Now in my project dir all things are together.

I prefer split notebooks, cpp project, and readme should be related with main notebook

Ask Michal Broz to run simulation for the:

Run anchored: Get Run number with maximum of 'good' events
Period: LHC15o
Process: rho(1720) → rho pi+ pi- → 4pi
Number of generated events: 100 000

GeneratorCode

AliGenerator* GeneratorCustom() 
{
  AliGenCocktail* genCocktail = GeneratorCocktail("StarlightEvtGen");
  
  AliGenStarLight* genStarLight = (AliGenStarLight*)GeneratorStarlight();
  genStarLight->SetParameter("PROD_PID     =   999    #Channel of interest (not relevant for photonuclear processes)");
  genStarLight->SetParameter("rho0PrimeMass     =   1.720");
  genStarLight->SetParameter("rho0PrimeWidth     =   0.249");
  genStarLight->SetParameter("W_MAX     =   5.0");
  genCocktail->AddGenerator(genStarLight, "Starlight", 1.);

  AliGenSLEvtGen *genEvtGen = new AliGenSLEvtGen();
  TString decayTable="RHOPRIME.RHOPIPI.DEC"; // default
  genEvtGen->SetUserDecayTable(gSystem->ExpandPathName(Form("$ALICE_ROOT/STARLIGHT/AliStarLight/DecayTables/%s",decayTable.Data())));
  genEvtGen->SetEvtGenPartNumber(30113);

  genEvtGen->SetPolarization(1);           // Set polarization: transversal(1),longitudinal(-1),unpolarized(0)

  genCocktail->AddGenerator(genEvtGen,"EvtGen",1.);
  return genCocktail;
}

Here is the question about decay channel. Now it specified as 999 that correspond to 4 pions final state, but in startlight one more exists - 913 that produces a final state consisting of two pions, also it adds the amplitudes (with interference) for photoproduction of the rho, and for photoproduction of direct pi^+pi^-., but again the final state is only two pions.

I've processed 125 runs, but only ~80 were recommended by DPG.

I can compare the quality of such runs and the rest via visible c-s.

For fitting in #44 I have standard $\chi^2$ test for deterring quality of the fit, but for area with small statistics (Puasson) it's wrong.
E.g. I've calculated error as $\sqrt(N)$ for each bin, but in case of small count of events in bin it provide us the situation when 2 events in bin and 1.4 sigma.

Here is the fragment from lection of Cambridge prof. Mark Thomson about the question:

Now In case of large set of data e.g. ccup8 or ccup31 scripts works slowly and take all memory.

Keeping a code in jupyter notebook cells make notebook oversized.
I think the good solution is keeping scripts in the 'scripts' folder and use their as modules.

Concerning of fitting by two B-Ws in #44

How to calculate interference term of two resonances?

Here is the some explanation about interference of BW with C

Now, when I want to plot mass with total charge zero and non-zero, I can't do it because I don't have a functions or objects that could change the state. So I should repeat a code. This is bad solution.

I have to parametrize calculations and states.

I can check the quality of trigger via handle checks of fired fastors.

How to do it I can see here, here and here

What run has more good events and why?

Figure out how to:

• Turn on line numbers to cells by default
• Turn off suppress error-output

Now me and Valeri have discrepancies in the data.

He has less number of processed runs, but more statistics than me.

What is the reason of such discrepancies?

He'll provide to me file with event info and we can find out the difference...

Solving #20 I've seen that I can't get data from TObjArray of AliTriggerClass objects that contains in the trending_merged_PbPb.root file with usage of uproot4.

It really impossible or any way to do it exist?

In my analysis two the same criteria, but exposed on the different manner gives difference result:

selectOnlyStandard    = data['T_TPCRefit']  * (data['T_TPCNCls'] > 50) * \
            (data['T_ITSNCls'] > 3) * \
            (~newT_ITSsa) * (np.abs(data['T_NumberOfSigmaTPCPion']) < 3)
GoodEventsStandard = np.argwhere(selectOnlyStandard.sum()==4).flatten() # get events with 4 good tracks
GoodEventsStandard = GoodEventsStandard[np.argwhere(data['T_Q'][selectOnlyStandard][GoodEventsStandard].sum()==0).flatten()].flatten()  

pxstd = data['T_Px'][selectOnlyStandard][GoodEventsStandard]
pystd = data['T_Py'][selectOnlyStandard][GoodEventsStandard]
    
ptstd = np.sqrt(pxstd.sum()**2  + pystd.sum()**2)

fig = plt.figure(figsize=(15, 7))
ax = fig.add_axes([0,0,1,1])
fig.suptitle(f'4pr pt standard criteria', fontsize=32)
plt.style.use(hep.style.ROOT)
counts, bins = np.histogram(ptstd, bins=100, range=(0,2))
_ = ax.hist(ptstd, bins=bins, color='black', histtype='step', label=f'Q = 0;Entries {np.sum(counts)}')
plt.xlabel('Pt, GeV')
plt.ylabel('# events')
ax.legend()

GoodEvents = GetGoodEvents(WithGoodNTpcTracks=4)
counts, bins = np.histogram(GetPt(Draw=False), bins=100, range=(0,2))
_ = ax.hist(GetPt(Draw=False), bins=bins, color='red', histtype='step', label=f'4 TPC tracks;Entries {np.sum(counts)}', linewidth=2)
plt.xlabel('Pt, GeV')
plt.ylabel('# events')
ax.legend()

gives:

First criteria direct combination of second criteria.

Obviously for direct criteria condition to total charge doesn't work:

We can see the sum of zero charge events and non zero:

Picture for the second case, but the meaning is understood

In the slides of the one presentation I've seen:

We have two types of UPC events Triggered by MUON Triggered in the Central Barrel In both cases the events are almost empty The average size of AOD UPC events in the central barrel for 2015 PbPb is around 0.5 kB We have some 30 M triggered events, which means some 15 GB of data. The purity of this sample is small, meaning that a simple pre-selection may reduce this number by a large factor (between 2 and 3).

In the past we have used a LEGO train to access the events and produced trees to perform local analyses. The code in the LEGO train is quite stable Latchezar commented that this approach may not be optimal for such small data set distributed over all the PbPb data set and recommended that we had a look at nanoAODs.

The code to select only those branches we use and create with them ‘a nanoAOD’ is ready. As it is so general, we do not expect the code to be changed frequently. Latchezar mentioned that there exist AOD trains in the central framework.

Could it be a good option to use them to produce UPC AODs?

In Summary, what is the best way to run over the UPC triggers in the PbPb sample so that we do not have to run over all the PbPb events to find our few gigas of data?

Plot histogram of zdc energy distribution.

I've to see smth like abs(sinc)....

Also demonstrate that basic part (let say first half of the plot) contains events that form Mass spectra meanwhile second half mass spectra contains background events.

Till this moment I've used uproot4 for reading root files and output result was pandas dataframe.
Now my file has grown from 500mb to 1.5gb, so current version of script doesn't work because it takes forever and takes all memory.

I've tested parsing to numpy arrays and it works fine.

Now I have to move the main script form pandas usage to numpy.

Possible the situation when I have 6 tracks, but only 4 from them satisfy some criteria (TPC + total charge).

What influence of such events?

I have many common scenarios for data visualizing.

E.g.

• vertical comparison or horizontal comparison on the different plots
• adding data to the same plots with labels
• histogramming with errors bars
• custom styles for histograms

How can I simplify usage of such behavior?

First of all I know that in matplotlib I can specify default parameters for some functions. I can use this e.g. for changing default type of histograms to 'step', change defalut colors, applying root style as default.

Then I can prepare functions that can be wrap described common scenarios with additional parameters like arrays of colors and labels, and extend standard arguments of matplotlib via args and kwargs.

I've seen that expanding condition from >3 to >=3 for ITSNCls parameter gives me about 600 events to statistics.

What are these events, background or signal?

The same track can be triggered with different names (CCUP2 and CCUP9 and so on).

I have to estimate this influence.

I can get the ration between the number of good events for each run and luminosity of each run.

Luminosity for last 115 runs:
Alice/analysis/dev/pvn/lumi115runs.hist.root

The ratio curve should be constant because of the number of events is equal cross section multiply by luminosity.

I've got mass from 4 tracks, but what if I will calculate it from pion subsystems?

I faced a problem that after splitting event to triggered and untriggered I have a difference between origin total event count and
triggered event plus untriggered:

Definitely this is wrong

In the solution process #22 I realized that a problem of structure is not only in oversized. Also reinitializing of variables in case of restarting the kernel could be a headache. Moreover jumping from cell to cell without tags is really slowly.

I've a plan to create package for my analysis based on python modules and in notebooks just import modules and call functions.

The Dalitz plot should help to see kinematics of the resonance

Can I represent tree arrays via matrix?

shape(entries, ntracks)?

Can I try to get decay by the channel
$\rho' \rightarrow \rho_0 \rho_0$

Some time ago I already have got such distribution here

But it has incorrect numbers.

Repeat this results. This will be topic of the next presentation.

Right now I have legacy README.md.

What if I can use github action to convert current notebook to md and put it into README.md?

Repeat pt distribution with demonstration of amplifying signal for mass:

Now I have one notebook and this lead to some problem after changing criteria for debugging and so on.

I have to split it

ROOT has excellent histogram tools with different visualizations modes (error bars, markers), operations for histogram such as ratio or difference.

What I need is

• Stats table, such as entries, mean, std, overflow, integral
• Operations with histograms such as division and difference
• Histogram with Error bars
• Visualization with comparison purposes it could be n hist at one plot, subplots, so on

I've seen many of histogramming tools including ROOT, e.g.
scikit-hep/hist
scikit-hep/aghast
scikit-hep/boost-histogram
numpy.histogram

Perhaps many of these tick could be easy solved via let say standard packages, e.g. stats could be provided by pd.describe (except overflow and integral)

Now I use such constructions for drawing:

bw, = ax.plot(np.linspace(x.min(), x.max(),1000), fit.bw(x=np.linspace(x.min(), x.max(),1000), M = result.best_values['bw_M'], G=result.best_values['bw_G'], amp=result.best_values['bw_amp'] ), 'g--',label=r'Breit-Wigner')
bkg, = plt.plot(np.linspace(x.min(), x.max(),1000), fit.polyn(np.linspace(x.min(), x.max(),1000), result.best_values['bckg_c0'], result.best_values['bckg_c1'], result.best_values['bckg_c2'], result.best_values['bckg_c3'], result.best_values['bckg_c4'],0,0,0, result.best_values['bckg_amp']), 'b--', label='Bckgr fit')
# plt.plot(bcg_x, bcg_y, 'r+', label='bkg data')
result.plot_fit(datafmt='ko',fitfmt='r-',numpoints=100,data_kws={'xerr':0.022})
ax.set_title('')
_ = ax.set_ylabel(r'Counts per 20 Mev/$c^2$')
_ = ax.set_xlabel(r'M$(\pi^+\pi^-\pi^+\pi^-)$ (GeV/$c^2$)')
_ = plt.legend([dat,ft, bw, bkg],['Data','Fit','Breit-Wigner','Bkg'])

Instead of this, I can combine data to DataFrames and use pandas plot for drawing purposes

Let's see how does further parameters for tracks are distributed for TPC+ITS and ITS only

• full momentum
• pt
• pseudorapidity

Mass based on ITS only tracks should be biased to the soft side.

In continuation of #21 let's compare Mass tracks and event

Now I have number of events with $\rho'$ for each runs and I can get luminosity of these runs.

This means that I can get cross section of the $\rho'$

In fact this is not true, but why?

$$\sigma=\frac{N}{L}$$

Obviously this should be flat. But for such definition I can't confirm that:

Actually in case we will take std as $\frac{1}{\sqrt{nev}}$ such calculation looks strange because of scale doesn't match.

Perhaps, we have to normalize the data. In this case we can see flat curve(last picture):

During one of the last meetings we decided to update twiki page of our group.

I have to add names (me and pvn) and also information about ongoing analysis

The idea is that we can check $\eta$ distribution for the pairs and check, perhaps lightest mass condition for selecting pair is to hard and we can increase accuracy if make condition for $\eta$

Now here
I just play with data and get correct pt, but now I have to get pt for different data for comparison and so on

Here is ρ' resonance.

According with PDG description
The best fit could be obtained with the hypothesis of ρ-like resonances at 1420 and 1770 MeV, with widths of about 250 MeV.

I can create two BW model and fit resonance by them.

Estimate triggers influence: selected triggers are CCUP9, CCUP2, CCUP4, C1ZED, CCUP11. what difference between them according for my events?

Until now I was studying only one decay mode:

$\rho' \rightarrow 4 \pi$

In PDG I've seen also other interesting modes for $\rho'$:

• 1. $\rho' \rightarrow \eta_0 \rho_0$ | ?
  • 1.1. $\rho_0 \rightarrow 4 \pi$ | $2*10^{-5}%$
  • 1.2. $\rho_0 \rightarrow \pi^+ \pi^-$ | $10^{-2}%$
  • 1.3. $\eta_0' \rightarrow \pi^+ \pi^- \gamma$ | $4%$
  • 1.4. $\eta_0' \rightarrow \pi^+ \pi^- \pi^0$ | $23%$
• 2. $\rho' \rightarrow 4 \pi$ | ?

What about $\rho' \rightarrow \rho_0 \rho_0$ is it possible?

I know that to get luminosity data per run I have to work with trending_merged_PbPb.root file. I can find it e.g. here for each year.

Here I have description of SPD
but I have to clarify connection between SPD and AliRoot.

What is ITSModule? (ITS Module / 1000000) is detector number? What does it mean?
What is fF0Modules in SPD?

During 4$\pi$ photoproduction in reaction of the $\rho'$ decay:

$$\rho'\rightarrow \pi^+\pi^-\pi^+\pi^-$$

I have a intermediate two-body decay state when $\rho_0\rightarrow \pi^+\pi^-$.

This allows us to check $\rho_0$ via determining of the decay angle for rest frame of $\rho_0$.

I see in my events plenty of duplicated tracks:

There are only in case of ITS selection

((data['T_HasPointOnITSLayer0']) + (data['T_HasPointOnITSLayer1'])) * \
data['T_ITSRefit']  * (np.abs(data['T_NumberOfSigmaITSPion']) < 3)

In case of adding TPC criteria looks like (cause of I can't get it) that such tracks will be thrown...

Plot the Mass for 4 prongs events with zero and non-zero total charge for different pt intervals:

• (0,150]
• (150,800]
• (800,2000)

There is a point that minuit based framework roofit is better then lmfit based on scipy.
Let's compare the quality of the fits.

In the selection script PVN limits the number of THE GOOD(passed by some criteria) TRACKStracks and fill predefined arrays.

I have chosen another strategy and fill vectors with dynamic size.

That's interesting to compare data losses in PVN case.