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researcher

I. Ojalvo

18 papers hereh-index 7324.1k citations342 works total

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

author position
  • middle author9
  • last author3

Across the 12 of 18 papers where every author was matched, so the position is known.

fields
  • physics.ins-det6
  • hep-ex5
  • cs.LG3
  • hep-ph2
  • physics.acc-ph1
  • physics.comp-ph1
same name
  • I. Ojalvo — 356 papers
  • I. Ojalvo — 57 papers
  • I. Ojalvo — 30 papers
  • I. Ojalvo — 24 papers, h 70
  • I. Ojalvo — 14 papers
  • I. Ojalvo — 9 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
20202026
most citedGraph Neural Networks for Charged Particle Tracking on FPGAs

39 citations · 113 across the 12 of their papers we have counts for

collaborators
Showing 2021Show all

4 papers · 1 filter

physics.ins-det2021★ 39 cited

Graph Neural Networks for Charged Particle Tracking on FPGAs

Abdelrahman Elabd, Vesal Razavimaleki, Shi-Yu Huang +12

The determination of charged particle trajectories in collisions at the CERN Large Hadron Collider (LHC) is an important but challenging problem, especially in the high interaction…

physics.ins-det2021

Test beam characterization of sensor prototypes for the CMS Barrel MIP Timing Detector

R. Abbott, A. Abreu, F. Addesa +196

The MIP Timing Detector will provide additional timing capabilities for detection of minimum ionizing particles (MIPs) at CMS during the High Luminosity LHC era, improving event re…

hep-ph2021

The Muon Smasher's Guide

Hind Al Ali, Nima Arkani-Hamed, Ian Banta +31

We lay out a comprehensive physics case for a future high-energy muon collider, exploring a range of collision energies (from 1 to 100 TeV) and luminosities. We highlight the advan…

hep-ex2021

Charged particle tracking via edge-classifying interaction networks

Gage DeZoort, Savannah Thais, Javier Duarte +5

Recent work has demonstrated that geometric deep learning methods such as graph neural networks (GNNs) are well suited to address a variety of reconstruction problems in high energ…

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