We lifted 6 functions out of this paper's own repositories and ran 4 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| elsaeessc/Deep-Top-based-CNN | reimplementation | 4 of 6 |
| Function | Status | Where it lives |
|---|---|---|
| eta | Ran | elsaeessc/Deep-Top-based-CNN/preprocessing.py pointer only (licence: NONE) · get_code("ff97a2e584d9db26") |
| orig_image | Ran | elsaeessc/Deep-Top-based-CNN/preprocessing.py pointer only (licence: NONE) · get_code("102b394ad2e2774c") |
| phi | Ran | elsaeessc/Deep-Top-based-CNN/preprocessing.py pointer only (licence: NONE) · get_code("5cdbbda47e910667") |
| to_image | Ran | elsaeessc/Deep-Top-based-CNN/Train_Top_Tagging.py pointer only (licence: NONE) · get_code("00f853ecdcc3ee74") |
| Predictor_Vier | Not yet run | elsaeessc/Deep-Top-based-CNN/Train_Top_Tagging.py pointer only (licence: NONE) · get_code("21e5cc0c7ccf14ce") |
| Predictor_Zwolf | Not yet run | elsaeessc/Deep-Top-based-CNN/Train_Top_Tagging.py pointer only (licence: NONE) · get_code("9ce218eb0434254a") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Machine learning based on convolutional neural networks can be used to study jet images from the LHC. Top tagging in fat jets offers a well-defined framework to establish our DeepTop approach and compare its performance to QCD-based top taggers. We first optimize a network architecture to identify top quarks in Monte Carlo simulations of the Standard Model production channel. Using standard fat jets we then compare its performance to a multivariate QCD-based top tagger. We find that both approaches lead to comparable performance, establishing convolutional networks as a promising new approach for multivariate hypothesis-based top tagging.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("1701.08784")
get_code_for_paper("1701.08784")
have("1701.08784")
Connect an agent — have() is free.