Zobrazeno 1 - 10
of 54
pro vyhledávání: '"Nguyen Thong Q."'
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
We present Semantic Interpreter, a natural language-friendly AI system for productivity software such as Microsoft Office that leverages large language models (LLMs) to execute user intent across application features. While LLMs are excellent at unde
Externí odkaz:
http://arxiv.org/abs/2306.03460
Autor:
Govorkova, Ekaterina, Puljak, Ema, Aarrestad, Thea, James, Thomas, Loncar, Vladimir, Pierini, Maurizio, Pol, Adrian Alan, Ghielmetti, Nicolò, Graczyk, Maksymilian, Summers, Sioni, Ngadiuba, Jennifer, Nguyen, Thong Q., Duarte, Javier, Wu, Zhenbin
Publikováno v:
Nature Machine Intelligence 4, 154 (2022)
In this paper, we show how to adapt and deploy anomaly detection algorithms based on deep autoencoders, for the unsupervised detection of new physics signatures in the extremely challenging environment of a real-time event selection system at the Lar
Externí odkaz:
http://arxiv.org/abs/2108.03986
We present a fast simulation application based on a Deep Neural Network, designed to create large analysis-specific datasets. Taking as an example the generation of W+jet events produced in sqrt(s)= 13 TeV proton-proton collisions, we train a neural
Externí odkaz:
http://arxiv.org/abs/2010.01835
Autor:
Knapp, Oliver, Dissertori, Guenther, Cerri, Olmo, Nguyen, Thong Q., Vlimant, Jean-Roch, Pierini, Maurizio
We apply an Adversarially Learned Anomaly Detection (ALAD) algorithm to the problem of detecting new physics processes in proton-proton collisions at the Large Hadron Collider. Anomaly detection based on ALAD matches performances reached by Variation
Externí odkaz:
http://arxiv.org/abs/2005.01598
Autor:
Martinez, Jesus Arjona, Nguyen, Thong Q, Pierini, Maurizio, Spiropulu, Maria, Vlimant, Jean-Roch
Publikováno v:
J. Phys. : Conf. Ser. 1525 (2020) 012081
We investigate how a Generative Adversarial Network could be used to generate a list of particle four-momenta from LHC proton collisions, allowing one to define a generative model that could abstract from the irregularities of typical detector geomet
Externí odkaz:
http://arxiv.org/abs/1912.02748
Autor:
Moreno, Eric A., Nguyen, Thong Q., Vlimant, Jean-Roch, Cerri, Olmo, Newman, Harvey B., Periwal, Avikar, Spiropulu, Maria, Duarte, Javier M., Pierini, Maurizio
Publikováno v:
Phys. Rev. D 102, 012010 (2020)
We develop an algorithm based on an interaction network to identify high-transverse-momentum Higgs bosons decaying to bottom quark-antiquark pairs and distinguish them from ordinary jets that reflect the configurations of quarks and gluons at short d
Externí odkaz:
http://arxiv.org/abs/1909.12285
Autor:
Moreno, Eric A., Cerri, Olmo, Duarte, Javier M., Newman, Harvey B., Nguyen, Thong Q., Periwal, Avikar, Pierini, Maurizio, Serikova, Aidana, Spiropulu, Maria, Vlimant, Jean-Roch
Publikováno v:
Eur. Phys. J. C 80, 58 (2020)
We investigate the performance of a jet identification algorithm based on interaction networks (JEDI-net) to identify all-hadronic decays of high-momentum heavy particles produced at the LHC and distinguish them from ordinary jets originating from th
Externí odkaz:
http://arxiv.org/abs/1908.05318
Publikováno v:
J. High Energ. Phys. (2019) 2019: 36
Using variational autoencoders trained on known physics processes, we develop a one-sided threshold test to isolate previously unseen processes as outlier events. Since the autoencoder training does not depend on any specific new physics signature, t
Externí odkaz:
http://arxiv.org/abs/1811.10276
Autor:
Nguyen, Thong Q., Weitekamp III, Daniel, Anderson, Dustin, Castello, Roberto, Cerri, Olmo, Pierini, Maurizio, Spiropulu, Maria, Vlimant, Jean-Roch
Publikováno v:
Comput Softw Big Sci (2019) 3: 12
We show how event topology classification based on deep learning could be used to improve the purity of data samples selected in real time at at the Large Hadron Collider. We consider different data representations, on which different kinds of multi-
Externí odkaz:
http://arxiv.org/abs/1807.00083