arXiv (All)
open access
Reconstruct everything everywhere all at once: An end-to-end model for future colliders
We present the Reconstruct Everything Model (REM), an end-to-end multitask model for sparse, multimodal sensor data, demonstrated in high-energy physics. To our knowledge, REM is the first demonstration of a single learned framework simultaneously reconstructing multiple particle types directly from
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Exploring Futures for Particle Physics#1482103
- Toward an artificial intelligence physicist for unsupervised learning#964486
- EveNet: A Foundation Model for Particle Collision Data Analysis#1490430
- Generative machine learning for detector response modeling with a conditional normalizing flow#780547
- Cross-Detector Transfer Learning with Parnassus: From ALEPH to SLD#1051554
Record · ID 1535207
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive