Pythia8 Quark and Gluon Jets (float16)

A float16 (half-precision) version of the quark and gluon jet dataset originally published by Komiske, Metodiev, and Thaler (Zenodo record 3164691). Only the 20-file subset without charm and bottom quark jets is included here. All simulation parameters and jet selection criteria are identical to the original: Pythia 8.226, √s = 14 TeV Quarks from WeakBosonAndParton:qg2gmZq, gluons from WeakBosonAndParton:qqbar2gmZg with the Z decaying to neutrinos FastJet 3.3.0, anti-k_t jets with R = 0.4 p_T^jet ∈ [500, 550] GeV, |y^jet| < 1.7 There are 20 files, each in compressed NumPy format (QG_jets_fp32_0.npz through QG_jets_fp32_19.npz). Each file contains two arrays: X: (100000, M, 4) — 50k quark and 50k gluon jets, randomly sorted, padded to max multiplicity M, with particle features (pt, rapidity, azimuthal angle, pdgid) stored as float32 y: (100000,) — jet labels, gluon = 0, quark = 1 The original dataset stores X in float64. Here X has been cast to float16, approximately halving file size. The y labels are unchanged. Users should be aware that float16 has limited dynamic range and precision. If you use this dataset, please cite the original Zenodo record and its associated paper: Komiske, Metodiev, Thaler, Energy Flow Networks: Deep Sets for Particle Jets, JHEP 01 (2019) 121, arXiv:1810.05165本数据集为Komiske、Metodiev与Thaler最初发布的夸克与胶子喷注数据集（Zenodo记录编号3164691）的float16（半精度）版本。本次仅提供不含粲夸克与底夸克喷注的20文件子集。 所有模拟参数与喷注筛选标准均与原始数据集保持一致： Pythia 8.226，质心系能量√s = 14 TeV 夸克源自WeakBosonAndParton:qg2gmZq过程，胶子源自WeakBosonAndParton:qqbar2gmZg过程，其中Z玻色子衰变至中微子 FastJet 3.3.0，采用anti-k_t算法、半径参数R=0.4的喷注 喷注横向动量p_T^jet ∈ [500, 550] GeV，喷注快度|y^jet| < 1.7 本数据集共包含20个文件，均采用压缩NumPy格式存储（文件名为QG_jets_fp32_0.npz至QG_jets_fp32_19.npz）。每个文件包含两个数组： X数组：形状为(100000, M, 4)，包含5万个夸克喷注与5万个胶子喷注，样本经随机打乱后填充至最大粒子多重数M，粒子特征（横向动量pt、快度、方位角、pdgid）以float32格式存储 y数组：形状为(100000,)，为喷注标签，其中胶子喷注标注为0，夸克喷注标注为1 原始数据集的X数组采用float64格式存储，本次版本将X转换为float16格式，文件大小约缩减一半。y标签未作修改。使用者需注意，float16格式的动态范围与精度均有限。 若使用本数据集，请引用原始Zenodo记录及相关论文： Komiske, Metodiev, Thaler, Energy Flow Networks: Deep Sets for Particle Jets, JHEP 01 (2019) 121, arXiv:1810.05165