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Prefilters are defined in more detail here.

Pass 7 Events

To replicate compare with Bill's Pass7 results, I 've gathered collected the root files that he was using. I believe that the they correspond to the following data sets in the data catalog:

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The first step is to compare against the event selection that Bill does. The results seem very comparable. One possible difference is that IM truncates it's floating point precision.

BILL PASS7	SIG	BKG
RibbonCut_p7	99.66% (99.66%)	88.38% (88.38%)
CornerCut_p7	98.25% (98.59%)	83.70% (94.70%)
BasicTileCut_p7	90.19% (91.79%)	16.04% (19.16%)
TotalTileEnergyCut_p7	85.42% (94.71%)	6.269% (39.09%)
VetoTileCut_p7	84.37% (98.77%)	4.384% (69.94%)
TileEdgeCut_p7	84.29% (99.91%)	4.312% (98.35%)

ALEX PASS7	SIG	BKG
RibbonCut_p7	99.6% (99.6%) – 121159	89.25% (89.25%) – 159950
CornerCut_p7	98.29% (98.68%) – 119564	85.81% (96.15%) – 153790
BasicTileCut_p7	92.64% (94.25%) – 112691	13.95% (16.26%) – 25009
TotalTileEnergyCut_p7	86.39% (93.25%) – 105089	7.007% (50.21%) – 12558
VetoTileCut_p7	84.8% (98.16%) – 103159	4.951% (70.66%) – 8873
TileEdgeCut_p7	84.74% (99.93%) – 103084	4.884% (98.65%) – 8753

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Selection Efficiency as a Function of Energy

Name	Solo Individual Selection	Selection with Respect to Complement
RibbonVeto
CornerVeto	Image Removed Image Added
BasicTileVeto
TotalTileEnergyVeto
TkrVTileVeto
TileEdgeVeto

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PASS7 ACD2	SIG	BKG
RibbonCut2_p7	99.01% (99.01%) – 60482	89.9% (89.9%) – 50790
CornerCut2_p7	97.99% (98.96%) – 59856	86.69% (96.43%) – 48978
BasicTileCut2_p7	92.92% (94.83%) – 56760	22.73% (26.22%) – 12843
TotalTileEnergyCut2_p7	86.05% (92.61%) – 52566	13.42% (59.03%) – 7581
VetoTileCut2_p7	82.59% (95.98%) – 50452	9.709% (72.35%) – 5485
TileEdgeCut2_p7	82.54% (99.94%) – 50421	9.693% (99.84%) – 5476
Cal1ConeCut_p8	80.34% (97.33%) – 49073	4.461% (46.02%) – 2520

95.12%

PASS8	SIG	BKG
RibbonVeto_p8	99.5% (99.5%)	89.9% (89.9%)
CornerVeto_p8	VetoHit1Cut_p8	98.6% (99.1%)	86.9% (96.6%)
Tkr1SigmaVeto_p8	93.3% (94.7%)	22.5% (25.9%)
TkrSigmaHitVeto_p8	88.7% (95%)	16.6% (73.7%)
Tkr1ConeVeto_p8	83.7% (94.3%)	10.5% (63.6%)
Cal1ConeVeto_p8	81.7% (97.6%)	4.96% (47.1%)

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{htmlcomment:hidden} \|\| OLD PASS8 \|\| SIG \|\| BKG \|\| \| VetoHit1Cut_p8 \| 95.12% (95.12%)

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 -- 58105 | 26.2% (26.2%)

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 -- 14802 |
| TotalTileEnergyCut_p8

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 | 86.53% (90.96%)

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 -- 52855 | 12.83% (48.99%)

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 -- 7251 |
| VetoGap1Cut_p8

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 | 86.36% (99.8%)

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 -- 52750 | 12.65% (98.52%)

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 -- 7144 |
| VetoHitCut_p8

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 | 85.53% (99.05%)

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 -- 52247 | 12.06% (95.34%)

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 -- 6811 |
| Cal1ConeCut_p8

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 | 82.85% (96.86%)

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 -- 50609 | 5.245% (43.5%)

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 -- 2963 |
{htmlcomment}

The power of the Cal1ConeCut comes from the fact that the majority of the residual background is sneaking through the bottom of the TKR. The direction of these events is poorly reconstructed since, especially at high energy, the longest straightest track will often come from back-splash. On the other hand, the CAL axis will point along the direction of the incident particle causing the ACD to query the proper tiles.

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