Alan Watson L1Calo Upgrade Meeting 1 EM Rejection in Phase1 Developments since Stockholm: Using depth information aloneUsing depth information alone Using transverse granularityUsing transverse granularity EM-Jet DisambiguationEM-Jet Disambiguation

Alan Watson L1Calo Upgrade Meeting 2 Methodology A 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 → e 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

Alan Watson L1Calo Upgrade Meeting 3 Transverse Granularity at L0? What might be possible?  FEB unchanged  4-channel sums in shapers  Told summed in  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×  /32  Strip/Mid: 0.025×  /32  Back: 0.05×  /16  Note: For the purpose of the study - not assuming all of these will be available.

Alan Watson L1Calo Upgrade Meeting 4 Fine-Granularity Algorithms RoI 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 E T cluster

Alan Watson L1Calo Upgrade Meeting 5 Jet Vetoes Studied Current L1Calo (analogue inputs)  EMIsol, HadIsol, HadCore – cuts on E T values Depth Only  EM back sample E T – cut on E T, fraction of EM cluster (digital)  Digitised at 100 MeV/layer. No negative layer E T. Transverse Granularity  Various shower width tests in layers 1 and 2  e.g. ratio of E T in 3x2/7x2 cells in layer 2  “L0 cells” digitised with 50 MeV count, no negative cell E T.

Alan Watson L1Calo Upgrade Meeting 6 Caveats Background 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.

Alan Watson L1Calo Upgrade Meeting 7 Single Cuts. EM23 RoIs, Signal Efficiency ≈ 98% Cut variable and valueSignal εJF17 Survivial EM Isolation ≤ Had Isolation ≤ Had Core ≤ EM Cluster, Back Sample < 1.0 GeV EM Cluster, Back Sample/Total < EM Layer 2, 3×2/7×2 > EM Layer 1, 2x2/4x2 >  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)

Alan Watson L1Calo Upgrade Meeting 8 Single Cuts. EM23 RoIs, Signal Efficiency ≈ 95% Cut variable and valueSignal εJF17 Survivial EM Isolation ≤ Had Isolation ≤ Had Core ≤ EM Cluster, Back Sample < 0.5 GeV EM Cluster, Back Sample/Total < EM Layer 2, 3×2/7×2 > EM Layer 1, 2x2/4x2 >  Note that had core cut cannot be tightened to 95% efficiency  EM layer 2 cluster width cut clearly most powerful now

Alan Watson L1Calo Upgrade Meeting 9 Two-Cut Combinations, Signal Efficiency ≈ 98% Cut variable and valueSignal εJF17 Survivial Had Core ≤ 1 + EM Isolation ≤ Had Core ≤ 1 + EM Back/Total < Had Core ≤ 2 + EM2 3×2/7×2 > Had Core ≤ 1 + EM2 3×2/7×2 > Had Core ≤ 1 + EM1 2×2/4×2 > EM2 3/7 > EM Back/Total <  Cluster width cuts show useful gains in rejection  But quite sensitive to cut value – arithmetical precision required

Alan Watson L1Calo Upgrade Meeting 10 Two-Cut Combinations, Signal Efficiency ≈ 95% Cut variable and valueSignal εJF17 Survivial Had Core ≤ 1 + EM Isolation ≤ Had Core ≤ 1 + EM Back/Total < Had Core ≤ 1 + EM2 3×2/7×2 > Had Core ≤ 1 + EM1 2×2/4×2 > EM2 3/7 > EM Back/Total <  Gains from cluster width more significant – mid layer width cut dominates rejection

Alan Watson L1Calo Upgrade Meeting 11 Three-Cut Combos, Signal Efficiency ≈ 98% Cut variable and valueSignal εJF17 Survivial Had Core≤1+EM Isolation≤ 3+Had Isol≤ Had Core≤1+EM Isolation≤10+EM2 3/7> Had Core≤1+EM Isolation≤ 4 +EM2 3/7> Had Core≤2+EM2 3/7 > EM1 2/4> HadCore≤2+EM2 3/7>0.89+EM Back/Tot< Had Core≤1+EM Isol≤ 4 +EM Back/Tot<  No real gain over the two cut combinations for same efficiency  Question simplicity vs robustness?  Best-performing combinations dominated by 2 cuts

Alan Watson L1Calo Upgrade Meeting 12 Three-Cut Combos, Signal Efficiency ≈ 95% Cut variable and valueSignal εJF17 Survivial Had Core≤0+EM Isolation≤ 2+Had Isol≤ Had Core≤2+EM Isolation≤ 5+EM2 3/7> Had Core≤1+EM Isolation≤ 4 +EM2 3/7> Had Core≤2+EM2 3/7 > EM1 2/4> HadCore≤2+EM2 3/7>0.89+EM Back/Tot< Had Core≤1+EM Isol≤ 4 +EM Back/Tot<  Again, little if any gain over two cut combinations.  Combinations including mid-layer width cut distinctly better than others

Alan Watson L1Calo Upgrade Meeting 13 Rate Comparison (unnormalised) – ε = 98% x2.5x3.5

Alan Watson L1Calo Upgrade Meeting 14 Rate Comparison (unnormalised) – ε = 95% x2.6 5 x4.5

Alan Watson L1Calo Upgrade Meeting 15 Comparison with Denis’/Steve’s Results Cuts 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?

Alan Watson L1Calo Upgrade Meeting 16 Quick Cross-Checks Compare 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 E T values.

Alan Watson L1Calo Upgrade Meeting 17 Algorithm Effects: Layer 2 Seeding Previous 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 RoIs Very rushed  Last thing before holiday!  Very limited statistics (few hundred signal, ~1k background events)

Alan Watson L1Calo Upgrade Meeting 18 Effect of Purer Layer 2 Seeding Removing 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.

Alan Watson L1Calo Upgrade Meeting 19 Tentative Conclusions Concrete 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 E T  Greater gain from tightening 98% → 95% efficiency that current L1 cuts  Signs of greater gains at lower RoI E T  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

Alan Watson L1Calo Upgrade Meeting 20 EM-JET DISAMBIGUATION

Alan Watson L1Calo Upgrade Meeting 21 The Problem of Combined Triggers Current 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 E T  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)

Alan Watson L1Calo Upgrade Meeting 22 Our Original Phase 1 Proposal Resolve 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?

Alan Watson L1Calo Upgrade Meeting 23 Another Quick and Dirty Study Same JF17 samples as before (  ×filter =  b)  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

Alan Watson L1Calo Upgrade Meeting 24 EM10+Jet vs Jet ET. No disambiguation 20 2E34

Alan Watson L1Calo Upgrade Meeting 25 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

Alan Watson L1Calo Upgrade Meeting 26 Effect of Disambiguation – EM10I + Jet Similar gains at mid ET Still merge at high ET

Alan Watson L1Calo Upgrade Meeting 27 Rate Improvement vs E T  Statistics poor, but indication that gains larger for more realistic EM20I trigger  Isolation also more effective EM10: gain bit under factor 2 at best EM20I: gain almost factor of 4. No statistics at higher ET

Alan Watson L1Calo Upgrade Meeting 28 EM+TAU Disambiguation Harder problem, as objects more similar Gain ~20% over broad range of TAU ET

Alan Watson L1Calo Upgrade Meeting 29 TAU + Jet More like EM + Jet

Alan Watson L1Calo Upgrade Meeting 30 Fine-Grain Isolation Plus Disambiguation The rejection from isolation alone seems large…

Alan Watson L1Calo Upgrade Meeting 31 Fine-Grain Isolation Plus Disambiguation Fractional gain better than with weaker isolation

Alan Watson L1Calo Upgrade Meeting 32 More Tentative Conclusions EM/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!