Simulated sPHENIX Time-Projection Chamber (TPC) Data in Central Au-Au Collisions at sqrt[s] = 200 GeV, outer layer group

This is the dataset we used to train the 2D and 3D Bicephalous Convolutional Autoencoders (BCAEs) described in "Fast 2D Bicephalous Convolutional Autoencoder for Compressing 3D Time Projection Chamber Data" published in the 9th International Workshop on Data Analysis and Reduction for Big Scientific Data (https://drbsd.github.io/). To untar the file, run `tar -xvzf outer.tgz`. The Time Projection Chamber (TPC) is a hollow cylinder. Along the radial dimension, the TPC is composed of 48 cylindrical layers of small sensors, which are grouped into three layer groups: inner, middle, and outer. Each layer group has 16 consecutive layers. On each TPC layer, the voxels are presented as a rectangular grid with rows along the z (or horizontal) direction and columns along the azimuthal direction. Within one layer group, all layers have the same number of rows and columns. This allows us to represent the ADC values from one layer group as a 3D array. The data released here focuses on the outer layer group, where the array of ADC values has shape (16, 2304, 498) in the radial, azimuthal, and horizontal orders. The full voxel data are divided into 24 equal-size non-overlapping sections: 12 along the azimuthal direction (30 degrees per section) and 2 along the horizontal direction (divided by the transverse plane passing the collision point). We call one such section a TPC wedge. The array of ADC values from each TPC wedge in the outer layer has shape (16, 192, 249), listed in radial, azimuthal, and horizontal directions, respectively. The TPC wedges are used as the direct input to the deep neural network compression algorithms. We simulated 1310 events for central sqrt[s]=200 GeV Au-Au collisions with 170kHz pile-up. The data were generated with the HIJING event generator and Geant4 Monte Carlo detector simulation package integrated with the sPHENIX software framework. The simulated TPC readout (ADC values) from these events are represented in a 10-bit unsigned integer in [0, 1023]. To reduce unnecessary data transmission between detector pixels and front-end electronics, a zero-suppression algorithm has been applied. ADC values below 64 are suppressed to zero as most of them are noise. The zero compression makes the TPC data sparse at about 10% nonzero occupancy.We divide the 1310 total events into 1048 events for training and 262 for testing. Each event contains 24 outer-layer wedges. Thus, the training partition contains 25152 TPC outer-layer wedges, while the testing portion has 6288 wedges. The compression algorithm compresses each wedge independently.The dataset has the following structure: 24 subfolders with the name `12-2_[azimuthal section]-[horizontal section]` where the [azimuthal section] is labeled by an integer in [0, 11] and the [horizontal section] is labeled by either 0 or 1. Each file in one of the subfolders has the name in the format "AuAu200_170kHz_10C_Iter2_[simulation id].xml_TPCMLDataInterface_[event id within simulation].npy". There are 131 simulations, and each simulation contains 10 independent events (and hence the 1310 total events as mentioned above). Each [event id within simulation] is an integer in [0, 9]. train.txt: a list of all TPC wedges for the training split. text.txt: a list of all TPC wedges for the test split. Note that the dataset is split by events. That is, if a TPC wedge from an event is in the train split, all 24 wedges from the same event will all be in the train split. The same holds for the test split.本数据集用于训练发表于第9届国际大科学数据数据分析与缩减研讨会（9th International Workshop on Data Analysis and Reduction for Big Scientific Data，https://drbsd.github.io/）的论文《Fast 2D Bicephalous Convolutional Autoencoder for Compressing 3D Time Projection Chamber Data》中提及的二维与双头卷积自编码器（2D and 3D Bicephalous Convolutional Autoencoders, BCAEs）。 若需解压该数据集文件，请执行命令`tar -xvzf outer.tgz`。 时间投影室（Time Projection Chamber, TPC）为空心圆柱结构。沿径向维度，TPC由48层小型传感器圆柱层构成，分为内、中、外三个层组，每个层组包含16个连续层。每个TPC层的体素以矩形网格形式呈现，行沿z轴（或水平方向）排布，列沿方位角方向排布。同一层组内的所有层拥有相同的行列数，因此可将单个层组的模数转换（Analog-to-Digital Converter, ADC）值表示为三维数组。 本次发布的数据以外层组为研究对象，其ADC值数组按径向、方位角、水平方向的维度顺序，形状为(16, 2304, 498)。完整体素数据被划分为24个等大且无重叠的区块：沿方位角方向分为12个区块（每个区块覆盖30度），沿水平方向分为2个区块（以穿过碰撞点的横向平面为界），此类单个区块被称为TPC楔形区块。外层组中每个TPC楔形区块的ADC值数组按径向、方位角、水平方向的维度顺序，形状为(16, 192, 249)。TPC楔形区块将作为深度神经网络压缩算法的直接输入。 我们针对质心系能量√s=200 GeV的金-金（Au-Au）中心碰撞，模拟了1310个事件，叠加率为170kHz。数据通过集成了sPHENIX软件框架的HIJING事件生成器与Geant4蒙特卡洛探测器仿真包生成。 上述事件的仿真TPC读出值（ADC值）以10位无符号整数形式存储，取值范围为[0, 1023]。为减少探测器像素与前端电子学间的不必要数据传输，我们应用了零抑制算法：将低于64的ADC值置零，因其大多为噪声。该零压缩算法使TPC数据的非零占用率约为10%，数据呈现稀疏特性。我们将全部1310个事件划分为训练集（1048个事件）与测试集（262个事件）。每个事件包含24个外层组楔形区块，因此训练集共包含25152个TPC外层楔形区块，测试集则包含6288个楔形区块。压缩算法将独立处理每个楔形区块。 本数据集的结构如下： 包含24个子文件夹，命名格式为`12-2_[azimuthal section]-[horizontal section]`，其中[azimuthal section]为取值于[0, 11]的整数标签，[horizontal section]的标签为0或1。每个子文件夹内的文件命名格式为`AuAu200_170kHz_10C_Iter2_[simulation id].xml_TPCMLDataInterface_[event id within simulation].npy`。本次仿真共包含131个仿真批次，每个批次包含10个独立事件（即总计1310个事件）。其中[event id within simulation]为取值于[0, 9]的整数。 `train.txt`：训练划分下所有TPC楔形区块的列表。`text.txt`：测试划分下所有TPC楔形区块的列表。 请注意，本数据集按事件进行划分。即若某一事件的某个TPC楔形区块属于训练集，则该事件的全部24个楔形区块都将被划入训练集，测试集的划分遵循相同规则。