1. 1 Methods of Experimental Particle Physics Alexei Safonov Lecture #22

2. Maximum Likelihood Likelihood for N measurements x i : It is essentially a joint p.d.f. seen as a function of the parameters  While xi could be any measurements, it’s easy to visualize it as a histogram of x with N bins; in each bin you calculate how probable it is to see x i for a particular  (or set of  ’s) In ML, the estimators are those values of  that maximize the likelihood function One could use MINUIT to find the position of minimum for -log(L) 2

3. Example: Maximum Likelihood

4. Likelihood Function Magnitude Let’s guesstimate (actually overestimate) L max for our example: 80 bins, average probability for outcomes in each bin ~50% L max =(1/2) 80 while data and fit would be clearly compatible Well, we didn’t ask the right question L max gives the probability of this EXACT outcome, which is of course very unlikely We wanted to know “can something like this happen”?

5. Hypothesis Testing We are trying to figure out if the minimum we got makes sense or not Is it artificial or do we have a reasonable degree of belief that these numbers at the minimum make sense? Ask a different (kind of opposite) question: If the function with the parameters you picked as best is true, is it probable to see the data you actually saw? Studying how the data should look seems easy: Take the function, and generate fake “pseudo-data” using a Monte Carlo generator following the function Hypothetically, you would want to say that if the real data and most of the “fake data” show a similar degree of similarity between the data and the function, you are presumably good

6. Hypothesis Testing

7. The p-value What we just calculated is called the p-value Probability for the data to look even less likely than what we observed in data assuming that the function is correct Calculate p-value: Pseudoexperiment: take your function ( with “best” parameters) and “simulate” “data” according to the function (allowing statistical fluctuations), every time calculate and record likelihood L Do 1,000,000 pseudoexperiments, check how often you get L lower than the L max you actually observed. Small p-value (say less than 1%) tells you that your data does not follow the function you are trying to describe it with When searching for smaller signals, can use p-value calculated using pseudoexperiments following the “background only” model: small p-values tell you that data does not like the “background only” model Caveat: as a single number, such p-value does not tell you if adding signal helps

8. Hypothesis Testing What we have described, even better works if you have a fixed “hypothesis” (no parameters) and want to check if the data is consistent with that hypothesis In the initial fit we have found “best” parameters and took it as L0; in pseudo-experiments we never had this additional flexibility to pick something that is “best” But for a fixed hypothesis there is never a problem Example: You stare at data and you have a well defined background prediction (say from MC simulation) You suspect there may be a bump in the data but you are not sure A good question to ask is “does the data look like the prediction?” – in other words what is the p-value? 8

9. Signal or No Signal? When you start looking for Higgs, you don’t know its mass or cross-section Comparing data and the background prediction is great, but it only tells you whether the data and background expectation look alike or not If something fishy is going on, you will see a low p-value, which tells you that data does not look like background prediction But it does not tell you if it looks like you may have found a Higgs (maybe it’s something else in there, which makes the data and predictions disagree) Need to answer a question “does it look more like X or more like Y”? If both X and Y are fixed (X is background only, Y is background plus signal with known mass and cross-section), one could probably calculate two p-values and then make a judgment The caveat is that in all real searches you almost never know the mass – what do you do then? 9

10. Hypothesis Testing – Unknown Mass Say I don’t know the mass, but I think I know the cross-section for each mass As if I believe SM predictions and look for SM Higgs How do you account for unknown mass? Proposal #1: Calculate p-value for Background and Background+Signal for every possible value of Higgs mass (say you scan over 100 values) One p-value for Background only that tells you that data does not look like background (say p=0.001%) and 100 p- values for each mass each saying you different things (most are small like p=0.001-1%, but the one at 140 is p=40%) One issue is having two p-values, but there is another one too 10

11. P-value for Comparing Hypotheses In the past you were using L to say is something is function-like or not Strictly speaking, you could have picked a different metrics, L is not the only possible choice When comparing hypotheses, a good choice is the ratio of joint p.d.f.’s It tells you at each point if something is more H1-like or H2-like When you do pseudo-experiments in calculating the p-value, you will still generate data according to background model (if you are determining if something is background-like), you will just use this statistic as a metrics in deciding if the pseudo-data is more “alike” than the true data or not Can successfully define p-value, p=1% will tell you that in only 1% of the cases the data, if it’s truly following H0, would look like yours If 1% was your threshold, you will reject the hypothesis H0 in favor of H1 Btw, what if both are wrong? 11

12. Unknown Mass Say we did define this relative p-value and calculated it for each mass Note: the plot below is something slightly different in that it allows for unknown cross-section of the signal There is a clear bump somewhere near 125 that has p-value of ~1% Does it mean that there is only 1% that this is background and 99% that this is Higgs? 12

13. A Caveat: Combinatorial Trial Factor Also called “Look Elsewhere Effect” or LEE: Local p-value tells us how significant the deviation is in this specific point This would be a correct estimate of signal significance if we knew Higgs mass ahead of time But we didn’t It is like looking at 1,000 data plots: Even if all of them truly came from their expected distributions, on average one of them must appear only 0.1% probable From the X-Files of HEP experimentalists: A bump that turned out to not be real

14. Combinatorial Trial Factors Our p-value ignores the fact that every time something jumps up it looks more like Higgs and less like background Need to account for that in your pseudoexperiments Hense the word “local” in the bottom plot When you do pseudo- experiments, you should also try all sorts of masses just like in data to see how bad the data can deviate from the prescribed expectation even if it is truly following the expectation More Monte Carlo pseudo-experiments calculating the p-value 14