SPADE: Split-and-Delay Embeddings for Autoregressive High-Granularity Calorimeter Simulation
arXiv:2606.11304v1 Announce Type: cross Abstract: We introduce SPADE (SPlit And Delay Embeddings), an autoregressive transformer for sequences whose tokens carry multiple features. Rather than embedding these features jointly, SPADE embeds them independently. Delaying each feature stream relative to the previous one allows intra-token correlations to be learned by the standard self-attention mechanism. Applied to point-cloud calorimeter shower generation in the highly granular ILD detector, SPADE is competitive with the state of the art AllShowers model on photon showers, and substantially out
The proliferation of advanced AI models has driven research into more efficient and accurate simulation techniques, especially in complex scientific fields like high-energy physics where traditional methods are computationally expensive.
This development indicates a significant advancement in AI's capacity for high-granularity scientific simulation, potentially accelerating discoveries in fields demanding precise data generation like fundamental physics and materials science.
The ability to simulate complex physical phenomena with greater accuracy and efficiency using AI models will reduce reliance on costly experimental setups and traditional computational methods, speeding up research cycles.
- · High-energy physics research
- · AI research & development
- · Scientific simulation software providers
- · Traditional simulation software
- · Experimental facility budget constraints
More accurate and faster simulation of particle showers for detector design and analysis in high-energy physics.
Accelerated development of new detector technologies and experimental methodologies due to rapid prototyping through AI simulation.
Broader adoption of similar AI-driven simulation techniques across other scientific and engineering disciplines facing complex multi-feature data generation challenges.
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Read at arXiv cs.LG