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1.
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Snowmass 2021 Computational Frontier CompF03 Topical Group Report: Machine Learning
/ Shanahan, Phiala (MIT) ; Terao, Kazuhiro (SLAC) ; Whiteson, Daniel (UC, Irvine) ; Aarts, Gert (Swansea U. ; ECT, Trento ; Fond. Bruno Kessler, Trento) ; Adelmann, Andreas (Northeastern U. ; PSI, Villigen) ; Akchurin, N. (Texas Tech.) ; Alexandru, Andrei (George Washington U. ; Maryland U.) ; Amram, Oz (Johns Hopkins U.) ; Andreassen, Anders (Google Inc.) ; Apresyan, Artur (Fermilab) et al.
The rapidly-developing intersection of machine learning (ML) with high-energy physics (HEP) presents both opportunities and challenges to our community. [...]
arXiv:2209.07559 ; FERMILAB-CONF-22-719-ND-PPD-QIS-SCD.
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Fermilab Library Server - eConf - Fulltext - Fulltext
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2.
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A General Introduction to Machine Learning (whenever possible with a twist towards accelerators)
/ Adelmann, Andreas (speaker) (PSI)
Abstract:
This module will give an overview of Machine Learning (ML) and its methodologies and examples of applications. As an hors d'oeuvre, we will make a transition from statistics to machine learning using regression models. Then we will discover the beauty and power of deep neural networks - one of the most flexible approaches to supervised learning. Unsupervised Learning will free us from labeled data, as an application we look at clustering. The last method we will discover is reinforcement learning. [...]
2022 - 3803.
Academic Training Lecture Regular Programme, 2021-2022
External link: Event details
In : A General Introduction to Machine Learning (whenever possible with a twist towards accelerators)
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3.
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A General Introduction to Machine Learning (whenever possible with a twist towards accelerators)
/ Adelmann, Andreas (speaker) (PSI)
Abstract:
This module will give an overview of Machine Learning (ML) and its methodologies and examples of applications. As an hors d'oeuvre, we will make a transition from statistics to machine learning using regression models. Then we will discover the beauty and power of deep neural networks - one of the most flexible approaches to supervised learning. Unsupervised Learning will free us from labeled data, as an application we look at clustering. The last method we will discover is reinforcement learning. [...]
2022 - 5052.
Academic Training Lecture Regular Programme, 2021-2022
External link: Event details
In : A General Introduction to Machine Learning (whenever possible with a twist towards accelerators)
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4.
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5.
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Scientific opportunies for bERLinPro 2020+, report with ideas and conclusions from bERLinProCamp 2019
/ Kamps, Thorsten (Helmholtz-Zentrum, Berlin ; Humboldt U., Berlin (main)) ; Abo-Bakr, Michael (Helmholtz-Zentrum, Berlin) ; Adelmann, Andreas (PSI, Villigen) ; Andre, Kevin (CERN) ; Angal-Kalinin, Deepa ; Armborst, Felix (Helmholtz-Zentrum, Berlin) ; Arnold, Andre (HZDR, Dresden) ; Arnold, Michaela (Darmstadt, Tech. U.) ; Amador, Raymond (Humboldt U., Berlin (main)) ; Benson, Stephen (Jefferson Lab) et al.
The Energy Recovery Linac (ERL) paradigm offers the promise to generate intense electron beams of superior quality with extremely small six-dimensional phase space for many applications in the physical sciences, materials science, chemistry, health, information technology and security. [...]
arXiv:1910.00881.
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2019. - 7 p.
Fulltext
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6.
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Intensity limits of the PSI Injector II cyclotron
/ Kolano, Anna (CERN ; PSI, Villigen ; Huddersfield U.) ; Adelmann, Andreas (PSI, Villigen) ; Barlow, Roger (Huddersfield U.) ; Baumgarten, Christian (PSI, Villigen)
We investigate limits on the current of the PSI Injector II high intensity separate-sector isochronous cyclotron, in its present configuration and after a proposed upgrade. Accelerator Driven Subcritical Reactors, neutron and neutrino experiments, and medical isotope production all benefit from increases in current, even at the ~ 10% level: the PSI cyclotrons provide relevant experience. [...]
arXiv:1707.07970.-
2018-03-21 - 6 p.
- Published in : Nucl. Instrum. Methods Phys. Res., A 885 (2018) 54-59
Preprint: PDF;
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7.
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1.8 MW Upgrade of the PSI Proton Facility
/ Schmelzbach, Pierre A (PSI, Villigen) ; Adam, Stefan Rudolf Alfred (PSI, Villigen) ; Adelmann, Andreas (PSI, Villigen) ; Fitze, Hansruedi (PSI, Villigen) ; Heidenreich, Gerd (PSI, Villigen) ; Raguin, Jean-Yves (PSI, Villigen) ; Rohrer, Urs (PSI, Villigen) ; Sigg, Peter Kurt (PSI, Villigen)
2006 - 3 p.
External link: Published version from JACoW
In : 10th European Particle Accelerator Conference, Edinburgh, UK, 26 - 30 Jun 2006, pp.1879
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8.
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H5Part : A Portable High Performance Parallel Data Interface for Particle Simulations
/ Adelmann, Andreas ; Ryne, Robert D ; Shalf, John M ; Siegerist, Cristina
Largest parallel particle simulations, in six dimensional phase space generate wast amont of data. It is also desirable to share data and data analysis tools such as ParViT (Particle Visualization Toolkit) among other groups who are working on particle-based accelerator simulations. [...]
2005
External link: Published version from JACoW
In : 21st IEEE Particle Accelerator Conference, Knoxville, TN, USA, 16 - 20 May 2005, pp.4129
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9.
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From Visualisation to Data Mining with Large Data Sets
/ Adelmann, Andreas ; Ryne, Robert D ; Shalf, John M ; Siegerist, Cristina
In 3D particle simulations, the generated 6D phase space data are can be very large due to the need for accurate statistics, sufficient noise attenuation in the field solver and tracking of many turns in ring machines or accelerators. There is a need for distributed applications that allow users to peruse these extremely large remotely located datasets with the same ease as locally downloaded data. [...]
2005
External link: Published version from JACoW
In : 21st IEEE Particle Accelerator Conference, Knoxville, TN, USA, 16 - 20 May 2005, pp.4114
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10.
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Steps Towards a 3 mA, 1.8 MW Proton Beam at the PSI Cyclotron Facility
/ Schmelzbach, Pierre A ; Adam, Stefan R A ; Adelmann, Andreas ; Fitze, Hansruedi ; Heidenreich, Gerd ; Raguin, Jean-Yves ; Rohrer, Urs ; Sigg, Peter K
The PSI Cyclotron Facility produces routinely a 1.8-1.9 mA proton beam at 590 MeV. The beam power reaches 1.1 MW at the the pion production targets and 0.7 MW at the neutron spallation target SINQ. [...]
2005
External link: Published version from JACoW
In : 21st IEEE Particle Accelerator Conference, Knoxville, TN, USA, 16 - 20 May 2005, pp.2405
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