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CERN Document Server 73 elementer funnet  1 - 10nesteslutt  gå til element: Søket tok 0.64 sekunder. 
1.
Making Likelihood Calculations Fast: Automatic Differentiation Applied to RooFit / Singh, Garima (CERN ; Princeton U.) ; Rembser, Jonas (CERN) ; Moneta, Lorenzo (CERN) ; Lange, David (Princeton U.) ; Vassilev, Vassil (Princeton U.)
With the growing datasets of current and next-generation HighEnergy and Nuclear Physics (HEP/NP) experiments, statistical analysis has become more computationally demanding. These increasing demands elicit improvements and modernizations in existing statistical analysis software. [...]
2024 - 7 p. - Published in : EPJ Web Conf. 295 (2024) 06014 Fulltext: PDF;
In : 26th International Conference on Computing in High Energy & Nuclear Physics, Norfolk, Virginia, Us, 8 - 12 May 2023, pp.06014
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2.
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Overview of AI activities at CERN / Moneta, Lorenzo (speaker) (CERN)
2024 - 1145. Workshops and Training; 2024 CERN openlab Technical Workshop External links: Talk details; Event details In : 2024 CERN openlab Technical Workshop
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3.
New RooFit Developments to Speed up your Analysis / Wolffs, Zef (Nikhef, Amsterdam) ; Bos, Patrick (Unlisted, NL) ; Burgard, Carsten (Tech. U., Dortmund (main)) ; Michalainas, Emmanouil (CERN ; Aristotle U., Thessaloniki) ; Moneta, Lorenzo (CERN) ; Rembser, Jonas (CERN) ; Verkerke, Wouter (Nikhef, Amsterdam)
As the field of high energy physics moves to an era of precision measurements its models become ever more complex and so do the challenges for computational frameworks that intend to fit these models to data. This report describes two computational optimizations with which RooFit intends to address this challenge: parallelization and batched computations. [...]
2022 - 6 p. - Published in : PoS ICHEP2022 (2022) 249 Fulltext: PDF;
In : 41st International Conference on High Energy Physics (ICHEP 2022), Bologna, Italy, 6 - 13 Jul 2022, pp.249
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4.
C++ Code Generation for Fast Inference of Deep Learning Models in ROOT/TMVA / An, Sitong (CERN ; Carnegie Mellon U.) ; Moneta, Lorenzo (CERN) ; Sengupta, Sanjiban (Bhubaneswar, Inst. Phys.) ; Hamdan, Ahmat (Yaounde U.) ; Sossai, Federico (U. Padua (main)) ; Saxena, Aaradhya (IIT, Roorkee)
We report the latest development in ROOT/TMVA, a new tool that takes trained ONNX deep learning models and emits C++ code that can be easily included and invoked for fast inference of the model, with minimal dependency. An introduction to SOFIE (System for Optimized Fast Inference code Emit) is presented, with examples of interface and generated code. [...]
2023 - 5 p. - Published in : J. Phys. : Conf. Ser. 2438 (2023) 012013 Fulltext: PDF;
In : 20th International Workshop on Advanced Computing and Analysis Techniques in Physics Research (ACAT 2021), Daejeon, Korea, 29 Nov - 3 Dec 2021, pp.012013
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5.
Automatic Differentiation of Binned Likelihoods With Roofit and Clad / Singh, Garima (Princeton U.) ; Rembser, Jonas (CERN) ; Moneta, Lorenzo (CERN) ; Lange, David (Princeton U.) ; Vassilev, Vassil (Princeton U.)
RooFit is a toolkit for statistical modeling and fitting used by most experiments in particle physics. [...]
arXiv:2304.02650. - 6 p.
Fulltext
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6.
Unravelling the JPMorgan spoofing case using particle physics visualization methods / Debie, Philippe (Wageningen U.) ; Gardebroek, Cornelis (Wageningen U.) ; Hageboeck, Stephan (CERN) ; van Leeuwen, Paul (Unlisted, NL) ; Moneta, Lorenzo (CERN) ; Naumann, Axel (CERN) ; Pennings, Joost M E (Wageningen U. ; Maastricht U. ; Illinois U., Urbana) ; Trujillo‐Barrera, Andres A (Idaho U.) ; Verhulst, Marjolein E (Wageningen U.)
On 29 September 2020, JPMorgan was ordered to pay a settlement of $920.2 million for spoofing the metals and Treasury futures markets from 2008 to 2016. We examine these cases using a visualization method developed in particle physics (CERN) and the messages that the exchange receives about market activity rather than time-based snapshots. [...]
2022 - 39 p. - Published in : Eur. Financ. Manag. 29 (2022) 288-326 Fulltext: PDF;
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7.
Annual Report 2022 / Aglieri, Gianluca
This report summarises the activities and main achievements of the CERN strategic R&D programme on technologies for future experiments during the year 2022
CERN-EP-RDET-2023-002 - 100.

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8.
Extension of the R&D Programme on Technologies for Future Experiments / Joram, Christian
we have conceived an extension of the R&D programme covering the period 2024 to 2028, i.e [...]
CERN-EP-RDET-2023-001 -

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9.
Acceleration with GPUs and other RooFit news / Michalainas, Emmanouil (Aristotle University of Thessaloniki (GR)) ; Rembser, Jonas (CERN) ; Hageboeck, Stephan (CERN) ; Moneta, Lorenzo (CERN)
RooFit is a toolkit for statistical modeling and fitting, and together with RooStats it is used for measurements and statistical tests by most experiments in particle physics, particularly the LHC experiments. [...]
CERN-OPEN-2022-014. - 2023. - 6 p.
Preprint
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10.
HL-LHC Analysis With ROOT / Naumann, Axel (CERN) ; Canal, Philippe (Fermilab) ; Tejedor, Enric (CERN) ; Guiraud, Enrico (CERN) ; Moneta, Lorenzo (CERN) ; Bellenot, Bertrand (CERN) ; Couet, Olivier (CERN) ; Tadel, Alja Mrak (UC, San Diego) ; Tadel, Matevz (UC, San Diego) ; Linev, Sergey (Darmstadt, GSI) et al.
ROOT is high energy physics' software for storing and mining data in a statistically sound way, to publish results with scientific graphics. [...]
arXiv:2205.06121 ; FERMILAB-TM-2774-SCD. - 46.
Fermilab Library Server - Fulltext - Fulltext
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83 MONETA, Lorenzo
248 Moneta, L
29 Moneta, L.
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