Wilks Δχ² significance pipeline — the same code the collaboration uses to quote significance in the paper. No new significance metric, no cherry-picked working point. Two numbers are reported:

Success criterion: Vikshep achieves equal or higher Δσ at strictly lower ΔJSD. A higher-significance result that sculpts more than the NN baseline is not a success — it would trade a physics guarantee for a statistical number. The goal is to demonstrate that the two objectives are not in tension: you can have both.

Status & results

Currently in progress.

Reproduce

The full harness is in the repo.

The pilot analysis harness — data loading, scattering configuration, DisCo training loop, and Wilks significance pipeline — lives in Vikshep/pilot/ in the GitHub repository. To reproduce:

1. Clone the repo: git clone https://github.com/samvardhan03/Vikshep
2. Install dependencies: pip install vikshep && cargo install omnipulse-mcp
3. Obtain the Geant4 samples (see pilot/README.md for access instructions)
4. Run the harness: bun run pilot/run.ts --config pilot/uoe-diboson.yaml
5. Results are written to pilot/results/ as JSON + ROOT files

Acknowledgements

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