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====== Public results ====== | ====== Public results ====== | ||
- | ======Publications====== | + | ====== |
- | Since 2013, there was a number of publications | + | Since 2013, there was a number of articles |
* A. V. Kotwal, S. Chekanov, M. Low, Double Higgs Production in the 4τ channel from resonances in longitudinal vector boson scattering at a 100 TeV collider, [[http:// | * A. V. Kotwal, S. Chekanov, M. Low, Double Higgs Production in the 4τ channel from resonances in longitudinal vector boson scattering at a 100 TeV collider, [[http:// | ||
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* J. Clavijo, P.Glaysher, J.Katzy, Adversarial domain adaptation to reduce sample bias of a high energy physics, DESY 20-073, arXiv: | * J. Clavijo, P.Glaysher, J.Katzy, Adversarial domain adaptation to reduce sample bias of a high energy physics, DESY 20-073, arXiv: | ||
* M.T. Lucchini et. al., "New perspectives on segmented crystal calorimeters for future colliders", | * M.T. Lucchini et. al., "New perspectives on segmented crystal calorimeters for future colliders", | ||
+ | * S. V. Chekanov, G. Gavalian, N. A. Graf, Jas4pp - a Data-Analysis Framework for Physics and Detector Studies, Comp. Physics. Comm. 262 (2021) 107857, (https:// | ||
+ | * S.Chekanov, Machine Learning Using Rapidity-Mass Matrices for Event Classification Problems in HEP. Universe, 2021, 7(1):19. https:// | ||
+ | * J.Pata, et.al. MLPF: Efficient machine-learned particle-flow reconstruction using graph neural networks. [[https:// | ||
+ | * S.V. Chekanov, S. Darmora, W. Islam, C.E.M. Wagner, J. Zhang, Model-independent searches for new physics in multi-body invariant masses, ANL-HEP-166648, | ||
+ | * Frank E. Taylor, Applications of pT-xR Variables in Describing Inclusive Cross Sections at the LHC. https:// | ||
+ | * S.V.Chekanov, | ||
+ | * S. Darmora et. al, Signal optimization studies for dijet resonances in events with identified leptons using machine learning, June 2021, ATL-COM-PHYS-2021-391, | ||
+ | * S.V. Chekanov, W. Hopkins, Event-based anomaly detection for new physics searches at the LHC using machine learning, https:// | ||
+ | * S.Chekanov et al, " | ||
+ | * B. Nachman et al, Jets and Jet Substructure at Future Colliders, [[https:// | ||
+ | * F. Mokhtar et al., Explaining machine-learned particle-flow reconstruction, | ||
+ | * ATLAS Collaboration, | ||
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There are also several ATLAS papers and a number of supporting notes that used Monte Carlo files from the HEPSIM repository. | There are also several ATLAS papers and a number of supporting notes that used Monte Carlo files from the HEPSIM repository. | ||
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* S.Chekanov, A.V.Kotwal, J.List, | * S.Chekanov, A.V.Kotwal, J.List, | ||
* Chih-Hsiang Yeh et al. Timing layers. 4th FCC Physics and Experiments Workshop, Nov 11, 2020, CERN [[https:// | * Chih-Hsiang Yeh et al. Timing layers. 4th FCC Physics and Experiments Workshop, Nov 11, 2020, CERN [[https:// | ||
+ | * S.Chekanov, S.Magill, Towards simulations of HHCAL for future detectors, CalVision workshop, Univ of Maryland, 29-30 July 2021, PDF file | ||
+ | * S.Chekanov, Machine learning and anomaly detection using rapidity-mass matrices PDF file ISMD2021. 50th International Symposium on Multiparticle Production. (July 12-July 16, as a poster) | ||
+ | * Wasikul I. Model-Independent Searches for New Physics in Multi-Body Invariant Masses, APS April Meeting 2021, https:// | ||
+ | * S.Chekanov et al., Jas4pp. A Data-Analysis Framework for Physics and Detector Studies. APS April Meeting 2021, https:// | ||
+ | * S.Chekanov et. al, Calorimeter performance studies using Monte Carlo simulations for future collider detectors. CPAD Instrumentation Frontier Workshop 2021, 18-22 March 2021, Stony Brook, NY (https:// | ||
+ | * J.Crosby, " | ||
+ | * S.Chekanov, Event-based anomaly detection for new physics searches at the LHC using machine learning, APS April Meeting, Apr 8-14, 2022, https:// | ||
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