|
1.
|
|
|
2.
|
|
|
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.
-
Fermilab Library Server - eConf - Fulltext - Fulltext
|
|
|
3.
|
|
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)
|
|
|
4.
|
|
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)
|
|
|
5.
|
|
|
6.
|
Uncertainty quantification analysis and optimization for proton therapy beam lines
/ Rizzoglio, V (PSI, Villigen ; CERN) ; Adelmann, A (PSI, Villigen) ; Gerbershagen, A (PSI, Villigen ; CERN) ; Meer, D (PSI, Villigen) ; Nesteruk, K P (PSI, Villigen) ; Schippers, J M (PSI, Villigen)
Since many years proton therapy is an effective treatment solution against deep-seated tumors. A precise quantification of sources of uncertainty in each proton therapy aspect (e.g. [...]
2020 - 8 p.
- Published in : Physica Medica 75 (2020) 11-18
|
|
|
7.
|
|
|
Search for a muon EDM using the frozen-spin technique
/ Adelmann, A. (ETH, Zurich (main) ; PSI, Villigen) ; Backhaus, M. (ETH, Zurich (main)) ; Chavez Barajas, C. (Liverpool U.) ; Berger, N. (Mainz U., Inst. Phys.) ; Bowcock, T. (Liverpool U.) ; Calzolaio, C. (PSI, Villigen) ; Cavoto, G. (Rome U. ; INFN, Rome) ; Chislett, R. (University Coll. London) ; Crivellin, A. (PSI, Villigen ; CERN ; U. Zurich (main)) ; Daum, M. (PSI, Villigen) et al.
This letter of intent proposes an experiment to search for an electric dipole moment of the muon based on the frozen-spin technique. [...]
arXiv:2102.08838.
-
28 p.
Fulltext
|
|
|
8.
|
|
|
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.
-
2019. - 7 p.
Fulltext
|
|
|
9.
|
|
|
Opportunities in Machine Learning for Particle Accelerators
/ Edelen, A. (SLAC) ; Mayes, C. (SLAC) ; Bowring, D. (Fermilab) ; Ratner, D. (SLAC) ; Adelmann, A. (PSI, Villigen) ; Ischebeck, R. (PSI, Villigen) ; Snuverink, J. (PSI, Villigen) ; Agapov, I. (DESY) ; Kammering, R. (DESY) ; Edelen, J. (RadiaSoft, Boulder) et al.
Machine learning (ML) is a subfield of artificial intelligence. [...]
arXiv:1811.03172 ; FERMILAB-PUB-19-017-AD.
-
2018. - 25 p.
Fermilab Library Server (fulltext available) - Fulltext - Fulltext
|
|
|
10.
|
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;
|
|
email alert
or subscribe to the 
RSS feed.