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（https://drbsd.github.io/）的《用于压缩3D时间投影室数据的快速双头二维卷积自编码器》一文中提及的二维与三维双头卷积自编码器（Bicephalous Convolutional Autoencoders, BCAEs）。如需解压该文件，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。沿径向方向，TPC由48层小型传感器圆柱层构成，被划分为内、中、外三个层组，每个层组包含16个连续的圆柱层。每一层TPC的体素以矩形网格形式呈现，行沿z轴（即水平方向）排布，列沿方位角方向排布。同一层组内的所有层数的行、列数量均一致，因此我们可将一个层组的模拟数字转换（Analog-to-Digital Converter, ADC）数值表示为三维数组。本次发布的数据以外层组为核心，该层组的ADC数值数组按径向、方位角、水平方向的顺序排列，形状为(16, 2304, 498)。完整的体素数据被划分为24个大小均等且无重叠的区块：沿方位角方向分为12个区块（每个区块对应30度方位角），沿水平方向分为2个区块（以碰撞点所在的横向平面为分界）。我们将此类区块称为TPC楔形区块（TPC wedge）。外层组中每个TPC楔形区块的ADC数值数组按径向、方位角、水平方向依次排列，形状为(16, 192, 249)。这些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）对心碰撞场景，模拟了1310个叠加率为170kHz的事件。数据通过HIJING事件生成器与集成了sPHENIX软件框架的Geant4蒙特卡洛探测器模拟包生成。上述事件模拟得到的TPC读出数据（ADC数值）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之间的不必要数据传输，我们采用了零抑制算法：将低于64的ADC数值置零，因这类数值大多为噪声。该零压缩算法使TPC数据的非零占据率约为10%，整体数据呈稀疏特性。我们将全部1310个事件划分为训练集与测试集，其中训练集包含1048个事件，测试集包含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。压缩算法将对每个楔形区块独立进行压缩处理。本数据集的结构如下：共包含24个子文件夹，文件夹命名格式为`12-2_[azimuthal section]-[horizontal section]`，其中`[azimuthal section]`为取值范围[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`。本次模拟共包含131个模拟批次，每个模拟批次包含10个独立事件（即前述的1310个总事件），其中`[event id within simulation]`为取值范围[0, 9]的整数标签。数据集附带两个文本文件：`train.txt`，存储训练集拆分下的全部TPC楔形区块列表；`text.txt`，存储测试集拆分下的全部TPC楔形区块列表。请注意，本数据集按事件进行拆分：若某一事件中的某一个TPC楔形区块被划入训练集，则该事件的全部24个楔形区块均会被划入训练集；测试集拆分遵循相同规则。