BTSbot v10 training set

This is the production version of the BTSbot training set, limited to public (programid=1) ZTF alerts. BTSbot is a multi-modal convolutional neural network designed for real-time identification bright extragalactic transients in Zwicky Transient Facility (ZTF) data. BTSbot provides a bright transient score to individual ZTF detections using their image data and 25 extracted features. BTSbot is able to eliminate the need for daily visual inspection of new transients by automatically identifying and requesting spectroscopic follow-up observations of new bright transient candidates. The training data is split into two zipped files. metadata_v10.zip contains alert packet features for the alerts in the train, validation, and test splits stored a separate .csv files. images_v10.zip contains the corresponding image cutouts stored as three .npy files. The BTSbot source code contains routines for reading these files and training a model on them. They can also easily be loaded with pandas.read_csv() and numpy.load(). This training set data is necessary for reproducing the results of the BTSbot study, although this dataset only contains ZTF public data while the production BTSbot model also trained on ZTF partnership data. If you use reference this data or BTSbot please cite the BTSbot paper.

本数据集为BTSbot训练集的正式生产版本，仅包含公开（programid=1）的兹威基瞬变设施（Zwicky Transient Facility, ZTF）告警数据。BTSbot是一种多模态卷积神经网络，专为在ZTF观测数据中实时识别明亮河外瞬变天体而设计。该模型可利用单条ZTF探测事件的图像数据与25项提取特征，为其赋予明亮瞬变天体评分。BTSbot可自动识别明亮瞬变天体候选体并申请光谱后续观测，从而无需对新发现的瞬变天体开展每日目视检视。
本次训练数据分为两个压缩包文件：metadata_v10.zip包含训练、验证与测试子集的告警数据包特征，这些特征以独立的.csv格式文件存储；images_v10.zip则包含对应的图像截帧，以三个.npy格式文件存储。
BTSbot的源代码包含读取上述文件并基于其训练模型的例程，也可通过pandas.read_csv()与numpy.load()直接加载。尽管正式生产的BTSbot模型还额外使用了ZTF合作项目数据进行训练，但本训练集仅包含ZTF公开数据，因此仅可用于复现BTSbot相关研究的实验结果。若您使用或引用本数据集与BTSbot，请引用BTSbot相关研究论文。