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Lukas Heinrich

4 papers hereh-index 4154 citations9 works total

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

author position
  • middle author4

Across the 4 of 4 papers where every author was matched, so the position is known.

fields
  • cs.DC1
  • hep-ex1
  • hep-ph1
  • stat.CO1
same name
  • Lukas Heinrich — 15 papers, h 13
  • Lukas Heinrich — 10 papers, h 7
  • Lukas Heinrich — 5 papers, h 1
  • Lukas Heinrich — 4 papers, h 3
  • Lukas Heinrich — 3 papers, h 0
  • Lukas Heinrich — 2 papers, h 3

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
20212026
most citedDistributed statistical inference with pyhf enabled through funcX

3 citations · 3 across the 4 of their papers we have counts for

collaborators

4 papers

hep-ph2026

pylhe: A Lightweight Python interface to Les Houches Event files

Alexander Puck Neuwirth, Matthew Feickert, Lukas Heinrich +1

Les Houches Event files are a standard format for Monte Carlo event generators in high-energy physics. pylhe is a lightweight pure-Python library for reading and writing LHE event…

stat.CO2023

Bayesian Methodologies with pyhf

Matthew Feickert, Lukas Heinrich, Malin Horstmann

bayesian_pyhf is a Python package that allows for the parallel Bayesian and frequentist evaluation of multi-channel binned statistical models. The Python library pyhf is used to bu…

hep-ex2022

pyhf: pure-Python implementation of HistFactory with tensors and automatic differentiation

Matthew Feickert, Lukas Heinrich, Giordon Stark

The HistFactory p.d.f. template is per-se independent of its implementation in ROOT and it is useful to be able to run statistical analysis outside of the ROOT, RooFit, RooStats fr…

cs.DC2021★ 3 cited

Distributed statistical inference with pyhf enabled through funcX

Matthew Feickert, Lukas Heinrich, Giordon Stark +1

In High Energy Physics facilities that provide High Performance Computing environments provide an opportunity to efficiently perform the statistical inference required for analysis…

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