The question that the project tries to address is can Machine Learning assign High Energy Physics in discovering and categorizing new particle?
For this we need clear understanding about a few concepts in High Energy Physics.
Some of the concepts like particle detectors in Large Hadron Collider.
What does reconstructing particle tracks from points left in silicon detectors mean?
In more details : for each collision, about 10.000 space tracks (helicoidal trajectories originating approximately from the center of the detector), will leave about 10 precise 3D points. The core pattern recognition tracking task is to associate the 100.000 3D points into tracks. Current studies show that traditional algorithms suffer from a combinatorial explosion of the CPU time.
There is a strong potential for application of Machine Learning techniques to this tracking issue. The problem can be related to representation learning, to combinatorial optimization, to clustering (associate together the hits which were deposited by the same particle), and even to time series prediction. An essential question is to efficiently exploit the a priori knowledge about geometrical constraints (structural priors).
The question that the project tries to address is can Machine Learning assign High Energy Physics in discovering and categorizing new particle?
For this we need clear understanding about a few concepts in High Energy Physics.
Some of the concepts like particle detectors in Large Hadron Collider.
What does reconstructing particle tracks from points left in silicon detectors mean?
In more details : for each collision, about 10.000 space tracks (helicoidal trajectories originating approximately from the center of the detector), will leave about 10 precise 3D points. The core pattern recognition tracking task is to associate the 100.000 3D points into tracks. Current studies show that traditional algorithms suffer from a combinatorial explosion of the CPU time.
There is a strong potential for application of Machine Learning techniques to this tracking issue. The problem can be related to representation learning, to combinatorial optimization, to clustering (associate together the hits which were deposited by the same particle), and even to time series prediction. An essential question is to efficiently exploit the a priori knowledge about geometrical constraints (structural priors).