How Architecture and Training Affect TPC Representations Across Experiments
cs.LG, cs.CV, nucl-ex, physics.ins-det
Submitted: 2026-08-22
Updated: 2026-08-27
Terminology
Sources
- Machine Learning Methods for Track Classification in the AT-TPC
- Particle Hit Clustering and Identification Using Point Set Transformers in Liquid Argon Time Projection Chambers
- Unsupervised Learning for Identifying Events in Active Target Experiments
- Scalable Deep Convolutional Neural Networks for Sparse, Locally Dense Liquid Argon Time Projection Chamber Data
- Object Detection with Deep Learning for Rare Event Search in the GADGET II TPC
- On the Opportunities and Risks of Foundation Models
- Masked Particle Modeling on Sets: Towards Self-Supervised High Energy Physics Foundation Models
- Solving Key Challenges in Collider Physics with Foundation Models
- Particle Trajectory Representation Learning with Masked Point Modeling
- Panda: Self-distillation of Reusable Sensor-level Representations for High Energy Physics
- Fine-tuning machine-learned particle-flow reconstruction for new detector geometries in future colliders
- Sparse Methods for Vector Embeddings of TPC Data
- How transferable are features in deep neural networks?
- PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
- Deep Residual Learning for Image Recognition
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