Zobrazeno 1 - 10
of 2 582
pro vyhledávání: '"Pierini Maurizio"'
Autor:
Tsoi Ho Fung, Pol Adrian Alan, Loncar Vladimir, Govorkova Ekaterina, Cranmer Miles, Dasu Sridhara, Elmer Peter, Harris Philip, Ojalvo Isobel, Pierini Maurizio
Publikováno v:
EPJ Web of Conferences, Vol 295, p 09036 (2024)
The high-energy physics community is investigating the potential of deploying machine-learning-based solutions on Field-Programmable Gate Arrays (FPGAs) to enhance physics sensitivity while still meeting data processing time constraints. In this cont
Externí odkaz:
https://doaj.org/article/817d76a9edcc44f6a31f1c9c7616cb98
Autor:
Odagiu, Patrick, Que, Zhiqiang, Duarte, Javier, Haller, Johannes, Kasieczka, Gregor, Lobanov, Artur, Loncar, Vladimir, Luk, Wayne, Ngadiuba, Jennifer, Pierini, Maurizio, Rincke, Philipp, Seksaria, Arpita, Summers, Sioni, Sznajder, Andre, Tapper, Alexander, Aarrestad, Thea K.
Three machine learning models are used to perform jet origin classification. These models are optimized for deployment on a field-programmable gate array device. In this context, we demonstrate how latency and resource consumption scale with the inpu
Externí odkaz:
http://arxiv.org/abs/2402.01876
Autor:
Orzari, Breno, Chernyavskaya, Nadezda, Cobe, Raphael, Duarte, Javier, Fialho, Jefferson, Gunopulos, Dimitrios, Kansal, Raghav, Pierini, Maurizio, Tomei, Thiago, Touranakou, Mary
Publikováno v:
Mach. Learn.: Sci. Technol. 4 045023 (2023)
In high energy physics, one of the most important processes for collider data analysis is the comparison of collected and simulated data. Nowadays the state-of-the-art for data generation is in the form of Monte Carlo (MC) generators. However, becaus
Externí odkaz:
http://arxiv.org/abs/2310.13138
Autor:
Pol, Adrian Alan, Govorkova, Ekaterina, Gronroos, Sonja, Chernyavskaya, Nadezda, Harris, Philip, Pierini, Maurizio, Ojalvo, Isobel, Elmer, Peter
Unsupervised deep learning techniques are widely used to identify anomalous behaviour. The performance of such methods is a product of the amount of training data and the model size. However, the size is often a limiting factor for the deployment on
Externí odkaz:
http://arxiv.org/abs/2310.06047
Autor:
Pol Adrian Alan, Aarrestad Thea, Govorkova Katya, Halily Roi, Kopetz Tal, Klempner Anat, Loncar Vladimir, Ngadiuba Jennifer, Pierini Maurizio, Sirkin Olya, Summers Sioni
Publikováno v:
EPJ Web of Conferences, Vol 251, p 04027 (2021)
We apply object detection techniques based on Convolutional Neural Networks to jet reconstruction and identification at the CERN Large Hadron Collider. In particular, we focus on CaloJet reconstruction, representing each event as an image composed of
Externí odkaz:
https://doaj.org/article/cdaa8b5f1b46459e8e7aac20a08d5b03
Publikováno v:
EPJ Web of Conferences, Vol 251, p 03072 (2021)
The high-luminosity upgrade of the LHC will come with unprecedented physics and computing challenges. One of these challenges is the accurate reconstruction of particles in events with up to 200 simultaneous protonproton interactions. The planned CMS
Externí odkaz:
https://doaj.org/article/d48f5a1655a04727a9da5e9a36fd723e
Autor:
Woźniak Kinga Anna, Cerri Olmo, Duarte Javier M., Möller Torsten, Ngadiuba Jennifer, Nguyen Thong Q., Pierini Maurizio, Spiropulu Maria, Vlimant Jean-Roch
Publikováno v:
EPJ Web of Conferences, Vol 245, p 06039 (2020)
We discuss a model-independent strategy for boosting new physics searches with the help of an unsupervised anomaly detection algorithm. Prior to a search, each input event is preprocessed by the algorithm - a variational autoencoder (VAE). Based on t
Externí odkaz:
https://doaj.org/article/f070f05f774541e8917af87521ee9c1a
Autor:
Anzalone, Luca, Chhibra, Simranjit Singh, Maier, Benedikt, Chernyavskaya, Nadezda, Pierini, Maurizio
Publikováno v:
Mach. Learn.: Sci. Technol. 5 (2024) 035064
We present a family of conditional dual auto-encoders (CoDAEs) for generic and model-independent new physics searches at colliders. New physics signals, which arise from new types of particles and interactions, are considered in our study as anomalie
Externí odkaz:
http://arxiv.org/abs/2306.12955
Autor:
Shenoy, Rohan, Duarte, Javier, Herwig, Christian, Hirschauer, James, Noonan, Daniel, Pierini, Maurizio, Tran, Nhan, Suarez, Cristina Mantilla
Publikováno v:
Mach. Learn.: Sci. Technol. 4 (2023) 045058
The Earth mover's distance (EMD) is a useful metric for image recognition and classification, but its usual implementations are not differentiable or too slow to be used as a loss function for training other algorithms via gradient descent. In this p
Externí odkaz:
http://arxiv.org/abs/2306.04712
Publikováno v:
SciPost Phys. 16, 123 (2024)
The Neyman-Pearson strategy for hypothesis testing can be employed for goodness of fit if the alternative hypothesis is selected from data by exploring a rich parametrised family of models, while controlling the impact of statistical fluctuations. Th
Externí odkaz:
http://arxiv.org/abs/2305.14137