The Machine Learning Deployment Gap in HEP

2025-05-29

Info

I want to talk about the challenge that I see in the HEP community regarding the deployment of ML models. This is not a criticism of the work done by the community, but rather an observation of the gap between training ML models and actually using them in practice.

The landscape of HEP computing stands at a crossroads. While our community has embraced ML with remarkable enthusiasm, developing sophisticated neural architectures that excel on MC samples and produce compelling results for conference and workshop presentations, we face a growing chasm between algorithmic innovation and operational deployment within our detector systems. Walk through any major HEP experiment today, and you’ll witness this dichotomy firsthand. Research groups showcase impressive deep learning models trained on carefully curated simulated datasets, achieving remarkable classification performance on benchmark tasks like jet tagging, electron identification, or invariant mass reconstruction.

These models generate beautiful ROC curves and efficiency plots that captivate audiences at workshops and conferences. Yet when the conversation turns to deploying these same algorithms within the Level-1 trigger, the High-Level Trigger, or reconstruction frameworks, the enthusiasm often gives way to uncomfortable silence.

Our current research methodology has inadvertently created what might be termed the “MC mirage”. Algorithms that perform brilliantly on simulated data but struggle to transition into the harsh realities of online data acquisition systems. The controlled environment of offline analysis, with its unlimited processing time and carefully preprocessed datasets, bears little resemblance to the microsecond-scale decision windows of trigger systems or the sustained throughput demands of real-time event reconstruction. The problem can even extend to the offline reconstruction where people find it hard and time-consuming to actually integrate the inference engine within the current frameworks.

This phenomenon reflects deeper structural issues within our field. The academic reward system naturally prioritizes algorithmic novelty and benchmark improvements over the less glamorous work of system integration and detector operations. Publications emerge from new architectures and improved efficiency measurements, not from successful deployments within ATLAS TDAQ or CMS trigger systems (What about LHCb impressive Allen Framework ?). Consequently, we’ve developed a research culture that optimizes for showing good plots for performance on MC rather than operational robustness.

Warning

While the HEP community has made significant strides in developing ML algorithms, we must confront the uncomfortable truth that many of these innovations remain untested in real-world environments. The gap between theoretical performance and practical deployment is widening, and we risk losing valuable insights if we do not address this issue head-on.

The High-Luminosity LHC (HL-LHC) represents more than an incremental upgrade it constitutes a fundamental paradigm shift that will expose the limitations of our current approach. The projected luminosity increase to 10³⁴ cm⁻²s⁻¹ translates directly into unprecedented computational demands across every aspect of our detector systems. Pileup conditions will reach 200 simultaneous interactions per bunch crossing, while trigger rates will be almost maintained at current levels despite the dramatically increased background. These conditions cannot be addressed through incremental improvements to existing algorithms or modest increases in computational resources. The HL-LHC environment demands fundamental rethinking of how we implement ML within detector readout systems, trigger processors, and reconstruction frameworks.

Traditional approaches that work adequately at current luminosities might fail under the new computational load. Current deployment patterns reveal the extent of our challenge. Beyond simple neural classifiers for particle identification, sophisticated ML remains largely absent from online systems. The Level-1 trigger continues to rely on lookup tables and hardwired logic, while even the High-Level Trigger implements only basic multivariate techniques for most selection algorithms.

The scarcity of expertise in parallel processing and accelerated computing within our community represents perhaps the most significant barrier to progress. Many physicists possess strong backgrounds in traditional scientific computing are comfortable with ROOT, familiar with grid computing paradigms, experienced in MC simulation frameworks yet lack exposure to the GPU programming and heterogeneous computing architectures that ML deployment requires. CUDA programming, memory hierarchy optimization, and kernel design remain specialized skills concentrated among relatively few individuals within major collaborations. This knowledge gap extends beyond technical implementation to fundamental algorithmic design principles. Effective utilization of GPU architectures requires rethinking algorithms from first principles, optimizing for parallel execution patterns rather than the sequential processing models that dominate traditional physics software development.

The situation becomes more complex when considering the heterogeneous computing landscape emerging in modern detector systems. FPGAs have found increasing adoption in trigger processors and front-end electronics, offering the low-latency, high-throughput characteristics essential for real-time applications. However, mapping neural network inference onto FPGA architectures requires expertise that spans both ML and digital signal processing, a combination rarely found within physics research groups.

Our experiments software frameworks evolved over decades to support specific analysis workflows centered around event-by-event processing of collision data. These systems prioritize reliability, reproducibility, and integration with established toolchains over performance optimization for ML workloads. ATLAS Athena, CMS CMSSW, and similar frameworks assume relatively modest computational requirements per event and sequential processing patterns that align poorly with batch-oriented neural network inference. Incorporating deep learning models into trigger systems presents particularly acute challenges. Level-1 trigger processors operate within 4-microsecond latency budgets, processing collision data at 40 MHz bunch crossing rates. These constraints demand inference engines optimized for sustained high-throughput operation with deterministic timing characteristics, requirements that standard ML frameworks rarely address.

Memory management represents another critical integration challenge. Experiments software typically assumes modest memory footprints and predictable allocation patterns. Neural network inference, especially for transformer architectures or large convolutional networks, demands substantial memory allocation and careful orchestration of data movement between CPU and GPU memory spaces. Reconciling these requirements with existing memory management strategies often necessitates fundamental framework redesign.

Computing resource allocation within experimental collaborations reflects historical priorities that may not align with modern ML deployment requirements. Grid computing infrastructures excel at embarrassingly parallel offline analysis tasks but provide limited support for the sustained, low-latency inference patterns that online applications demand. GPU resources, when available, are often configured for batch processing workloads rather than the real-time inference scenarios that trigger systems require.

The collaborative structure of experimental HEP introduces additional coordination complexities. Deployment decisions within detector systems affect multiple physics groups, trigger systems, and software subsystems simultaneously. Individual researchers may develop impressive ML solutions for specific analysis tasks, yet lack the institutional support and cross-system expertise necessary for integration into detector operations. Training and knowledge transfer present ongoing challenges in this environment. GPU programming and parallel processing expertise cannot be developed quickly, requiring sustained investment in education and mentorship programs. Many institutions lack personnel with the necessary background to provide this training, creating self-perpetuating cycles of limited expertise within collaborations.

The path forward requires systematic investment in both technical infrastructure and human capital development, recognizing that the HL-LHC timeline creates both urgency and opportunity for transformative change in how we approach ML deployment within experimental physics.