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Getting High: High Fidelity Simulation of High Granularity Calorimeters with High Speed paper by Independent / Community
⭐ 70.5
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Paper

Getting High: High Fidelity Simulation of High Granularity Calorimeters with High Speed

by Independent / Community 0021a4983c11c284b7dcca869e26a64287957ab3
Free2AITools Nexus Index
70.5
S: Semantic 50

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A: Authority 88
P: Popularity 65
R: Recency 100
Q: Quality 65
Tech Context
Vital Performance —

Accurate simulation of physical processes is crucial for the success of modern particle physics. However, simulating the development and interaction of particle showers with calorimeter detectors is a time consuming process and drives the computing needs of large experiments at the LHC and future colliders. Recently, generative machine learning models based on deep neural networks have shown promise in speeding up this task by several orders of magnitude. We investigate the use of a new archi...

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Semantic Scholar 141 Citations
Paper Information Summary
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Registry ID 0021a4983c11c284b7dcca869e26a64287957ab3
License ArXiv
Provider semantic_scholar
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Cite this paper

Academic & Research Attribution

BibTeX 
@misc{0021a4983c11c284b7dcca869e26a64287957ab3,
  author = {Unknown},
  title = {Getting High: High Fidelity Simulation of High Granularity Calorimeters with High Speed Paper},
  year = {2026},
  howpublished = {\url{https://api.semanticscholar.org/0021a4983c11c284b7dcca869e26a64287957ab3}},
  note = {Accessed via Free2AITools.}
}
APA Style
Unknown. (2026). Getting High: High Fidelity Simulation of High Granularity Calorimeters with High Speed [Paper]. Free2AITools. https://api.semanticscholar.org/0021a4983c11c284b7dcca869e26a64287957ab3

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Full Specifications [+]

βš–οΈ Free2AITools Nexus Index V2.0

Methodology How FNI works
Semantic (S) 50

Query-time baseline · scored live at search

Authority (A) 88
Popularity (P) 65
Recency (R) 100
Quality (Q) 65

πŸ’¬ Index Insight

FNI V2.0 for Getting High: High Fidelity Simulation of High Granularity Calorimeters with High Speed: Authority (A:88), Popularity (P:65), Recency (R:100), Quality (Q:65). Semantic (S) is a query-time baseline scored live at search.

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πŸ“ Executive Summary

"Accurate simulation of physical processes is crucial for the success of modern particle physics. However, simulating the development and interaction of particle showers with calorimeter detectors is a time consuming process and drives the computing needs of large experiments at the LHC and future colliders. Recently, generative machine learning models based on deep neural networks have shown promise in speeding up this task by several orders of magnitude. We investigate the use of a new archi..."

❝ Cite Node 

@article{Unknown2026Getting,
  title={Getting High: High Fidelity Simulation of High Granularity Calorimeters with High Speed},
  author={},
  note={Indexed by Free2AITools},
  year={2026}
}

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πŸ“Š Research Signals

πŸ“ˆ141CitationsSemantic Scholar
πŸ›οΈ88AuthorityFNI pillar
⏱️100RecencyFNI pillar
βœ…65QualityFNI pillar
πŸ—‚οΈtext generationField
πŸ“¦Data Source: semantic_scholar
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πŸ†” Identity & Source

source
semantic_scholar
author
Unknown
license
ArXiv
tags
paper, research, academic

βš™οΈ Technical Specs

architecture
null
params billions
null
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