SciPost Submission Page
CaloDREAM -- Detector Response Emulation via Attentive flow Matching
by Luigi Favaro, Ayodele Ore, Sofia Palacios Schweitzer, Tilman Plehn
Submission summary
| Authors (as registered SciPost users): | Luigi Favaro · Ayodele Ore · Sofia Palacios Schweitzer |
| Submission information | |
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| Preprint Link: | https://arxiv.org/abs/2405.09629v2 (pdf) |
| Code repository: | https://github.com/heidelberg-hepml/calo_dreamer |
| Data repository: | https://calochallenge.github.io/homepage/ |
| Date submitted: | 2024-05-31 22:40 |
| Submitted by: | Ore, Ayodele |
| Submitted to: | SciPost Physics |
| Ontological classification | |
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| Academic field: | Physics |
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| Approach: | Computational |
Abstract
Detector simulations are an exciting application of modern generative networks. Their sparse high-dimensional data combined with the required precision poses a serious challenge. We show how combining Conditional Flow Matching with transformer elements allows us to simulate the detector phase space reliably. Namely, we use an autoregressive transformer to simulate the energy of each layer, and a vision transformer for the high-dimensional voxel distributions. We show how dimension reduction via latent diffusion allows us to train more efficiently and how diffusion networks can be evaluated faster with bespoke solvers. We showcase our framework, CaloDREAM, on datasets 2 and 3 of the CaloChallenge.
Author indications on fulfilling journal expectations
- Provide a novel and synergetic link between different research areas.
- Open a new pathway in an existing or a new research direction, with clear potential for multi-pronged follow-up work
- Detail a groundbreaking theoretical/experimental/computational discovery
- Present a breakthrough on a previously-identified and long-standing research stumbling block
Current status:
In refereeing