Zobrazeno 1 - 10
of 628
pro vyhledávání: '"Cerri Olmo"'
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 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:
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
At the Large Hadron Collider, the high transverse-momentum events studied by experimental collaborations occur in coincidence with parasitic low transverse-momentum collisions, usually referred to as pileup. Pileup mitigation is a key ingredient of t
Externí odkaz:
http://arxiv.org/abs/1810.07988
Autor:
Cerri, Olmo
As part of the Phase II upgrade program, the Compact Muon Solenoid (CMS) detector will incorporate a new timing layer designed to measure minimum ionizing particles (MIPs) with a time resolution of $\sim$30 ps. Precision timing will mitigate the impa
Externí odkaz:
http://arxiv.org/abs/1810.00860
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
$W$ bosons are produced at LHC from a forward-backward symmetric initial state. Their decay to a charged lepton and a neutrino has a strong spin analysing power. The combination of these effects results in characteristic distributions of the pseudora
Externí odkaz:
http://arxiv.org/abs/1707.09344