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researcher

T. Tomei

95 papers hereh-index 6221.6k citations475 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • sole author4
  • middle author6
  • last author2

Across the 12 of 95 papers where every author was matched, so the position is known.

fields
  • hep-ex77
  • nucl-ex7
  • physics.data-an3
  • physics.ins-det3
  • hep-ph2
  • physics.comp-ph2
same name
  • T. Tomei — 57 papers, h 0
  • T. Tomei — 4 papers

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20132026
most citedCombined measurements of Higgs boson couplings in proton-proton collisions at s​= 13 TeV

442 citations · 2.9k across the 95 of their papers we have counts for

collaborators
Showing 2022Show all

4 papers · 1 filter

hep-ex2022

The High-Level Trigger for the CMS Phase-2 Upgrade

Thiago R. F. P. Tomei

The High-Luminosity LHC (HL-LHC) will usher in a new era in high-energy physics. The HL-LHC experimental conditions entail an instantaneous luminosity of up to 7.5×1034…

hep-ex2022★ 57 cited

Evaluating generative models in high energy physics

Raghav Kansal, Anni Li, Javier Duarte +4

There has been a recent explosion in research into machine-learning-based generative modeling to tackle computational challenges for simulations in high energy physics (HEP). In or…

hep-ex2022

Background Monte Carlo Samples for a Future Hadron Collider

Robert Gardner, Simone Pagan Griso, Stefan Hoeche +14

A description of Standard Model background Monte Carlo samples produced for studies related to future hadron colliders.

physics.comp-ph2022★ 21 cited

Particle-based Fast Jet Simulation at the LHC with Variational Autoencoders

Mary Touranakou, Nadezda Chernyavskaya, Javier Duarte +6

We study how to use Deep Variational Autoencoders for a fast simulation of jets of particles at the LHC. We represent jets as a list of constituents, characterized by their momenta…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.