alan watson l1calo upgrade meeting 1 em rejection in phase1 developments since stockholm: using...

Alan Watson L1Calo Upgrade Meeting 1

EM Rejection in Phase1

Developments since Stockholm:

•Using depth information alone

•Using transverse granularity

•EM-Jet Disambiguation


MethodologyA brief MC study – not much changed since

Stockholm Form towers by summing CaloCells. Keep finer-granularity subsums as well as complete tower sums Enchanced transverse granularity plus depth information. No cell noise cuts applied. No simulation of noise from layer summing.

1,500 MC10 W → en as signal, 1,700 JF17 as background Medium electrons used for signal efficiency study

Pileup included (46 mbias/crossing)Algorithm simulation Current EM trigger, with standard (analogue) inputs

Same algorithm on digital inputs Finer-granularity sums formed at same time

Match “digital” RoI to “analogue” and combine features


Transverse Granularity at L0?What might be

possible? FEB unchanged 4-channel sums in shapers

Told summed in f direction in mid layer – need confirmation in endcap

Change Layer Sum/Backplane as well as Tower Builder Digital sums with transverse granularity as well as depth

Simulated granularities

PS: 0.1×p/32 Strip/Mid: 0.025×p/32 Back: 0.05×p/16

Note: For the purpose of the study - not assuming all of these will be available.


Fine-Granularity AlgorithmsRoI location based on current algorithm

(2x2 core = max)Most energetic

layer 2 cell (within

central 2x2

region)Most energetic

neighbour in phi

(above or below)Add neighbours in

eta to form cluster

Wider eta

environment for

isolation/rejection

Add overlapping cells in other layers to

form ET cluster


Jet Vetoes StudiedCurrent L1Calo (analogue inputs) EMIsol, HadIsol, HadCore – cuts on ET values

Depth Only EM back sample ET – cut on ET, fraction of EM

cluster (digital) Digitised at 100 MeV/layer. No negative layer ET.

Transverse Granularity Various shower width tests in layers 1 and 2 e.g. ratio of ET in 3x2/7x2 cells in layer 2 “L0 cells” digitised with 50 MeV count, no negative cell

ET.


CaveatsBackground dataset: JF17 QCD events that have been filtered at 4-vector level to exclude events highly unlikely to pass triggers Possibility that these are biassed to narrower jets/denser cores. Rejection might differ slightly with minBias.

Choice of cuts: signal statistics Rejection is sensitive to precise cut value. Statistical fluctuations in signal sample may lead to looser/tighter cut giving required efficiency. Beware of making too fine distinctions from these data.


Single Cuts. EM23 RoIs, Signal

Efficiency ≈ 98% Cut variable and value Signal ε JF17 Survivial

EM Isolation ≤ 2 0.977 0.78

Had Isolation ≤ 1 0.98 0.72

Had Core ≤ 0 0.98 0.52

EM Cluster, Back Sample < 1.0 GeV 0.998 0.65

EM Cluster, Back Sample/Total < 0.02 0.99 0.60

EM Layer 2, 3×2/7×2 > 0.90 0.98 0.37

EM Layer 1, 2x2/4x2 > 0.84 0.98 0.41

Typically ~5% statistical uncertainty on background rejection

Showing only cluster width definitions that give best performance in each layer

EM back layer fraction cut requires very fine tuning (sub-percent)


Single Cuts. EM23 RoIs, Signal


EM Isolation ≤ 2 0.946 0.62

Had Isolation ≤ 0 0.946 0.63

Had Core ≤ 0 0.98 0.52

EM Cluster, Back Sample < 0.5 GeV 0.96 0.55

EM Cluster, Back Sample/Total < 0.02 0.99 0.60

EM Layer 2, 3×2/7×2 > 0.92 0.96 0.28

EM Layer 1, 2x2/4x2 > 0.88 0.96 0.38

Note that had core cut cannot be tightened to 95% efficiency

EM layer 2 cluster width cut clearly most powerful now


Two-Cut Combinations, Signal


Had Core ≤ 1 + EM Isolation ≤ 2 0.988 0.45

Had Core ≤ 1 + EM Back/Total < 0.02 0.99 0.43

Had Core ≤ 2 + EM2 3×2/7×2 > 0.90 0.98 0.30

Had Core ≤ 1 + EM2 3×2/7×2 > 0.89 0.98 0.30

Had Core ≤ 1 + EM1 2×2/4×2 > 0.84 0.98 0.35

EM2 3/7 > 0.89 + EM Back/Total < 0.02

0.98 0.32

Cluster width cuts show useful gains in rejection But quite sensitive to cut value – arithmetical precision required


Two-Cut Combinations, Signal


Had Core ≤ 1 + EM Isolation ≤ 1 0.946 0.40

Had Core ≤ 1 + EM Back/Total < 0.02 0.99 0.43

Had Core ≤ 1 + EM2 3×2/7×2 > 0.92 0.96 0.23

Had Core ≤ 1 + EM1 2×2/4×2 > 0.87 0.95 0.30

EM2 3/7 > 0.89 + EM Back/Total < 0.02

0.98 0.32

Gains from cluster width more significant – mid layer width cut dominates rejection


Three-Cut Combos, Signal


Had Core≤1+EM Isolation≤ 3+Had Isol≤ 2 0.98 0.43

Had Core≤1+EM Isolation≤10+EM2 3/7>0.90 0.98 0.28

Had Core≤1+EM Isolation≤ 4 +EM2 3/7>0.89 0.98 0.30

Had Core≤2+EM2 3/7 > 0.89 +EM1 2/4>0.65 0.98 0.30

HadCore≤2+EM2 3/7>0.89+EM Back/Tot<0.02 0.98 0.30

Had Core≤1+EM Isol≤ 4 +EM Back/Tot<0.02

0.98 0.38

No real gain over the two cut combinations for same efficiency Question simplicity vs robustness?

Best-performing combinations dominated by 2 cuts


Three-Cut Combos, Signal


Had Core≤0+EM Isolation≤ 2+Had Isol≤ 3 0.96 0.40

Had Core≤2+EM Isolation≤ 5+EM2 3/7>0.92 0.96 0.25

Had Core≤1+EM Isolation≤ 4 +EM2 3/7>0.92 0.95 0.23

Had Core≤2+EM2 3/7 > 0.89 +EM1 2/4>0.86 0.95 0.23

HadCore≤2+EM2 3/7>0.89+EM Back/Tot<0.02 0.98 0.30

Had Core≤1+EM Isol≤ 4 +EM Back/Tot<0.02

0.98 0.38

Again, little if any gain over two cut combinations.

Combinations including mid-layer width cut distinctly better than others


Rate Comparison (unnormalised)

– ε = 98%

x2.

5

x3.

5


Rate Comparison (unnormalised)

– ε = 95%

x2.

65

x4.

5


Comparison with Denis’/Steve’s

ResultsCuts for given efficiency slightly looser Hence rejection is not quite as good.Possible reasons Data preparation? Calibration or noise handling differences

Cluster seeding? My layer 2 cluster location is partly determined by L1 algorithm, rather than maximum being entirely determined by layer 2 cells

Datasets or statistics?


Quick Cross-ChecksCompare with L2 variable Use T2CaRcore variable in ntuple Match RoI word to L1 RoI Results: 98% (95%) efficiency ⇒ 25% (24%) JF17 survival Compared with 37% (28%) above But still not quite as good as Denis saw – difference due to dataset, analysis?

Sensitivity to cell noise cuts Repeat with layer 2 4-cell sums truncated to 250 MeV counts

Results: 99% (96%) efficiency ⇒ 32% (23%) JF17 survival Actually slightly better for coarser digitisation! Cut values were slightly harder, presumably noise suppression effect

Would need to check effect for other RoI ET values.


Algorithm Effects: Layer 2

SeedingPrevious seeding was constrained by L1 algorithm

Find RoI location using current algorithm Look for maximal cell within layer 2 inside 2x2 tower core region

Remove this constraint on seeding Just look for maxima within layer 2 Match RoIs found this way to L1 RoIsVery rushed Last thing before holiday! Very limited statistics (few hundred signal, ~1k background events)


Effect of Purer Layer 2 SeedingRemoving constraint does sharpen

efficiency curve As used in studies above Pure layer 2 seeding

Also seems to produce slightly better rejection Pretty similar to L2 algorithm. Very preliminary study though.


Tentative ConclusionsConcrete gains possible from ECAL transverse

granularity Not quite as strong as reported by Denis & Steve (S) Algorithm differences seem to be partial explanation.

Combining 3/7 cell cluster fraction with hadronic isolation most powerful Modest gains from adding third cut Not tested systematically at lower RoI ET

Greater gain from tightening 98% → 95% efficiency that current L1 cuts

Signs of greater gains at lower RoI ET

Need confirmation, ideally with minBias sample (check for filter bias)?

Caveats Low-stats study, not tried to optimise tower noise cuts for lumi

Fine granularity implementation not fully realistic (RoI definition)

Precise (percent-level) precision used in fraction calculations


EM-JET DISAMBIGUATION


The Problem of Combined

TriggersCurrent L1 uses only multiplicities So if I want an EM + Jet trigger, or EM + TAU, how do I ensure these are not the same object?

Easy if both have same ET

Any EM20 passes J20, so ask for EM20 + 2J20 and all is well

Also OK if EM more energetic than jet But that’s useless in practice!Tricky when jet more energetic than EM Best you can do is something like

EM20 + 2J20 + J50 …but even then, nothing stops the EM20 and J50 being same object

Isolation potentially complicates this (but I think issues overstated)


Our Original Phase 1 ProposalResolve ambiguities (in TP or CMX) Match EM/TAU/Jet RoIs Decide whether a distinct pair passes the trigger requirement

Determined that only modest coordinate precision (jet element size) needed.

But what gain does it bring us? Depends on trigger menu, of course But have we ever actually studied this?


Another Quick and Dirty StudySame JF17 samples as before (s×filter =

231.39 mb) Choose some baseline threshold – 10 or 20 GeV Normalise to events passing balanced combination trigger EMx + 2Jx, EMx + 2TAUx, TAUx + 2Jx Include isolated EM, TAU

Look at rate vs ET of more inclusive object (Jet or TAU)

Disambiguation Find most energetic TAU/Jet distinct from EMx/TAUx Repeat for all EMx/TAUx RoIs, to find highest-ET disambiguated TAU/Jet in event

Plot fraction of events passing disambiguated trigger Normalised to balanced combination trigger, as above

Estimate improvement in rate from disambiguation


EM10+Jet vs Jet ET. No

disambiguation

20 kHz @

2E34


Effect of Disambiguation – EM10

+ Jet

Main gain is

when jet ET is 2-

3xEM threshold

At high ET

most events

have another

jet passing

EM10


Effect of Disambiguation – EM10I

+ Jet

Similar

gains at

mid ET

Still merge at

high ET


Rate Improvement vs ET Statistics poor, but indication that gains larger for more realistic EM20I trigger Isolation also more effective

EM10: gain bit

under factor 2

at bestEM20I: gain almost

factor of 4. No

statistics at higher

ET


EM+TAU DisambiguationHarder problem, as objects more similar

Gain ~20% over

broad range of

TAU ET


TAU + Jet More like EM + Jet


Fine-Grain Isolation Plus

DisambiguationThe rejection from isolation alone seems large…


Fine-Grain Isolation Plus

DisambiguationFractional gain better than with weaker isolation


More Tentative ConclusionsEM/Tau-Jet Disambiguation Could be useful, even promising, if kinematic range between EM/Tau and jet not too large

Hints that stronger isolation (better jet rejection) improves this

EM-Tau Disambiguation More difficult to make major gains over current solution. Could still be useful in making efficiencies more comprehensible

Need example use cases And more statistics!

alan watson l1calo upgrade meeting 1 em rejection in phase1 developments since stockholm: using...

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