2025-11-15 09:02 |
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CMS FlashSim: End-to-end simulation with Machine Learning
/ Rizzi, Andrea (Pisa U. ; INFN, Pisa) ; Vaselli, Francesco (Pisa, Scuola Normale Superiore ; INFN, Pisa) ; Cattafesta, Filippo (Pisa, Scuola Normale Superiore ; INFN, Pisa) ; Asenov, Patrick (Pisa U. ; INFN, Pisa)
/CMS Collaboration
Detailed event simulation at the LHC is taking a large fraction of computing budget. CMS developed an end-to-end ML based simulation that can speed up the time for production of analysis samples of several orders of magnitude with a limited loss of accuracy. [...]
2025 - 12 p.
- Published in : EPJ Web Conf. 337 (2025) 01014
Fulltext: PDF;
In : 27th International Conference on Computing in High Energy & Nuclear Physics (CHEP2024), Kraków, Poland, 19 - 25 Oct 2024, pp.01014
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Anomaly Detection in the CMS L1 Trigger
/ Quinnan, Melissa (UC, San Diego)
/CMS Collaboration
We present the preparation, deployment, and testing of an autoen-coder trained for unbiased detection of new physics signatures in the CMS experiment Global Trigger (GT) test crate FPGAs during LHC Run 3. The GT makes the final decision whether to readout or discard the data from each LHC collision, which occur at a rate of 40 MHz, within a 50 ns latency. [...]
2025 - 6 p.
- Published in : EPJ Web Conf. 337 (2025) 01032
Fulltext: PDF;
In : 27th International Conference on Computing in High Energy & Nuclear Physics (CHEP2024), Kraków, Poland, 19 - 25 Oct 2024, pp.01032
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$\text{EVENTSETPROCESSOR}$: An Engine for Efficiently Combining High-Energy Physics Data
/ de Geus, Florine Willemijn (CERN ; Twente U., Enschede) ; Padulano, Vincenzo Eduardo (CERN) ; Blomer, Jakob (CERN) ; Mühleisen, Hannes (Amsterdam, CWI) ; Varbanescu, Ana-Lucia (Twente U., Enschede)
CERN’s Large Hadron Collider (LHC), the world’s largest high-energy physics (HEP) instrument, collects tens of petabytes of data per year. The LHC’s next phase is expected to produce up to ten times more data, which calls for novel, more efficient ways of storing and processing these data.HEP collider data are prepared and provided to physicists as read-only data sets, stored in a custom columnar data format. [...]
2025 - 9 p.
- Published in : 10.1109/eScience65000.2025.00020
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Exploring Hybrid Designs for a 14 T Common Coil Demonstrator Magnet (DAISY)
/ García-Matos, Jesús Ángel (Madrid, CIEMAT ; Comillas Pontifical U.) ; Martins Jardim, Carla (Madrid, CIEMAT) ; Toral, Fernando (Madrid, CIEMAT) ; Perez, Juan Carlos (CERN) ; Todesco, Ezio (CERN)
Building on the initial magnetic design of DAISY,
the 14 T common coil magnet demonstrator being developed at
CIEMAT under the High Field Magnet (HFM) programme, this pa-
per investigates the feasibility of hybrid designs combining Nb3Sn
and NbTi for high- and low-field regions, respectively. The primary
goal is to minimise superconductor usage while ensuring that the
magnet meets the functional requirements of future collider appli-
cations, such as the Future Circular Hadron Collider (FCC-hh).
The study examines the potential to achieve accelerator-grade field
quality without ancillary coils and to maintain minimal multi-
pole variation between low and nominal currents, highlighting
the trade-offs in superconductor efficiency. [...]
2026 - 5 p.
- Published in : IEEE Trans. Appl. Supercond. 36 (2026) 1-5
Fulltext: PDF; External link: Fulltext
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