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* 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. 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.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, | ||
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* S.Chekanov et al., Jas4pp. A Data-Analysis Framework for Physics and Detector Studies. 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:// | * 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:// | ||
- | * S.Chekanov et al, "Precision timing | + | * J.Crosby, "Searches |
+ | * 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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