1. Background and questions excepted to be solved by this study:

Machine learning and object detection networks have become powerful tools in computer vision and image processing, with numerous applications in fields such as autonomous vehicles, surveillance, and robotics. They can also offer a potential solution to many challenges faced in high-power laser experiments due to the fast development and increasing need for high-repetition-rate operation capabilities in laser systems and plasma targetry, enabling statistical methods that require a large number of shots. This study presents several applications of object detection networks in a high-power laser system with a peak power reaching the petawatt level and repetition rate at the hertz level, while laser systems with similar specs are emerging worldwide in the past few years. The study discusses the benefits of object detection networks and their use in computer vision specifically in the context of laser plasma diagnostics and laser damage detection. The paper provides several examples of successful applications of object detection networks in these areas, focusing on exploring online and offline analysis of diagnostic data in a high-power laser-plasma experiment. The study concludes by highlighting the potential for further research and development in this area, which could lead to even more advanced and sophisticated applications of object detection networks in laser systems. Recently, this paper was published in High Power Laser Science and Engineering , Vol. 11, Issue,1 (Jinpu Lin, Florian Haberstroh, Stefan Karsch, Andreas Döpp. Applications of object detection networks in high-power laser systems and experiments[J]. High Power Laser Science and Engineering, 2023, 11(1): 010000e7).

2. Theories and experimental focus:

Machine learning techniques are employed to diagnose a hybrid laser-plasma wakefield accelerator that produces GeV electron beams at the Center for Advanced Laser Applications at the Ludwig Maximilians University of Munich, Germany. In the experimental setup, an ultrashort laser pulse is focused into gaseous plasmas to generate a high-energy electron beam, which then drives a plasma-wakefield accelerator in the second stage. Three exemplary applications are presented in the diagnostics, including plasma wave shadowgraphy, electron energy spectra, and laser damage on optics. A state-of-the-art pre-trained object detection network is trained and tested on datasets with various sizes, where the model with the best performance is achieved using ~50 labeled data points. Beyond the regular training, validation, and test stages, the model is also applied to multiple inference sets consisting of over 1000 data points taken on multiple experiment days. The model training is performed using accessible computational resources in GPU hours or less, while the inference time on an unseen image with the trained models takes only tens of milliseconds.

3. Innovation, significance and potential application of the study:

The trained models can perform instant quantitative analysis of important physics parameters, such as electron charge in the energy peak, plasma density, plasma wavelength, and laser beam jitter. Being the first application of object detection networks in laser-plasma accelerators, this study demonstrates the advantages and potentials of machine learning methods in fast processing of big data, especially image data from advanced diagnostic tools. The main benefit of using object detection is the possibility of real-time, in-depth data analysis and visualization during an experimental campaign, which goes beyond what a human operator can achieve. The presented methodology is adaptable and easy to implement, requiring minimal expertise in machine learning and only a few dozen labeled diagnostic images. The paper explores the potential to speed up the experimental logic in high-power laser experiments, which typically involve a cycle of performing the experiment, analyzing data, adjusting plans, and performing the next experiment. This process can take several days, especially when dealing with large data sets or extensive diagnostic tools. The study presents a proof-of-principle study that provides online analysis to some diagnostics at 1Hz, allowing for in-situ decision-making that is not possible at this speed without a trained machine learning model. The study demonstrates the potential for object detection networks to improve the efficiency of high-power laser experiments and accelerate the pace of scientific discovery in this field.

The method can also be applied to kHz laser systems with little loss in the repetition rate. The algorithm can operate at approaching 100 Hz with moderate prediction accuracy, while experimentalists usually have to average over approximately 10 laser shots to reduce fluctuations. Therefore, the presented methodology is not much slower than the 'effective' repetition rate of a kHz laser.

4. Comments from a member of your group:

"As a user of a high-power laser system for laser plasma acceleration, I have found computer vision capabilities based on object detection networks to be highly useful. We use object detection as a tool for online diagnostics during laser wake field acceleration (LWFA) experiments, as well as for fast and reliable object detection when analyzing larger data sets.

During experiments, the object detection network assists in locating features that are moving and changing from shot to shot. With instant and automatic detection of these objects, key parameters for the experiment can be extracted. For example, spectra of pulsed electron bunches are detected and subsequently evaluated for their energy, charge, bandwidth, and divergence, which are displayed live to the experimentalists.

Object detection is also extremely helpful when post-processing and evaluating data sets taken during long experiments. For instance, identification of the relevant features in shadowgraph images of plasma modulations in LWFA greatly reduces the time needed for data analysis compared to manual identification and labeling of objects. This enables a consistent evaluation of parameters such as plasma density.

The applied pre-trained model is highly versatile and can easily be adapted to various tasks for online and offline data treatment. As a user, I am mainly convinced by the simple fine-tuning of the model and the benefits that the object detection network offers for daily experimental work and data evaluation."

5. The introduction of the follow-up work:

There are several potential follow-up works that could build on the research presented in this paper. One possibility is to apply the object detection network methodology to other high-power laser experiments, including those with different laser parameters and diagnostics tools. This could help to further validate the effectiveness and adaptability of the approach. Another potential follow-up work is to explore the possibility of using object detection networks for real-time decision-making in high-power laser experiments. This could involve developing algorithms that can analyze data from multiple diagnostics in real-time and make recommendations for adjustments to the experimental setup or data analysis protocols. Additionally, the paper notes that the object detection methodology presented in the study is not perfect and could be improved with additional training data and optimization of the network architecture. Further research could focus on optimizing the methodology for different types of data and improving the accuracy of the object detection algorithm. Overall, there are many potential avenues for follow-up work that could build on the findings of this paper and further advance the use of object detection networks in high-power laser experiments.