Summary of Fast Bayesian Inference For Neutrino Non-standard Interactions at Dark Matter Direct Detection Experiments, by Dorian W. P. Amaral et al.
Fast Bayesian Inference for Neutrino Non-Standard Interactions at Dark Matter Direct Detection Experiments
by Dorian W. P. Amaral, Shixiao Liang, Juehang Qin, Christopher Tunnell
First submitted to arxiv on: 23 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: High Energy Physics – Phenomenology (hep-ph); Machine Learning (stat.ML)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Multi-dimensional parameter spaces are commonly encountered in physics theories that go beyond the Standard Model. Recent innovations, such as GPU acceleration, automatic differentiation, and neural-network-guided reparameterization, have made navigating these complex posteriors possible. We apply these advancements to dark matter direct detection experiments, exploring non-standard neutrino interactions and benchmarking their performances against traditional nested sampling techniques. Compared to nested sampling alone, we find that these techniques increase performance for both nested sampling and Hamiltonian Monte Carlo, accelerating inference by factors of ~100 and ~60, respectively. These advancements can be exploited to improve model comparison performance while retaining compatibility with existing implementations widely used in the natural sciences. We perform a scan in the neutrino non-standard interactions parameter space, allowing all parameters to vary simultaneously. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Navigating complex spaces is important for understanding physics theories beyond the Standard Model. Some recent ideas are helping us do this more efficiently. One example is using computers with special chips (GPUs) to speed up calculations. We applied these ideas to dark matter experiments and found that they can make calculations go faster by a lot – 100 times or more. This could help us better understand what’s out there in the universe. |
Keywords
» Artificial intelligence » Inference » Neural network
