Zobrazeno 1 - 10
of 77
pro vyhledávání: '"Murnane Daniel"'
Autor:
Sha, Qiyu, Murnane, Daniel, Fieg, Max, Tong, Shelley, Zakharyan, Mark, Fang, Yaquan, Whiteson, Daniel
Analysis of data from particle physics experiments traditionally sacrifices some sensitivity to new particles for the sake of practical computability, effectively ignoring some potentially striking signatures. However, recent advances in ML-based tra
Externí odkaz:
http://arxiv.org/abs/2410.00269
Autor:
Huang, Andris, Melkani, Yash, Calafiura, Paolo, Lazar, Alina, Murnane, Daniel Thomas, Pham, Minh-Tuan, Ju, Xiangyang
Particle tracking is crucial for almost all physics analysis programs at the Large Hadron Collider. Deep learning models are pervasively used in particle tracking related tasks. However, the current practice is to design and train one deep learning m
Externí odkaz:
http://arxiv.org/abs/2402.10239
Autor:
Thais, Savannah, Murnane, Daniel
Incorporating inductive biases into ML models is an active area of ML research, especially when ML models are applied to data about the physical world. Equivariant Graph Neural Networks (GNNs) have recently become a popular method for learning from p
Externí odkaz:
http://arxiv.org/abs/2311.03094
Autor:
Murnane, Daniel
The use of graph neural networks has produced significant advances in point cloud problems, such as those found in high energy physics. The question of how to produce a graph structure in these problems is usually treated as a matter of heuristics, e
Externí odkaz:
http://arxiv.org/abs/2307.16662
Graph neural networks (GNNs) have gained traction in high-energy physics (HEP) for their potential to improve accuracy and scalability. However, their resource-intensive nature and complex operations have motivated the development of symmetry-equivar
Externí odkaz:
http://arxiv.org/abs/2304.05293
Autor:
Liu, Ryan, Calafiura, Paolo, Farrell, Steven, Ju, Xiangyang, Murnane, Daniel Thomas, Pham, Tuan Minh
We introduce a novel variant of GNN for particle tracking called Hierarchical Graph Neural Network (HGNN). The architecture creates a set of higher-level representations which correspond to tracks and assigns spacepoints to these tracks, allowing dis
Externí odkaz:
http://arxiv.org/abs/2303.01640
We present a new algorithm that identifies reconstructed jets originating from hadronic decays of tau leptons against those from quarks or gluons. No tau lepton reconstruction algorithm is used. Instead, the algorithm represents jets as heterogeneous
Externí odkaz:
http://arxiv.org/abs/2301.00501
Autor:
Ju, xiangyang, Wang, Yunsong, Murnane, Daniel, Choma, Nicholas, Farrell, Steven, Calafiura, Paolo
Many artificial intelligence (AI) devices have been developed to accelerate the training and inference of neural networks models. The most common ones are the Graphics Processing Unit (GPU) and Tensor Processing Unit (TPU). They are highly optimized
Externí odkaz:
http://arxiv.org/abs/2210.12247
Autor:
Thais, Savannah, Calafiura, Paolo, Chachamis, Grigorios, DeZoort, Gage, Duarte, Javier, Ganguly, Sanmay, Kagan, Michael, Murnane, Daniel, Neubauer, Mark S., Terao, Kazuhiro
Many physical systems can be best understood as sets of discrete data with associated relationships. Where previously these sets of data have been formulated as series or image data to match the available machine learning architectures, with the adve
Externí odkaz:
http://arxiv.org/abs/2203.12852
Autor:
Wang, Chun-Yi, Ju, Xiangyang, Hsu, Shih-Chieh, Murnane, Daniel, Calafiura, Paolo, Farrell, Steven, Spiropulu, Maria, Vlimant, Jean-Roch, Aurisano, Adam, Hewes, V, Cerati, Giuseppe, Gray, Lindsey, Klijnsma, Thomas, Kowalkowski, Jim, Atkinson, Markus, Neubauer, Mark, DeZoort, Gage, Thais, Savannah, Ballow, Alexandra, Lazar, Alina, Caillou, Sylvain, Rougier, Charline, Stark, Jan, Vallier, Alexis, Sardain, Jad
Particle tracking is a challenging pattern recognition task at the Large Hadron Collider (LHC) and the High Luminosity-LHC. Conventional algorithms, such as those based on the Kalman Filter, achieve excellent performance in reconstructing the prompt
Externí odkaz:
http://arxiv.org/abs/2203.08800