A Historical Approach

Learning Objectives

  • Build a decay chain using the most basic tools

  • Understand the limitations and problems of these tools

Lesson caveat

In this lesson we will explain how to work with the most basic building blocks of the Selection Framework. This is not the most optimal way to use it, but it is included here because their use is very generalized, for example in the Stripping, and understanding them is very useful for understanding most of our current code. At the end of this lesson its shortcomings will be highlighted and a better way to approach the problem will be presented in the following lesson.

Now we’ll learn to apply the concepts of the Selection Framework by running through a full example: using the DST files from the Downloading a file from the Grid lesson, we will build our own \(` D^\ast\rightarrow D^0(\rightarrow K^{-} K^{+}) \pi `\) decay chain from scratch. Get your LoKi skills ready and let’s start.

Getting started

There’s no need to download the files from the Grid for this lesson. We can simply open them using the root protocol thanks to the fact that they are replicated at CERN:

from GaudiConf import IOHelper

The starting code for this exercise can be found here.

Our input pions and kaons can be imported from the StandardParticles package:

from StandardParticles import StdAllNoPIDsPions as Pions
from StandardParticles import StdAllLooseKaons as Kaons

This is an ideal way to get pre-made particles with the standard LHCb configuration.

Where do StandardParticles come from?

One important type of Selection is the AutomaticData, which builds objects from their TES location using a centrally predefined algorithm. The StandardParticles/CommonParticles packages (one imports the other), which you can find here, allow to access premade particles with reasonable reconstruction/selections for us to use with AutomaticData.

For example, in our specific case, we use the AutomaticData class with the Phys/StdAllNoPIDsPions/Particles and Phys/StdAllLooseKaons/Particles locations to access the output of the StdAllNoPIDsPions and StdAllLooseKaons algorithms, respectively (see here and here). Therefore, the following code would be equivalent to what we have used:

from PhysConf.Selections import AutomaticData
Pions = AutomaticData('Phys/StdAllNoPIDsPions/Particles')
Kaons = AutomaticData('Phys/StdAllLooseKaons/Particles')

Once we have the input kaons, we can combine them to build a \(` D^0 `\) by means of the CombineParticles algorithm. This algorithm performs the combinatorics for us according to a given decay descriptor and puts the resulting particle in the TES, allowing also to apply some cuts on them:

  • DaughtersCuts is a dictionary that maps each child particle type to a LoKi particle functor that determines if that particular particle satisfies our selection criteria. Optionally, one can specify also a Preambulo property that allows us to make imports, preprocess functors, etc (more on this in the LoKi functors lesson). For example:

    d0_decay_products = {
      'K-': '(PT > 750*MeV) & (P > 4000*MeV) & (MIPCHI2DV(PRIMARY) > 4)',
      'K+': '(PT > 750*MeV) & (P > 4000*MeV) & (MIPCHI2DV(PRIMARY) > 4)'
  • CombinationCut is a particle array LoKi functor (note the A prefix, see more here) that is given the array of particles in a single combination (the children) as input (in our case two kaons). This cut is applied before the vertex fit so it is typically used to save CPU time by performing some sanity cuts such as AMAXDOCA or ADAMASS before the CPU-consuming fit:

    d0_comb = "(AMAXDOCA('') < 0.2*mm) & (ADAMASS('D0') < 100*MeV)"
  • MotherCut is a selection LoKi particle functor that acts on the particle produced by the vertex fit (the parent) from the input particles, which allows to apply cuts on those variables that require a vertex, for example:

    # We can split long selections across multiple lines
    d0_vertex = (
      '(VFASPF(VCHI2/VDOF)< 9)'
      '& (BPVDIRA > 0.9997)'
      "& (ADMASS('D0') < 70*MeV)"

With all the selections ready, we can build a combiner as

from Configurables import CombineParticles
d0 = CombineParticles(
    DecayDescriptor='[D0 -> K- K+]cc',

A small question

  • Do you understand this selection?

  • Do you know what each of these LoKi functors does?

Now we have to build a Selection out of it so we can later on put all pieces together:

from PhysConf.Selections import Selection
d0_sel = Selection(

We can already see that this two-step process (building the CombineParticles and the Selection) is a bit cumbersome. This can be simplified using a SimpleSelection object, which will be discussed in the next lesson.

For the time being, let’s finish building our candidates. Now we can use another CombineParticles to build the \(` D^\ast `\) with pions and the \(` D^0 `\)’s as inputs, and applying a filtering only on the soft pion:

dstar_decay_products = {'pi+': '(TRCHI2DOF < 3) & (PT > 100*MeV)'}
dstar_comb = "(ADAMASS('D*(2010)+') < 400*MeV)"
dstar_vertex = (
    "(abs(M-MAXTREE('D0'==ABSID,M)-145.42) < 10*MeV)"
    '& (VFASPF(VCHI2/VDOF)< 9)'

dstar = CombineParticles(
    DecayDescriptor='[D*(2010)+ -> D0 pi+]cc',

dstar_sel = Selection(
    RequiredSelections=[d0_sel, Pions]

Building shared selections

In some cases we may want to build several decays in the same script with some common particles/selection; for example, in our case we could have been building D0->K pi in the same script, and then we would have wanted to select the soft pion in the same way when building the Dstar. In this situation, we can make use of the FilterDesktop algorithm, which takes a TES location and filters the particles inside according to a given LoKi functor in the Code property, which then can be given as input to a Selection:

from Configurables import FilterDesktop
soft_pion = FilterDesktop('Filter_SoftPi',
                          Code='(TRCHI2DOF < 3) & (PT > 100*MeV)')
soft_pion_sel = Selection('Sel_SoftPi',
dstar = CombineParticles(
    DecayDescriptor='[D*(2010)+ -> D0 pi+]cc',
    CombinationCut="(ADAMASS('D*(2010)+') < 400*MeV)",
        "(abs(M-MAXTREE('D0'==ABSID,M)-145.42) < 10*MeV)"
        '& (VFASPF(VCHI2/VDOF)< 9)'
dstar_sel = Selection(
    RequiredSelections=[d0_sel, soft_pion_sel]

This allows us to save time by performing the filtering of the soft pions only once, and to keep all the common cuts in a single place, avoiding duplication of code.

We can now build build a SelectionSequence to add to the DaVinci execution sequence

from PhysConf.Selections import SelectionSequence
dstar_seq = SelectionSequence('Dstar_Seq', TopSelection=dstar_sel)
from Configurables import DaVinci
DaVinci().UserAlgorithms += [dstar_seq.sequence()]

Work to do

  • Finish the script by adapting the basic DaVinci configuration from its corresponding lesson and check the output ntuple (run over 10000 events to make sure your tuple has enough entries). The solution can be found here).

  • Add a PrintSelection algorithm in your selections and run again. It gets a Selection as input and it can be used the same way, except it will print the decay tree everytime making use of the PrintDecayTree algorithm which was discussed in the Exploring a DST lesson.

  • Compare your selection with what is done in the actual Stripping, which can be found here. You can appreciate the power of the Selection Framework in the modularity of that Stripping.

By looking at the final script, there is one striking thing: there is a lot of repetition (CombineParticles-Selection sequence) which leads to complicated naming schemes, due to the fact that we want our Selection or CombineParticle objects to have a unique name. To help with these name clashes and to allow a much more streamlined Selection building, the PhysConf.Selections module offers a large set of more optimized classes, which we’ll discuss in the next lesson.