I am an undergrad student and very much interested in joining a grad school. Thank you for the very clear explanation of sigma. I enjoyed reading it very much. I was wondering if you could also explain the meaning of chi-square, delta chi-square and likelihood in future blogs. These are some of the terms that have always confused me.

Thanks very much!

You got me lined up behind you!

Higgs does not exist as an independent particle. As I explained in many responses on this topic, Allow to label Higgs as ISM “Invisible State of Matter” because it exists as an ultra-rapid state-2-state transition of matter at speeds several folds the speed of light. ISM state lifespan is less than 1.0 X 10-33sec which is technologically infeasible to detect.

I confidently conjecture and predice that CERN’s scientists will one day agree on such findings from their CMS and ATLAS experiments, possibly also from othes.

Well! One way to know, is wait and see.

Hi, I like your positive argument. I am pleased and did not expect less. Will read more of your blogs. Thanx

However, not everyone has this knowledge, and if someone is genuinely interested in what we’re doing at CERN, but they get put off by the jargon that we use then there is a need to explain these terms. It’s always preferable to show someone how to make their own minds up about the jargon we use, rather than being spoon fed a result, which is often what I see in a lot of articles.

Not every post can be as exciting as we’d like, and some of the posts are necessary in order to help explain the more exciting posts. In fact, quite a large number of posts on here are laying the groundwork for something grander, and we refer people back to those posts as we need to. It’s a fine line to walk, and this feedback helps a lot. I hope you keep reading my posts, I’ll be back with more exciting posts!

Things start to get very complicated if there is an overall fluctuation (up or down) of the total number of events summed over the whole distribution. When this happens we need to make our best estimate of the shape of the distribution, and the significance of individual fluctuations. There can also be problems in a distribution that has a large gradient, where a fluctuation at one data point can cause a dramatic shift in the overall shape.

To take uncertainties into account where the data points are not statistically independent (eg if we have several entries per event) we can either choose to weight the contributions to each data point, or to include a covariance matrix that takes the correlations between the data points into account. A good example of a distribution where the data points are not statistically independent would be the transverse momentum of leptons. Suppose we have a Z boson decaying to two leptons. We can then find the relationship between the transverse momenta of the two leptons and we find that when we get one high transverse momentum lepton we also get one low transverse momentum lepton. If we don’t take this into account we can end up with all kinds of unwanted correlations. We usually separate out the “leading” from the “subleading” lepton for this reason.

I hope that answers your question!