Energy loss improvements and tracking Niels van Eldik, Peter Kluit, Alan Poppleton, Andi Salzburger, Sharka Todorova Common Tracking Meeting 4 July

Back tracking and the impact of the Landau 2 If one has a Landau distribution for the Eloss one can calculate the distribution at the IP – so after backtracking- by the following convolution: N(p’) = Integral Landau(E,E MOP,σ L )*Gaus(p’-E, σ p ) dE Note that also the impact of the track variation in the Barrel calorimeter can be written in this form where σ p = E MOP dL/L. The point is that the MOP value of the distribution N(p’) is not E MOP anymore. If σ L is very small the shift will be very small. However for larger values of σ L the shift will depend on σ p. This means that in the backtracking the new E MOP value after the convolution should be used. The proposal is to calculate the correction analytically using per track the σ p.

Numerical issues with the Landau distribution and convolutions 3 The second issue is that one has to use numerical integration and FFT to get the Landau convoluted with a Gaussian distribution. It is far from easy to reach the required numerical precision. To circumvent these problems I decided to move to a set of analytical functions that allow convolution and fast robust fitting. The Landau will be approximated by: Landau(x) = x/(a 2 +x 2 ) 2 for x>0 x = E + E 0 a = σ L E MOP = E 0 + a/√3 Gaussian(σ reso ) ≈ Breit-Wigner 2 ~ 1/(b 2 +x 2 ) 2 b ≈ σ reso These functions can be convoluted analytically and fitted to the data.

Approximation to the Landau distribution 4 In red the chosen approximation to the Landau function Landau(x) = x/(a 2 +x 2 ) 2 for x>0 compared to the root implementation

Data fitted to the model 5 Here a fit to some extreme cases. First a Barrel fit where the distribution is mostly gaussian. p0 = normalization p1 = mean p2 = a ~ Landau width p3 = b = BW2 width So b ~ sigma resolution

Data fitted to the model 6 The numerical analytical functions are in place. They are able to describe the data. For quite extreme cases from the Gaussian to the Landau and the Resolution dominated. More examples and fits are in the backup Next slides: Work out the formulae to give corrections to E MOP after convolution with a Gaussian. Give the mathematics for Landau distributions and 68% (95%) CL intervals. Finally some implications for MS and CB track fitting are discussed.

Modeling the shift in E MOP 7 Problem: suppose we have a MOP value and we have a Landau error σ L and a gaussian sigma σ reso what is the shift Δ E MOP ? Here we use the parameters a = σ L b = σ reso Δ E MOP = function (a,b) It has a special form Δ E MOP = a f(b/a) f(x=b/a) is shown on the next page It has an asymptotic behaviour where f->1 for x->inf The function can be approximated by f(x) = (p0*x*x+p1*x*x*x)/(1+p2*x+p3*x*x+p4*x*x*x) p e+00 p e-01 p e+00 p e+00 p e-01

Modeling the shift in E MOP 8

Typical example in the Calorimeter: the shift in E MOP 9 Barrel a = 200 MeV EC a = 450 MeV Assume a dp/p of 2% This gives the curves on the left*. So at high momenta one gets the full shift of a. At low momenta the shift is smaller. That is why the Jpsi pT > 5 GeV is very different from the Z pT > 20 GeV where the MOP shift starts to rise. * For simplicity it is assumed that a= σ L does not change with p

Typical example in the Muon Spectrometer: the shift in E MOP 10 Barrel a = 20 MeV EC a = 45 MeV Assume a dp/p of 2% This gives the curves on the left. So at high momenta one gets the full shift of a. At 3 GeV (EC 6 GeV) the shift is already 8 (20) MeV. The dependence is rather small but should be accounted for…

Convoluting Landau distributions and the track fit 11 Problem: suppose we have several E MOP values and errors. As discussed in the Muon-PUB note, the formula to combine these values is: This has very special features: 1.The MOP value shifts depending on the MOP errors (7) 2.The errors do not add up quadratically but linearly (8) This has implications for track fitting. It means that Eloss has to be combined using these formula. For the MS track fit I would propose to collect the total material between the measurements and use the formulae above to aggregate the Eloss. * I prefer the form: (σ 1 +σ 2 ) ln (σ 1 +σ 2 ) - σ 1 ln σ 1 - σ 2 ln σ 2 *

Landau distributions and CL intervals 12 We want to derive analytic expression for the different CL intervals. For a pure Landau distribution we define σ + and σ - as containing 68% of the events on the upper lower side of the MOP Eloss value. 68% CL σ - = 1.02 σ L σ + = 4.65 σ L 95% CL 2σ - = 1.82 σ L 2σ + = σ L After convolution with a Gaussian with σ Reso the formulae become (checked using the Landau-BW approximations): σ - = √ (1.02 σ L ) 2 + (σ Reso ) 2 σ + = √ (1.82 σ L ) 2 + (σ Reso ) 2 2σ - = √ (4.65 σ L ) 2 + (2 σ Reso ) 2 2σ + = √ (21.86 σ L ) 2 + (2 σ Reso ) 2 These expressions we need for Calorimeter object in the CB trackfit. Big!

Treatment of the Calorimeter Eloss 13 Proposal: from the Calorimeter Tracking Geometry we obtain: 1)The E MOP (from ID to MS) 2)The σ L on the Eloss 3)The dL/L: uncertainty on track length in Calorimeter NB in the calculation of 1) and 2) the Landau formalism of slide 11(adding errors linearly) should be used. From this we calculate: E MOP corrected using the formalism on slide 7 (dL/L gives a smearing and therefore shifts the mean). We also need σ + and σ - from slide 12. This allows to make the comparison of the measured Eloss in the Calorimeter with E MOP corrected + 2 σ.

Treatment of the Calorimeter Eloss 14 Next step is to determine the Eloss and errors that should be used in case no Calorimeter measurement is used. Now we have to calculate: E MOP corrected using dp/p inside MS and dL/L using the formalism on slide 7. We also need σ + and σ - from slide 12. The value of the E MOP corrected will depend on whether we looked at the measured Eloss or not. NB if we did look the Landau tail is reduced. Currently I work on the mathematics for this case. This Eloss object can be used for two purposes: Back extrapolation of the MS track to the IP -> SA parameters Perform the Combined fit -> CB parameters

Treatment of the MuonSpectrometer Eloss 15 To do a perfect job inside the Muon Spectrometer One should firstly aggregate according to the formalism for Landau distributions Secondly one should reiterate using the uncertainty on the momentum to update E MOP corrected and then refit. This is depicted on slide 10. Question: what is done in the Step Propagator? Is the MOP value returned? How is the aggregation of the Eloss done (is the Landau formalism used)? One could use the Landau formalism in the track fit. However one needs a re-iteration propagating the error on the momentum into the E MOP value. For the calorimeter Eloss object one could make the object taking into account the momentum error…

Backup slides 16

Introduction 17 Currently the Energy loss description for muons passing the calorimeter has a precision of about Et 100 MeV. For mass measurements using the combined and or muon (standalone) measurements it is important to improve significantly the precision of the E loss description. The target that we want to put here is a description that is accurate at e.g. the 10 MeV level. This we want to achieve on a track-track basis. This project will need improvements in several areas: 1)the Tracking Calorimeter description: E loss MOP and sigma modeling 2) (analytic) computing and reconstruction

Introduction: Physics 18 Concerning the physics aspects: Firstly one can get rid of part of the Landau tail. In particular for isolated muons. This is done by the Muid Combined algorithm. Secondly, we cannot fully get rid of the Landau tails; but we can give the right (well-centered) MOP value. This means that the MOP of di-muon resonances like Jpsi and Z will be (about) well centered. Thirdly, we cannot get the “mean” of the momentum or mass distribution well-centered. This has to do with the fact that a Landau distribution does NOT have a mean. So depending on the cuts, the “mean” will change. The MOP value is however well defined.

Calorimeter Tracking geometry 19 The current situation is the following. There is a layer based description of the calorimeter to describe the E loss and its error. This description is not used in Muid because it is not precise enough. The Muid description is basically an eta-phi-p map that gives MOP Eloss and its error. There are two possible roads that improve the situation: - Improve the E loss description of the Calorimeter - Improve the precision of the Muid Eloss map It is hard to conceive that a map with a precision of 10 MeV can be made. We think however it is possible to achieve a very precise description of the E loss in the calorimeter. This would require a volume based TG description of the calorimeter.

Calorimeter Tracking geometry 20 In the Muon system a volume based TG description gives a very accurate description of the material traversed. For the Muon system this is the best we can achieve. A similar description of the calorimeter would give on a track-by- track basis the E loss. And this is - in my opinion - the ‘ultimate’ description one wants to have. Both Sharka and Andi are willing to work on this topic. Requirements Eloss: for a given track one wants to know the MOP Eloss value and its error. Secondly one needs the track length in the Calorimeter and its error. Identification: crossed cells and the MOP E loss per cell and its error (for use in Muon Calorimeter Identification algorithms)

Energy loss modeling 21 Energy loss modeling is described by a Landau distribution that is characterized by a MOP value and a width or sigma value. The MOP energy and the sigma depend on the momentum of the muon and consists of a linear and a logarithmic term. The tracking geometry takes uses dependencies. To obtain a precise description Geant4 simulations and TG should agree on these underlying parametrisations and the material characteristics (such as X0). A study of the Eloss shape in the MC in different regions of the detector was performed. It was found out that the Eloss shape in the Barrel does NOT have a Landau shape. In the Endcap it can be described by a Landau distribution.

Energy loss modeling 22 Fit: frac*Landau + (1-frac) Gaussian Barrel has 83% Gaussian! Endcap only 4% Only 17% Landau as expected 96% Landau Puzzle

Energy loss EC plots for one eta bin and a 2 GeV p range 23 Here eta = ; p 12-14; and GeV Enough stats in Endcap from 14 GeV onwards: observe a large Landau tail (as expected)

Energy loss Barrel plots for one eta bin and a 2 GeV p range 24 Barrel eta same momentum range observe a larger Gaussian component

Energy loss Barrel plots for one eta bin and a 2 GeV p range 25 Barrel eta to lower momenta: one observes an increase in the Gaussian component

Barrel: Energy loss modeling 26 Barrel has more Gaussian shape. Why is this? First observed by Alan and Kostas. Their explanation: The distribution has two components: one of Landau component and another track length component. If the track length varies due to multiple scattering the mean of the Landau will be smeared with a Gaussian. Here a model that indeed does the job for 10% track length smearing

Energy loss modeling 27 Conclude: Energy loss modeling is described by a Landau distribution that is characterized by a MOP value and a width or sigma value. Need also to know the track length variations dL/L in the Calorimeter to account for the effect observed in the Barrel. That is why on slide this is added as a requirement to the TG description. Reconstruction and computing issues: - Back tracking and the impact of the Landau tail. - Numerical precision and the Landau distribution, convolutions. - Approximation to the Landau distribution.

Numerical issues with the Landau distribution 28 The first approach was to use the formula on slide 10 and compute it. This turns out to be not that trivial: the Landau distribution itself using e.g. the root implementation has a problem. The MOP value you put in is not the maximum of the distribution… It is shifted by about 40 MeV. MOP value of 3 GeV input The maximum of the curve is shifted by 40 MeV The point is that we need a precision of << 10 MeV on the MOP value

Back tracking and the impact of the Landau 29 Suppose we have a track measured in the MS its q/p distribution will be rather Gaussian. The momentum resolution dp/p in the Muon System is given below:

Can the data be fitted with the current model? 30 Here a fit to some extreme cases. An Endcap fit where the distribution is mostly Landau with small 0.02 dp/p smearing. p0 = normalization p1 = mean p2 = a ~ Landau width p3 = b = BW2 width So b ~ sigma resolution

Can the data be fitted with the current model? 31 Here a fit to some extreme cases. An Endcap fit where the distribution is mostly Landau with large 0.10 dp/p smearing. p0 = normalization p1 = mean p2 = a ~ Landau width p3 = b = BW2 width So b ~ sigma resolution Note the large b value As expected the MOP value increases wrt the small smearing case