1. Steve Worm, Rutgers B/Exotics Joint Session on Search Strategies 10/31/02 B/Exotics Search Strategies Searches with small numbers of events – what’s the problem? –In a search, the small number of events can put you at a big risk for bias –Looking at data samples while devising analysis cuts is obviously bad, the real problems are often more subtle. –“I looked at the data, but why can’t I just make cut X if I can justify it?” What can we do to avoid bias –Blinding: avoiding bias by not looking in the signal region until the end –Some Recent (good!) Exotics Examples Squarks/Gluinos  jets + MET Stop  dileptons Pros and Cons Discussion Steve Worm Rutgers University

2. Steve Worm, Rutgers B/Exotics Joint Session on Search Strategies 10/31/02 (Showed a slide from Brookhaven E871, illustrating the ‘classic’ blind-box analysis. The point was to contrast with what we are talking about now; our situation is not the same and we don’t need to be so extreme.)

3. Steve Worm, Rutgers B/Exotics Joint Session on Search Strategies 10/31/02 Squarks and Gluinos  jets + MET Straightforward signature, but lots to clean up Search strategy well thought out –Samples chosen so that signal region was bounded by control samples from both sides –Enough cross-checks to have confidence in signal region Remained completely blind throughout analysis (!) (Dr. Maria Spiropulu)

4. Steve Worm, Rutgers B/Exotics Joint Session on Search Strategies 10/31/02 Squarks and Gluinos  jets + MET (open signal box) “Classic” blind analysis (more or less) Result: great limit on squark/gluino production

5. Steve Worm, Rutgers B/Exotics Joint Session on Search Strategies 10/31/02 Stop  dileptons + jets + MET Another Run I example: “partial” blindness Backgrounds (many, messy, complicated): –Drell-Yan –tt –bb, cc –WW, WZ, ZZ –fakes Procedure used: 1.Apply basic cleanup cuts on data 2.Develop analysis cuts on MC 3.(Estimate fakes) 4.Apply initial analysis cuts on small subsample (Run Ia – 20%) 5.(Really understand fakes) 6.Open box for Run II data Result: great limit for stop (Dr. Arnold Pompos)

6. Steve Worm, Rutgers B/Exotics Joint Session on Search Strategies 10/31/02 Pros and Cons of being Blind… Goal: The elimination of subjectivity. Common Problems/Complaints: –You need to know your backgrounds too well before opening the box “in a hadronic environment you can never know your background that well…” “I’m using the data to estimate my background…” –“Blind analyses tend to take longer…” –What if I screw up, and forget about background X? Do I have to start over? Will you kick me off of CDF? Can I go back and fix problems I see? –“I have to look in my signal region, I don’t have a good orthogonal sample” –If I have to think of everything that might go wrong before looking at the data, I’ll tend to cut too hard. Benefits? –What do I get for this effort, besides bragging rights? –Credibility (…or embarrassment?) –A better result –A believable result –Drama! Blindfold (added by convener) Sword (added by advisor)

7. Steve Worm, Rutgers B/Exotics Joint Session on Search Strategies 10/31/02 Searches…with few events comes big risk We (typically) don’t police you during the analysis, you have to help… –Don’t tune analysis cuts on your data! Its obvious, but worth restating –*Think* about your analysis cuts and ways to test on orthogonal samples w/o data –Make a plan. If you have questions, ask. The obvious situations to avoid: –“If I cut at blabla=4.1 instead of 4.0 I can get a better limit…” –Cutting on quantities after filtering/inspecting the data –Cuts or fits that are sensitive to binning effects Possible (bad) solutions to search angst: –Student: “If I cut hard I’m left with no events and can graduate sooner!” –Junior Prof: “If we just had an interesting result, I could get tenure.” –PostDoc: “I think I’ll go do an analysis with more events.” Possible Good solutions: –Blinding –Minimizing potential for bias – at least “Partial” blindness If this sounds like a lecture, it is.