Abstract
High-energy physics detectors, images, and point clouds share many similarities in terms of object detection. However, while detecting an unknown number of objects in an image is well established in computer vision, even machine learning assisted object reconstruction algorithms in particle physics almost exclusively predict properties on an object-by-object basis. Traditional approaches from computer vision either impose implicit constraints on the object size or density and are not well suited for sparse detector data or rely on objects being dense and solid. The object condensation method proposed here is independent of assumptions on object size, sorting or object density, and further generalises to non-image-like data structures, such as graphs and point clouds, which are more suitable to represent detector signals. The pixels or vertices themselves serve as representations of the entire object, and a combination of learnable local clustering in a latent space and confidence assignment allows one to collect condensates of the predicted object properties with a simple algorithm. As proof of concept, the object condensation method is applied to a simple object classification problem in images and used to reconstruct multiple particles from detector signals. The latter results are also compared to a classic particle flow approach.
Highlights
Have proven to be just as powerful but with significantly lower resource requirements [5,8,9,10,11]
Object condensation can be implemented through a dedicated loss function and truth definition as detailed in the following. Since these definitions are mostly independent of the network architecture, this paper focuses on describing the training method in detail and provides an application to object identification and segmentation in an image as proof of concept together with an example application to a particle flow problem
The performance of the baseline particle flow (PF) algorithm and the object condensation method are evaluated with respect to single particle quantities and cumulative quantities
Summary
Reconstructing and identifying objects (e.g. particles) from detector hits in e.g. a high-energy physics experiment are, in principle, similar tasks, in the sense that both rely on a finely grained set of individual inputs (e.g. pixels or detector hits) and infer higher-level object properties from them. While this method in principle resolves the issues mentioned above, it comes with stringent requirements: The neural network architecture needs to be chosen such that it can predict properties of static edges, which limits the possible choices to graph neural networks; all possibly true edges need to be inserted in the graph at the preprocessing stage, such that they can be classified by the network; the same connections need to be evaluated once more to build the object under question by applying a threshold on the connection score This binary nature of an edge classification makes this approach less applicable to situations with large overlaps and fractional assignments, and it requires rather resource demanding pre- and post processing steps. Since these definitions are mostly independent of the network architecture, this paper focuses on describing the training method in detail and provides an application to object identification and segmentation in an image as proof of concept together with an example application to a particle flow problem
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