IMSA-CMS/CMSAnalysis
Apr 2025 – Oct 2025
6 Months active
Languages Used
C++ROOT
Technical Skills
C++ DevelopmentCode CommentingBDTC++Data AnalysisMachine Learning
Daniel Limosnero
PROFILE
Daniel Limosnero
Over six months, contributed to IMSA-CMS/CMSAnalysis by developing and refining machine learning pipelines and data analysis tools for high energy physics research. Work included integrating Boosted Decision Tree models for dark photon Higgs analysis, standardizing fit function outputs, and improving lepton-jet reconstruction logic. Leveraged C++, ROOT, and scientific computing techniques to enhance reproducibility, streamline configuration management, and ensure robust parameter handling across decay channels. Addressed edge-case bugs in file naming and logging, while maintaining clear documentation and commit traceability. These efforts improved analysis consistency, data quality, and maintainability, supporting more efficient and reliable workflows for CMS physics analyses.
Overall Statistics
Feature vs Bugs
78%Features
Repository Contributions
20Total
Bugs
2
Commits
20
Features
7
Lines of code
359,313
Activity Months6
Your Network
23 peopleShared Repositories
23
Aarya BajaJMember
Anwita KasturiMember
Anwita KasturiMember
Jayram PalamadaiMember
Blake HenningsMember
Brooke HobbsMember
fCameron EddingtonMember
Hannah ChenMember
Izen WongMember
Work History
October 2025
2 Commits • 1 Features
Oct 1, 2025October 2025 monthly summary for IMSA-CMS/CMSAnalysis: Implemented muon channel naming standardization and alphabetical organization of fit parameters to improve analysis consistency and readability. Also fixed a logging label bug to ensure accurate channel data is recorded in logs. These changes enhance data quality, traceability, and maintainability, supporting more reliable automated reporting.
2 Commits • 1 Features
Oct 1, 2025October 2025 monthly summary for IMSA-CMS/CMSAnalysis: Implemented muon channel naming standardization and alphabetical organization of fit parameters to improve analysis consistency and readability. Also fixed a logging label bug to ensure accurate channel data is recorded in logs. These changes enhance data quality, traceability, and maintainability, supporting more reliable automated reporting.
October 2025
September 2025
8 Commits • 2 Features
Sep 1, 2025September 2025 monthly performance summary for IMSA-CMS/CMSAnalysis. Focused on delivering structured and standardized output for fit functions and Higgs parameters by channel, stabilizing file naming conventions, and enhancing parameter saving with dynamic channel suffixing. The work improves data reproducibility, downstream analysis readiness, and robustness of parameter streaming across decay channels.
September 2025
8 Commits • 2 Features
Sep 1, 2025September 2025 monthly performance summary for IMSA-CMS/CMSAnalysis. Focused on delivering structured and standardized output for fit functions and Higgs parameters by channel, stabilizing file naming conventions, and enhancing parameter saving with dynamic channel suffixing. The work improves data reproducibility, downstream analysis readiness, and robustness of parameter streaming across decay channels.
August 2025
2 Commits • 1 Features
Aug 1, 2025Month: 2025-08 — IMSA-CMS/CMSAnalysis: Delivered key feature to enable LeptonJetSelector in LeptonJetReconstructionPlan and streamlined lepton-jet reconstruction by deactivating FakePhotonSelector; fixed LeptonJetSelector logic to improve reconstruction accuracy. These changes enhance signal efficiency, reduce background noise, and provide a clear, maintainable path for downstream analyses. All work is tracked via commit references and prepared for production integration.
2 Commits • 1 Features
Aug 1, 2025Month: 2025-08 — IMSA-CMS/CMSAnalysis: Delivered key feature to enable LeptonJetSelector in LeptonJetReconstructionPlan and streamlined lepton-jet reconstruction by deactivating FakePhotonSelector; fixed LeptonJetSelector logic to improve reconstruction accuracy. These changes enhance signal efficiency, reduce background noise, and provide a clear, maintainable path for downstream analyses. All work is tracked via commit references and prepared for production integration.
August 2025
July 2025
4 Commits • 1 Features
Jul 1, 2025Month: 2025-07 — Implemented end-to-end ML-assisted analysis for dark photon Higgs in IMSA-CMS/CMSAnalysis, integrating a BDT-based classifier, updating plotting and configuration for training and data analysis, and retraining the Higgs125 model. Fixed critical data handling and filtering issues, delivering ML artifacts and a second MLOutput graph to LeptonJetReconstructionPlan.cc. These efforts enhance signal discrimination, reduce manual tuning, and accelerate production-ready analysis in the CMS workflow.
July 2025
4 Commits • 1 Features
Jul 1, 2025Month: 2025-07 — Implemented end-to-end ML-assisted analysis for dark photon Higgs in IMSA-CMS/CMSAnalysis, integrating a BDT-based classifier, updating plotting and configuration for training and data analysis, and retraining the Higgs125 model. Fixed critical data handling and filtering issues, delivering ML artifacts and a second MLOutput graph to LeptonJetReconstructionPlan.cc. These efforts enhance signal discrimination, reduce manual tuning, and accelerate production-ready analysis in the CMS workflow.
June 2025
2 Commits • 1 Features
Jun 1, 2025June 2025 — IMSA-CMS/CMSAnalysis: Delivered a focused feature upgrade to the ML training pipeline by retraining the Boosted Decision Tree (BDT) model and updating configuration to support new datasets, TMVA output naming, and BDT structure changes. No major bugs reported this month; the work improves training reproducibility and prepares for future model refresh cycles. Technologies demonstrated include Boosted Decision Trees, TMVA, dataset/configuration management, and version-controlled ML pipelines. Business impact: more reliable model updates, clearer training artifacts, and faster iteration for data-driven CMS analyses.
2 Commits • 1 Features
Jun 1, 2025June 2025 — IMSA-CMS/CMSAnalysis: Delivered a focused feature upgrade to the ML training pipeline by retraining the Boosted Decision Tree (BDT) model and updating configuration to support new datasets, TMVA output naming, and BDT structure changes. No major bugs reported this month; the work improves training reproducibility and prepares for future model refresh cycles. Technologies demonstrated include Boosted Decision Trees, TMVA, dataset/configuration management, and version-controlled ML pipelines. Business impact: more reliable model updates, clearer training artifacts, and faster iteration for data-driven CMS analyses.
June 2025
April 2025
2 Commits • 1 Features
Apr 1, 2025April 2025: Documentation-focused improvement in IMSA-CMS/CMSAnalysis; added inline developer presence comments in MLTrain.C to enhance auditability; no functional changes.
April 2025
2 Commits • 1 Features
Apr 1, 2025April 2025: Documentation-focused improvement in IMSA-CMS/CMSAnalysis; added inline developer presence comments in MLTrain.C to enhance auditability; no functional changes.
Activity
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Quality Metrics
Correctness83.0%
Maintainability83.0%
Architecture78.0%
Performance71.0%
AI Usage22.0%
Skills & Technologies
Programming Languages
C++ROOT
Technical Skills
Algorithm ImplementationBDTBug FixC++C++ DevelopmentCode CommentingData AnalysisFile HandlingHiggs Physics AnalysisHigh Energy PhysicsMachine LearningROOTScientific ComputingSoftware DevelopmentSoftware Engineering
Repositories Contributed To
Overview of all repositories you've contributed to across your timeline