| Title: |
Fast Calorimeter Simulation Challenge 2022 - Submissions Dataset 3 |
| Authors: |
Faucci Giannelli, Michele; Kasieczka, Gregor; Krause, Claudius; Nachman, Benjamin; Salamani, Dalila; Shih, David; Zaborowska, Anna; Amram, Oz; Borras, Kerstin; Buckley, Matthew; Buhmann, Erik; Buss, Thorsten; Chernyavskaya, Nadezda; Da Costa Cardoso, Renato Paulo; Diefenbacher, Sascha; Eren, Engin; Ernst, Florian; Favaro, Luigi; Gaede, Frank; Hsu, Shih-Chieh; Jaruskova, Kristina; Käch, Benno; Korcari, William; Korol, Anatolii; Krücker, Dirk; Krüger, Katja Sophia; Liu, Qibin; Liu, Xiulong; McKeown, Peter; Melzer-Pellmann, Isabell-Alissandra; Mikuni, Vinicius; Ore, Ayodele; Palacios Schweitzer, Sofia; Pang, Ian; Pedro, Kevin; Plehn, Tilman; Pokorski, Witold; Raikwar, Piyush; Scham, Moritz Alfons Wilhelm; Schnake, Simon; Shlizerman, Eli; Shimmin, Chase; Shu, Li; Srivatsa, Mudhakar; Tsolaki, Kalliopi; Vallecorsa, Sofia |
| Publisher Information: |
Zenodo |
| Publication Year: |
2025 |
| Collection: |
Zenodo |
| Subject Terms: |
CaloChallenge; Generative Model; Calorimeter Simulation |
| Description: |
These are all the submitted samples to dataset 3 of the “Fast Calorimeter Simulation Challenge 2022”. They each consist of 100k calorimeter showers of electrons with energies sampled from a log-uniform distribution ranging from 1 GeV to 1 TeV. The training data (based on Geant4) can be found at https://doi.org/10.5281/zenodo.6366324, the paper describing the results is available on arXiv:2410.21611, and further details, in particular helper scripts to parse the data and calculate and visualize basic high-level physics features, are available at https://calochallenge.github.io/homepage/. The subscripts in the file names corresponds to the individual submissions: ID number Submission name Original reference _1 CaloDiffusion arXiv:2308.03876 _2 L2LFlows-MAF arXiv:2302.11594, arXiv:2405.20407 _3 conv. L2LFlows arXiv:2405.20407 _5 MDMA arXiv:2305.15254, arXiv:2408.04997 _6 CaloClouds arXiv:2305.04847, arXiv:2309.05704 _7 Calo-VQ arXiv:2405.06605 _9 CaloScore distilled arXiv:2206.11898, arXiv:2308.03847 _10 CaloScore single-shot arXiv:2206.11898, arXiv:2308.03847 _13 iCaloFlow teacher arXiv:2305.11934 _14 iCaloFlow student arXiv:2305.11934 _21 Geant4-Transformer DOI _23 CaloPointFlow arXiv:2403.15782 _27 CaloVAE+INN arXiv:2312.09290 _31 Calo-VQ(norm) arXiv:2405.06605 _33 CaloDREAM arXiv:2405.09629 The samples here can be used to reproduce the results of arXiv:2410.21611 and as benchmarks for new models after the challenge concluded. |
| Document Type: |
dataset |
| Language: |
unknown |
| Relation: |
https://zenodo.org/records/15962527; oai:zenodo.org:15962527; arXiv:2410.21611; https://doi.org/10.5281/zenodo.15962527 |
| DOI: |
10.5281/zenodo.15962527 |
| Availability: |
https://doi.org/10.5281/zenodo.15962527; https://zenodo.org/records/15962527 |
| Rights: |
Creative Commons Attribution 4.0 International ; cc-by-4.0 ; https://creativecommons.org/licenses/by/4.0/legalcode |
| Accession Number: |
edsbas.AF3BF9DA |
| Database: |
BASE |