Scientific compute infrastructure
Deterministic features for physics. Nothing learned, nothing leaked.
pip install vikshepAGPL-3.0 + Commercial · open source
A signal, decomposed across scales.
Fixed filters, no training. S₁[λ] = |x ⋆ ψλ| ⋆ φJ — each row is one scale band. Bright cells are where the signal has energy. Nothing is learned.
Why every modern tagger quietly breaks discovery
A neural net trained on jet features learns the jet mass — and your bump-hunt with it.
ATLAS / CMS
Mass decorrelation is a top concern in every boosted-object analysis
HL-LHC 2030
Restart with ×5 luminosity, ×10 pile-up, an order of magnitude more data
BSM searches
Anomaly detection is the field's response to not knowing what to look for
Two ideas, one guarantee
Deterministic features. Closed-form decorrelation. Reusable across every scientific domain.
Deterministic feature extraction
Fixed analytic Morlet wavelets cascade through the data — translate, modulate, pool. No learned weights means no mass leakage by construction. r₂ = S₂/S₁ is dimensionless and scale-invariant.
Mathematical decorrelation
A weighted distance-correlation penalty enforces statistical independence between the tagger output and the resonance mass. dCorr = 0 is a closed-form guarantee — not a heuristic.
r₂ = S₂/S₁. r₂ is dimensionless — it can't carry an energy scale. It's the scattering analogue of D2, generalized to a full basis.
One engine, every dimension
SE(2) for 2-D jet images. SO(3) for 3-D density fields. 1-D Morlets for GW strain. The (Dim, Group, J, Q, L) parameters are runtime config — not a rebuild.
The filter bank
J scales, L orientations — fixed by design.
Each cell shows the energy of one Morlet wavelet in (scale j, orientation θ) space. Toggle J and L to explore coverage — no recompilation needed.
4×8 filter bank · 32 wavelets
The bank that the engine uses. Fixed analytic Morlets at log-spaced scales and uniformly-spaced orientations. Filters never change — that's the point.
The mass-sculpting problem
NN taggers sculpt. r₂ doesn't.
Every standard jet classifier correlates with m_jj — cutting on it deforms the background mass spectrum. The scattering ratio r₂ = S₂/S₁ is mass-decorrelated by construction. DisCo makes it rigorous.
0.958
AUC
signal vs bkg
2.69
JSD ×10³
mass sculpting
The standard NN tagger is correlated with jet mass — cutting on it sculpts the background spectrum (JSD rises). The Vikshep r₂ tagger with DisCo penalty (λ) keeps the post-cut shape flat. AUC quantifies discrimination power; JSD quantifies sculpting. Drag the sliders to see the tradeoff.
Three recipes, one engine
Drop in a pipeline. Or write your own.
HEP Tagging (DisCo)
Mass-decorrelated boosted-object tagging.
Ingest jet constituents or calorimeter images, extract r₂ substructure features via 2-D oriented scattering, train the classifier under a distance-correlation penalty on the resonance mass. Background shape preserved; tagging efficiency competitive with the best learned taggers.
bun run main.ts process --request "tag jets, decorrelate mass"BSM Anomaly Detection
Template-free new-physics search.
Embed every event in the scattering feature space; index the SM background in an HNSW graph with Sliced-Wasserstein distance; flag events beyond a calibrated distance from the SM manifold. No signal model, no retraining.
bun run main.ts process --request "find events that don't look like SM"General Feature Extraction
Cosmology, plasma, GW, hydrodynamics.
Pass any {dim, group, J, Q, L} config at runtime. 1-D for transient time series. 2-D for lensing/CMB. 3-D for density fields and plasma simulations. Same engine, same MCP tool, different physics.
bun run main.ts process --request "extract rotation-invariant features"Fast-Sim Validation
Preview · Q3 2026
A principled fidelity metric for ML-generated Geant4 surrogates — Wasserstein distance between scattering distributions of generated vs full-simulation showers. Decomposable by scale and orientation.
Coming Q3 — Talk to founders →How it works
The full stack, from TypeScript to CUDA.
Every arrow is one of: a 28-char hex OID, a \n-terminated JSON frame, or a registered host page.
Where this earns its license
Built for analyses where the statistical guarantee is the publishable result.
Particle physics
Mass-decorrelated jet tagging for ATLAS, CMS, and HL-LHC-era analyses. Validated against your own Wilks Δχ² significance pipeline.
Wilks Δχ² · JSD-preserved background · zero data egress
Cosmology & astrophysics
Rotation-invariant features for weak-lensing maps, CMB patches, and 3-D density fields. Second-order coefficients capture non-Gaussian structure the power spectrum misses.
SE(2) and SO(3) invariance · GPU-accelerated
Simulation science
Solid-harmonic scattering for SO(3)-covariant features of plasma, nuclear, and hydrodynamics fields. Runtime (Dim, Group) config — no rebuild per domain.
ROOT, HDF5, VTK loaders · no model retraining
Custom deployment · Contract-based · SLA committed
Talk to founders →No black box
Every coefficient is derivable from the input. Every guarantee is provable.
Currently in the wild
University of Edinburgh — boosted di-boson resonance search
In partnership with a nuclear-physics researcher at the University of Edinburgh, Vikshep is running on Geant4-simulated ATLAS data, replacing a neural-network tagger trained on eight high-level kinematic variables. Two numbers will be reported: the significance gain (Δσ) over the NN baseline, and the reduction in background mass-shape distortion (ΔJSD). Results: Q1 2026.
Pilot details →Get started in three steps
Try it in 90 seconds.
# 1. Install the engine (binary wheel) pip install vikshep # 2. Install the Rust orchestrator cargo install omnipulse-mcp # 3. Run a recipe export OMNIPULSE_MCP_BIN=$(which omnipulse-mcp) bun run vikshep/agent/src/main.ts process \ --input data/jets.root \ --recipe hep-tagging-disco
Free for research and open-source use under AGPL-3.0. Commercial or proprietary deployment requires a commercial license — talk to founders.
Run it yourself
AGPL-3.0. Open-source. GPU-accelerated binary wheels on PyPI. Works on your hardware, in your facility.
GitHub →Or have us run it with you
We integrate into your existing analysis pipeline, validate against your significance benchmark, and deliver the two numbers that matter.
Talk to founders →Founders & creators
The people behind the plane
ARCHITECT — APPLIED AI / MLOPS
Samvardhan Singh
Automation Engineering & AI/MLOps Research, NielsenIQ
Automation engineering, AI/MLOps pipelines, engineering outcomes.
ARCHITECT — SYSTEMS / OPTIMAL TRANSPORT
Yash Mishra
Senior Software Engineer, Bajaj Finserv
Concurrent systems, optimal transport, real-time indexing logic.
RESEARCH PARTNER — ATLAS / DIBOSON PILOT
Komal Papanwar
MS Nuclear & Particle Physics, University of Edinburgh
Leads the ATLAS boosted di-boson resonance pilot analysis on Geant4-simulated data.