Collaborative Research: Elements: Enabling Particle and Nuclear Physics Discoveries with Neural Deconvolution

About This Grant

Even though large physics experiments are able to detect complex properties of reaction rates, such foundational scientific quantities are always compressed due to limitations in statistical methods and exchange platforms. The current paradigm introduces significant barriers for scientific discovery and data reusability. Comparisons between experiments with different compression schemes is challenging. Furthermore, the compression schemes necessarily throw out potentially useful information, which may be needed to explore interesting phenomena. This information loss likewise limits the long-term utility of archived data, which may be of scientific interest long after the experiment that generated it ends. Recent advancements in machine-learning methods initiated by the PIs and others solve these issues by enabling measurements directly in the un-compressed (or minimally compressed) data. However, there is currently no standard or platform for sharing such data, and therefore, no measurements of this kind with actual data have been published to date. This project builds open source cyberinfrastructure for publishing and reusing un- or minimally compressed measurements for research and educational purposes. These tools are widely applicable across physics domains and data from electron-proton collisions are used to test and benchmark the frameworks. This project serves the national interest, as stated by NSF's mission, by promoting the progress of science. The publication of minimally processed data greatly extends the practical lifetime of experimental facilities, enabling high-quality scientific analyses well beyond the time a detector is running and the researchers who collected the data. Many analyses are simply not possible with existing protocols where only limited numerical results are published alongside academic papers. Minimally compressed data can be studied without computationally expensive and often proprietary detector simulations, and are therefore of great interest for a first exposure to research by early career scientists in training. This project builds upon recent advances by the PIs and others in the development of machine machine learning solutions to measurements of reaction rates in large physics experiments. Cyberinfrastructure is created for publishing and reusing measurements created by these machine learning algorithms. The project develops an exchange format for sharing machine learning-based measurements whose data representation is neural networks, unlike the tabular, often histogram, format of traditional measurements. This format is integrated with a software platform that enables these data to be readily findable, accessible, interoperable, and reusable (FAIR). This cyberinfrastructure is tested with a prototype science pipeline, starting from a first unbinned measurement as input into the platform and ending with an analysis that reinterprets it. In the process, practical software is developed and made available to other researchers to carry out unbinned measurements and to reuse published data. These tools are integrated with widely-used frameworks in particle, nuclear, and astrophysics in order to accelerate their adaptation. Such developments are also used to broaden participation in fundamental physics and applied machine learning for undergraduate researchers, including those who would not normally have access to large experimental and computing resources. This is enabled in part by significantly reduced computational resources required to carry out forefront analysis since computationally expensive experimental tools including detector simulations are not needed. Undergraduate researchers are involved in the development and testing of the new infrastructure. This award by the Office of Advanced Cyberinfrastructure is jointly supported by the Physics at the Information Frontier program in the Division of Physics within the Directorate for Mathematical and Physical Sciences. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.