CMS Experiment At the CMS experiment, a hierarchical set of algorithms, called the trigger system, processes and filters the incoming data stream. The CMS trigger system is structured in two stages: - Level 1 Trigger (L1 Trigger) implemented on custom-designed electronics, reduces the 40 MHz input to a 100 kHz rate. L1 Trigger algorithms are implemented on Field Programmable Gate Arrays (FPGAs) and have 3.2 μs to decide whether to pass an event to the next step. - High-Level Trigger (HLT) is a collision reconstruction software running on a computing farm. It scales the 100 kHz stream from the L1 Trigger rate to 1 kHz in 100 ms. The trigger system throws away 99.9975% of events (99.75% at the L1 Trigger). The Large Hadron Collider (LHC) at CERN operates at a rate of 40M events per second. This enables physicists to search for rare and new interactions. Due to technological limitations (e.g. storage constraints and readout electronics), the experiments at the LHC are required to reduce the amount of recorded data. The Princeton group at CMS is developing CICADA (Calorimeter Image Convolutional Anomaly Detection Algorithm). - CICADA is a fully autonomous AI algorithm that can process LHC event data in real-time - CICADA independently picks out topologies that are different from the majority of currently recorded events. - CICADA operates close to the rawest recorded data, i.e. calorimeter energy deposits. In this manner, CICADA has as little human bias as possible. CICADA leverages deep learning-based anomaly detection and methods for efficient neural network deployment in hardware. Anomaly Detection with Machine Learning Neutrino Experiments Elements: RAD Discoveries for Fundamental Physics Collaborators: Georgia Karagiori, Akshay Malige (Columbia University), Isabel Ojalvo, Pallabi Das, Adrian Pol (Princeton University) Anomaly detection is challenging when dealing with non-linear data relationships, especially in particle detectors. Deep learning, particularly Convolutional Neural Networks, excels at learning from raw energy deposits in this context by uncovering hierarchical features. This eliminates manual feature engineering and adapts to various input natures, making it ideal for image- based anomaly detection in calorimeter data, where calorimeter cells are treated as pixels representing energy deposits after projecting and unwrapping. The groups propose to utilize methods for efficient model deployment to meet computing constraints, i.e. quantization, pruning and knowledge distillation. - Quantization involves assigning the model’s weights and activations to a lower bit-width representation. - Pruning deals with the problem of the optimal removal of parameters deemed unimportant via some heuristic. - Knowledge distillation is a method of training a smaller network, called a student using soft labels generated by a larger network, called a teacher. Motivated by the use case, the Columbia and Princeton groups will propose novel methods for compression and deployment that are directly transferable to a broader physics community. Machine Learning on Highly Constrained Systems Student-Teacher Knowledge Distillation The teacher network condenses data into a latent space and then reconstructs back into the detector space using two NN. It then calculates the Mean Square Error Loss (MSE) between the output and input The student network uses a single dense layer Neural Network to calculate the MSE directly. Overall Project Goals: Advance detector real-time monitoring: Initially, we will instantiate AI algorithms to ``re-discover'' known physics processes or known detector issues, e.g. known dead channels, or noisy channels in the detector. The goal will be to demonstrate unsupervised learning capability in flagging detector abnormalities within the data. As part of this effort, we will also focus on physics/label-informed data dimensionality reduction. Goal 1. Goal 2. Goal 3. Expand physics capability of near-future experiments: N