Relational DatabasesRelational Databases

Maintaining Sanity in aMaintaining Sanity in a

Modern Partial Wave AnalysisModern Partial Wave Analysis

Douglas Applegate

April 19, 2023

Carnegie Mellon University

A Brief Discussion ofA Brief Discussion of Numerical Stability inNumerical Stability in

MinuitMinuit

Also:Also:

OutlineOutline

The ProblemThe Problem• Too much information, too many fitsToo much information, too many fits

Advantages of a MySQL solutionAdvantages of a MySQL solution Overview of the database setupOverview of the database setup

Minuit numerical stabilityMinuit numerical stability• Is there a problem?Is there a problem?

Output of a FitOutput of a Fit

What do you need to know?What do you need to know?• LikelihoodLikelihood• Parameter valuesParameter values• Errors on valuesErrors on values

What is nice to have?What is nice to have?• YieldsYields• Cross SectionsCross Sections

for each channel, spin,

projection, etc…

The FitInfo ClassThe FitInfo Class

All fit output in one locationAll fit output in one location No text parsing No text parsing (but we still create a txt file!)(but we still create a txt file!)

ROOT compatibleROOT compatible• Stored in root fileStored in root file

Scaling IssuesScaling Issues

For an analysis:For an analysis:

150 fits X 75 bins X 10 iterations/bin =150 fits X 75 bins X 10 iterations/bin =

112,500112,500 FitInfo root files! FitInfo root files!

Other IssuesOther Issues

Accessibility of informationAccessibility of information• Want to make a dynamic status Want to make a dynamic status

webpagewebpage Code modularityCode modularity

• IO code and analysis code shouldn’t mixIO code and analysis code shouldn’t mix

Solution: Solution: A Relational DatabaseA Relational Database

(MySQL)(MySQL)

The Relational Database SolutionThe Relational Database Solution

Dataset

•Name, Location

W bin

•Range, Flux

Fit

•Name, Location, Description

Iteration

•Number, Likelihood, Location

Amplitude

A Very Fancy Index

Some SQLSome SQL Dataset NameDataset Name

SELECT Name FROM Dataset WHERE Dataset_id = %d;

Best Iteration (in 3 statements)CREATE TEMPORARY TABLE temp_minlh (rec_id int not null

auto_increment primary key, minVal float(10) not null);

INSERT INTO temp_minlh (minVal) SELECT MIN(Likelihood) AS best FROM Iteration WHERE Wbin_id = %d AND Fit_id = %d GROUP BY Fit_id;

SELECT Iteration_number FROM Iteration, temp_minlh WHERE Iteration.Likelihood = minVal AND Wbin_id = %d AND Fit_id = %d GROUP BY Likelihood;

The Relational Database SolutionThe Relational Database Solution

ROOT Users Web Page…

C++ Interface

MySQL

(Index Service)

File System

Stand Alone Apps

Possible applications

C++ Interface ClassC++ Interface Class

ModularityModularity• MySQL code hiddenMySQL code hidden• SQL syntax hiddenSQL syntax hidden• File IO hiddenFile IO hidden• Memory management of FitInfo classes Memory management of FitInfo classes

hiddenhidden LightweightLightweight ROOT compatibleROOT compatible

MinuitMinuitNumerical StabilityNumerical Stability

Is There A Problem?Is There A Problem?

Numerical StabilityNumerical Stability

Are parameter values consistent?Are parameter values consistent? Are reported errors meaningful?Are reported errors meaningful?

Minuit Parameter ValuesMinuit Parameter Values

Yield ValuesYield Values

Yield ValuesYield Values

Errors on YieldsErrors on Yields

Error on YieldsError on Yields

Numerical StabilityNumerical Stability

Are parameter values consistent?Are parameter values consistent?• YesYes

Are reported errors meaningful?Are reported errors meaningful?• Consistent (if HESSE is called)Consistent (if HESSE is called)

ConclusionsConclusions

Relational Databases:Relational Databases:• Organized, easily searchable resultsOrganized, easily searchable results• Code modularityCode modularity• Ease of useEase of use

MinuitMinuit• For a given likelihood, results are For a given likelihood, results are

consistentconsistent• Errors are consistent (with HESSE)Errors are consistent (with HESSE)