refactor: Stable RNG for smeared digitization in Examples

[andiwand]

Our smeared digitization will create very different random numbers if the simulations changes. This can be unexpected for pile-up studies where you would think your measurements from the hard scatter stay the same and you just get more pile-up hits.

I am trying to improve the situation by creating stable random number streams for each particle on a sensitive. To avoid correlations for multiple hits of a single particle on a single surface (should only happen for loopers) I introduced an index which is also part of the seed for the rng.

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[github-actions]

📊: Physics performance monitoring for a3036b6

Full contents

physmon summary

• ✅ Particles fatras
• ✅ Particles geant4
• ✅ Particles ttbar
• ✅ Vertices ttbar
• 🔴 Truth tracking (KF)
• 🔴 Truth tracking (GSF)
• 🔴 Truth tracking (GX2F)
• 🔴 Truth tracking (KF refit)
• 🔴 Truth tracking (GSF refit)
• 🔴 CKF finding performance | trackfinding | single muon | truth smeared seeding
• 🔴 CKF fitting performance | trackfinding | single muon | truth smeared seeding
• 🔴 CKF track summary | trackfinding | single muon | truth smeared seeding
• ✅ Seeding trackfinding | single muon | truth estimated seeding
• 🔴 CKF finding performance | trackfinding | single muon | truth estimated seeding
• 🔴 CKF fitting performance | trackfinding | single muon | truth estimated seeding
• 🔴 CKF track summary | trackfinding | single muon | truth estimated seeding
• ✅ Seeding trackfinding | single muon | default seeding
• 🔴 CKF finding performance | trackfinding | single muon | default seeding
• 🔴 CKF fitting performance | trackfinding | single muon | default seeding
• 🔴 CKF track summary | trackfinding | single muon | default seeding
• ✅ Seeding trackfinding | single muon | orthogonal seeding
• 🔴 CKF finding performance | trackfinding | single muon | orthogonal seeding
• 🔴 CKF fitting performance | trackfinding | single muon | orthogonal seeding
• 🔴 CKF track summary | trackfinding | single muon | orthogonal seeding
• ✅ Seeding trackfinding | 4 muon x 50 vertices | default seeding
• 🔴 CKF finding performance | trackfinding | 4 muon x 50 vertices | default seeding
• 🔴 CKF fitting performance | trackfinding | 4 muon x 50 vertices | default seeding
• 🔴 CKF track summary | trackfinding | 4 muon x 50 vertices | default seeding
• 🔴 Ambisolver finding performance | trackfinding | 4 muon x 50 vertices | default seeding
• 🔴 IVF notime | trackfinding | 4 muon x 50 vertices | default seeding
• 🔴 AMVF gauss notime | trackfinding | 4 muon x 50 vertices | default seeding
• 🔴 AMVF grid time | trackfinding | 4 muon x 50 vertices | default seeding
• 🔴 Seeding trackfinding | ttbar with 200 pileup | default seeding
• 🔴 CKF finding performance | trackfinding | ttbar with 200 pileup | default seeding
• 🔴 CKF fitting performance | trackfinding | ttbar with 200 pileup | default seeding
• 🔴 CKF track summary | trackfinding | ttbar with 200 pileup | default seeding
• 🔴 Ambisolver finding performance | trackfinding | ttbar with 200 pileup | default seeding
• 🔴 AMVF gauss notime | trackfinding | ttbar with 200 pileup | default seeding
• 🔴 AMVF grid time | trackfinding | ttbar with 200 pileup | default seeding

[andiwand]

Tested this locally and works fine

[andiwand]

cc @benjaminhuth @paulgessinger @asalzburger

I don't understand how this changes physmon so much? Did we have some weird correlations before? Or do we now?

[benjaminhuth]

One thing I'm not sure is if the the properties of any / this particular RNG are similar in situations like

rng = rng_from_seed(seed)
seq1 = [ rng() for  i in range(N) ]
# vs
seq2 = [ rng_from_seed(i)() for i in range(N) ]

Because effectively we have a measurement unique RNG, that is only called once or twice, right?

Another thing one could try to improve the quality of the seed is maybe using std::seed_seq, instead of just summing up the ids.

[andiwand]

looks like std::seed_seq only does 32 bits

[sonarcloud]

Quality Gate passed

Issues
1 New issue
0 Accepted issues

Measures
0 Security Hotspots
0.0% Coverage on New Code
0.0% Duplication on New Code

See analysis details on SonarQube Cloud