IMSA-CMS/CMSAnalysis
Apr 2025 – Feb 2026
8 Months active
Languages Used
C++ROOT
Technical Skills
C++ DevelopmentCode CommentingBDTC++Data AnalysisMachine Learning
Daniel Limosnero
PROFILE
Daniel Limosnero
Contributed to the IMSA-CMS/CMSAnalysis repository by developing and enhancing machine learning pipelines, data analysis tools, and scientific computing workflows for high energy physics research. Leveraged C++ and ROOT to implement Boosted Decision Tree models, automate lepton-jet reconstruction, and standardize fit function outputs across decay channels. Improved background estimation and visualization by extending toolkit capabilities for histogram manipulation and parameter graphing. Focused on reproducibility and maintainability through structured file handling, dynamic configuration, and detailed code documentation. Addressed edge-case bugs and streamlined onboarding by refining naming conventions and logging, resulting in robust, production-ready analytics and scalable data-driven model development.
Overall Statistics
Feature vs Bugs
82%Features
Repository Contributions
23Total
Bugs
2
Commits
23
Features
9
Lines of code
925,822
Activity Months8
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
February 2026
2 Commits • 1 Features
Feb 1, 2026In Feb 2026, IMSA-CMS/CMSAnalysis delivered substantial enhancements to the Particle Physics Data Analysis Toolkit, focusing on background estimation, visualization, and graphing capabilities. Centralized data analysis tooling improvements now support processing and visualizing particle physics data with robust background estimation and histogram manipulation. The ParameterizationFunctionGrapher was enhanced to plot background fit functions alongside signal fit functions, enabling direct validation and comparison. Changes were delivered via commits to ParameterizationFunctionGrapher.C (58f7132dd0016ded97e1dc794f2888c44fcf4971) and graphing background in ParameterizationFunctionGrapher.C (7a6da7b82ff49cbb73de8edb4cc12853a0665b16). Overall, this work increases analysis throughput, improves model validation, and strengthens the ability to derive data-driven background estimates for physics analyses.
2 Commits • 1 Features
Feb 1, 2026In Feb 2026, IMSA-CMS/CMSAnalysis delivered substantial enhancements to the Particle Physics Data Analysis Toolkit, focusing on background estimation, visualization, and graphing capabilities. Centralized data analysis tooling improvements now support processing and visualizing particle physics data with robust background estimation and histogram manipulation. The ParameterizationFunctionGrapher was enhanced to plot background fit functions alongside signal fit functions, enabling direct validation and comparison. Changes were delivered via commits to ParameterizationFunctionGrapher.C (58f7132dd0016ded97e1dc794f2888c44fcf4971) and graphing background in ParameterizationFunctionGrapher.C (7a6da7b82ff49cbb73de8edb4cc12853a0665b16). Overall, this work increases analysis throughput, improves model validation, and strengthens the ability to derive data-driven background estimates for physics analyses.
February 2026
November 2025
1 Commits • 1 Features
Nov 1, 2025November 2025 for IMSA-CMS/CMSAnalysis: Delivered the FitFunction Loader and Channel Organization feature, enabling loading FitFunctions from a file and organizing them by channel. This enhances data management, retrieval, and channel-specific analytics, contributing to faster experiment iteration and stronger data governance. No major bugs fixed this month. Overall impact: streamlined FitFunction ingestion, improved reproducibility, and a scalable foundation for future analytics.
November 2025
1 Commits • 1 Features
Nov 1, 2025November 2025 for IMSA-CMS/CMSAnalysis: Delivered the FitFunction Loader and Channel Organization feature, enabling loading FitFunctions from a file and organizing them by channel. This enhances data management, retrieval, and channel-specific analytics, contributing to faster experiment iteration and stronger data governance. No major bugs fixed this month. Overall impact: streamlined FitFunction ingestion, improved reproducibility, and a scalable foundation for future analytics.
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
Correctness82.6%
Maintainability82.6%
Architecture78.2%
Performance71.4%
AI Usage22.6%
Skills & Technologies
Programming Languages
C++ROOT
Technical Skills
Algorithm DesignAlgorithm ImplementationBDTBug FixC++C++ DevelopmentC++ programmingCode CommentingData AnalysisData StructuresFile HandlingHiggs Physics AnalysisHigh Energy PhysicsMachine LearningROOT
Repositories Contributed To
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