Asigra Cloud Backup Partner Summit to Focus on MSPs (Slides)
Backup Slides
description
Transcript of Backup Slides
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Backup Slides
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Moments Correlations
Stat Stat+Exp+BR Stat+Exp+BR+Theo
m 74% 48% (no BR) -
M 92% 62% -
-99% -88% -77%
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Vcb: exclusive determination
• Measure absolute scale of BD*l• D** states also important for |Vcb|
exclusive determination– end-point in q2 for BD*l decays– systematic uncertainty from BD**l
background
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Channels with neutral B
• B0 D**+ l-
– D**+ D0 + OK– D**+ D+ 0 Not reconstructed. Half the rate of D+ -
– D**+ D*0 +
• D*0 D0 0 Not reconstructed. Background to D0 +
• D*0 D0 Not reconstructed. Background to D0 +
– D**+ D*+ 0 Not reconstructed. Half the rate of D*+ -
We will not deal with neutral B
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Data StabilityA: (152595-154012) Before winter 2003 shutdown
B: (158826-165297) After winter 2003 shutdown
C: (164303-165297) SVT 4/5
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Kinematic Comparisons lD*, D0K
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Kinematic Comparisons lD*, D0K0
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Kinematic Comparisons: D+
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Can we “predict” yields?
Two methods (a,b) to derive this BR
a) Based on inclusive bD(*)+l
b) Based on exclusive BD(*)+l, D**l
+PDG BR + MC efficiency ratios
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What background model for what?
• WS is often used in this kind of analyses as a model for the background
• We can also use our fully reco’d B from other triggers
• We choose to use WS for the optimization
• Embedding is being used as a cross-check for systematics
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D**D0
• No background subtraction
• ~80 events in D*+
• ~80 events in D+
• ~215 events on D0
uncertainty > sqrt(n)
What’s available on the market…D**D+
D**D*
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Estimator Behaviour
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K Optimization
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D+ Optimization
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Combinatorial Background• WS **
– Already used for the optimization– Physics can be different
• Fully reco. B– independent emulation of the background– Limited statistics– Needs some machinery for emulating a semileptonic decay!
• Eliminate the B daughters• Replace the B with a semileptonic B with the same 4-momentum:
a template montecarlo where the B decay comes from EvtGen and the rest of the event comes from the data!
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Background Modeling II
Lxy>500m
•Tight cuts (avoid subtractions)
•Exclude B tracks
•Replace with MC B
•QuickCdfObjects/GenTrig
•Re-decay N times
•Same analysis path from there on
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Signal Fits
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Sample Consistency
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Embedded MC vs Semileptonics
MC yield scaled to number of data events
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• Theory prediction depends on Pl* cuts. We cannot do much but:
– see how our analysis bias looks like– Use a threshold-like correction– Evaluate systematics for different threshold values
Pl*
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MC efficiencies(M) is dependent on:
•D** decay Model
•Pl* cut
•Use different models/cuts to evaluate systematics
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MC Validation* is an unique probe:
•Large statistics
•Low background
•“Similar” spectrum to **
•Can reconstruct with minimal cuts (e.g. COT only)
•Technique:
•Search for * with very loose cuts
•Do not include in B vertex
•Study biases to kinematics from tracking
•Study IP resolution(data/MC): Primary, B & D vertices
•Study (data/MC) vs selection criteria
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MC validation•Cross-check kinematic variables
•B spectrum modeling
•Trigger emulation
•Validate CdfSim model of tracking resolution
•Relative efficiencies
** selection/bias•Compare many data/MC distributions using binned 2
•Every possible decay mode
•Sideband subtracted before comparison
•Duplicate removal (D0K)
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Kinematics•Can we rely on kinematical biases estimated from MC?
•Rem: we don’t care about absolute scales
•Pt dependent MC/data ratio:
400 MeV
* Pt
MC
Data
MC/Data vs Pt
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Impact Parameters148/34
26/30
151/45
134/32
61/30
118/43
40/33
42/42
58/52
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Impact Parameters (covr)40/33
550/40
124/49
133/32
39/29
137/43
146/34
25/30
146/45
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(MC), (data) vs selection criteria
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Another perspective: MC(after/before) / data(after/before)
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MC(after/before) / data(after/before)Plan for the evaluation of
systematics
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Ds Background
•Use peak in D+ candidates to set the scale
•Measure the relative contribution of Ds decays to D+ fakes using MC
•Extrapolate to the total size of the contribution: 4%
•Build a suitable D** background model
•Subtract
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Cross-feeds
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Cross-feeds (details, sat.)
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Cross-feeds (details, K)
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Cross-feeds (details K)
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D** Moments
• Combine all events of all types in all channels (D*,D+,SRS,SBRS,feed-down, etc.)
• Compute mean (m1) and variance (m2) of M2 distribution with weighted events.
• Errors and correlation computed with MC (for toy MC) or bootstrap (for data).
• For some realizations, one finds a negative value for m2 = Var(M2) = <M4> - <M2>2.
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Inputs for the D0 and D*0 Contributions
• For the BR’s, results from charged and neutral B decays are combined using isospin: partial widths are assumed equal.
• BR’s, ratio of lifetimes and ratio of production fractions are taken from PDG.
• Toy Monte Carlo is used to propagate the uncertainties from m1, m2, the BR’s, etc., to uncertainties on M1 and M2 and their correlation.
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