Zobrazeno 1 - 10
of 68
pro vyhledávání: '"Murnane Daniel"'
Autor:
Murnane Daniel
Publikováno v:
EPJ Web of Conferences, Vol 295, p 09016 (2024)
Significant progress has been made in applying graph neural networks (GNNs) and other geometric ML ideas to the track reconstruction problem. State-of-the-art results are obtained using approaches such as the Exatrkx pipeline, which currently applies
Externí odkaz:
https://doaj.org/article/23bb220f86e54c7ebf6d867c21d0eea5
Autor:
Caillou Sylvain, Calafiura Paolo, Ju Xiangyang, Murnane Daniel, Pham Tuan, Rougier Charline, Stark Jan, Vallier Alexis
Publikováno v:
EPJ Web of Conferences, Vol 295, p 03030 (2024)
Particle tracking is vital for the ATLAS physics programs. To cope with the increased number of particles in the High Luminosity LHC, ATLAS is building a new all-silicon Inner Tracker (ITk), consisting of a Pixel and a Strip subdetector. At the same
Externí odkaz:
https://doaj.org/article/5825d8225f2b49bd970ac1e07cc7241b
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:
Hewes Jeremy, Aurisano Adam, Cerati Giuseppe, Kowalkowski Jim, Lee Claire, Liao Wei-keng, Day Alexandra, Agrawal Ankit, Spiropulu Maria, Vlimant Jean-Roch, Gray Lindsey, Klijnsma Thomas, Calafiura Paolo, Conlon Sean, Farrell Steve, Ju Xiangyang, Murnane Daniel
Publikováno v:
EPJ Web of Conferences, Vol 251, p 03054 (2021)
This paper presents a graph neural network (GNN) technique for low-level reconstruction of neutrino interactions in a Liquid Argon Time Projection Chamber (LArTPC). GNNs are still a relatively novel technique, and have shown great promise for similar
Externí odkaz:
https://doaj.org/article/c67ce5e63cd34f69b730ef8fededbcd9
Publikováno v:
EPJ Web of Conferences, Vol 245, p 06006 (2020)
We present preliminary results of the first convergent global fits of several minimal composite Higgs models. Our fits are performed using the differential evolution optimisation package Diver. A variety of physical constraints are taken into account
Externí odkaz:
https://doaj.org/article/5f3d4e652e814d3c953e6a3b2bec096f
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