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configurationdatabase
In this tutorial you will learn the meaning of the most important parameters of the MLHEP package.
LcpK0spp:
mass: 2.2864
sel_reco_unp: "pt_cand>1"
sel_reco_singletrac_unp : null
sel_gen_unp: "pt_cand>1 and abs(z_vtx_gen)<10"
sel_cen_unp: null
#sel_good_evt_unp: "is_ev_rej == 0"
sel_good_evt_unp: null
sel_reco_skim: ["pt_prong0>0.25 and pt_prong1>0.30 and pt_prong2>0.30 and cos_p_K0s>0.999 and abs(nsigTPC_Pr_0)<3.0",
"pt_prong0>0.25 and pt_prong1>0.30 and pt_prong2>0.30 and cos_p_K0s>0.999 and abs(nsigTPC_Pr_0)<3.0",
"pt_prong0>0.25 and pt_prong1>0.30 and pt_prong2>0.30 and cos_p_K0s>0.999 and abs(nsigTPC_Pr_0)<3.0",
"pt_prong0>0.25 and pt_prong1>0.30 and pt_prong2>0.30 and cos_p_K0s>0.999 and abs(nsigTPC_Pr_0)<3.0",
"pt_prong0>0.25 and pt_prong1>0.30 and pt_prong2>0.30 and cos_p_K0s>0.999 and abs(nsigTPC_Pr_0)<3.0"]
sel_gen_skim: [null,null,null,null,null]
sel_skim_binmin: [1,2,4,8,12] #list of nbins
sel_skim_binmax: [2,4,8,12,24] #list of nbins
var_binning: pt_cand
dofullevtmerge: false
The first part of the database includes the parameters needed to perform the conversion and skimming step. In particular:
-
sel_reco_unp
: is the selection applied at the conversion stage on the reco candidates -
sel_reco_singletrac_unp
: option to apply single track selection at the unpacking level. In most of the cases this selection is already preapplied at the level of the TTree creation -
sel_gen_unp
: is the selection applied at the conversion stage on the gen candidates -
sel_cen_unp
: centrality selection applied at the conversion stage -
sel_reco_skim
: is the selection applied at the skimming stage on the reco candidates -
sel_gen_skim
: is the selection applied at the skimming stage on the gen candidates -
sel_skim_binmin
,sel_skim_binmax
: ranges used to bin the converted dataframes in skimmed dataframes. At the skimming level indeed the pandas dataframe is splitted into several subdataframes according to the value of a given variable. This is typically done in bins of pT if you are performing an analysis vs pt. -
var_binning
: here you define the variable name used for splitting the datasets (e.g. pt_cand or multiplicity)
bitmap_sel:
use: True
var_name: cand_type
var_isstd: isstd
var_ismcsignal: ismcsignal
var_ismcprompt: ismcprompt
var_ismcfd: ismcfd
var_ismcbkg: ismcbkg
isstd : [[0],[]]
ismcsignal: [[1],[5]]
ismcprompt: [[1,3],[5]]
ismcfd: [[1,4],[5]]
ismcbkg: [[2],[]]
Selections via bitmap cand_type
associated to each candidate when filling the trees on the Grid.
-
var_isstd: isstd
: candidate selected by standard analysis cuts (subsample of the candidates stored in the tree) -
var_ismcsignal: ismcsignal
: MC true signal candidates -
var_ismcprompt: ismcprompt
: MC true prompt signal candidates -
var_ismcfd: ismcfd
: MC true feed-down signal candidates -
var_ismcbkg: ismcbkg
: MC background candidates -
isstd : [[0],[]]
: candidate selected by standard analysis cuts (subsample of the candidates stored in the tree). Selection performed checking the single bits of "cand_type" -
ismcsignal: [[1],[5]]
: MC true signal candidates. Selection performed checking the single bits of "cand_type" -
ismcprompt: [[1,3],[5]]
: MC true prompt signal candidates. Selection performed checking the single bits of "cand_type" -
ismcfd: [[1,4],[5]]
: MC true feed-down signal candidates. Selection performed checking the single bits of "cand_type" -
ismcbkg: [[2],[]]
: MC background candidates. Selection performed checking the single bits of "cand_type"
variables:
var_all: [cos_t_star, dca_K0s, signd0, imp_par_K0s, d_len_K0s, armenteros_K0s, ctau_K0s,
cos_p_K0s, pt_prong0, pt_prong1, pt_prong2, imp_par_prong0, imp_par_prong1, imp_par_prong2,
inv_mass, pt_cand, phi_cand, eta_cand, inv_mass_K0s, pt_K0s, cand_type, y_cand,
run_number, ev_id, nsigTPC_Pr_0, nsigTOF_Pr_0,
spdhits_prong0, spdhits_prong1, spdhits_prong2,
pt_jet, eta_jet, phi_jet, delta_eta_jet, delta_phi_jet, delta_r_jet,
pt_gen_jet, eta_gen_jet, phi_gen_jet, delta_eta_gen_jet, delta_phi_gen_jet, delta_r_gen_jet, pt_gen_cand]
var_evt:
data: [centrality, z_vtx_reco, n_vtx_contributors, n_tracks, is_ev_rej, run_number,
ev_id, n_tracklets,V0Amult, trigger_hasbit_INT7, trigger_hasbit_HighMultSPD,
trigger_hasbit_HighMultV0, trigger_hasclass_INT7, trigger_hasclass_HighMultSPD,
trigger_hasclass_HighMultV0, n_tracklets_corr, v0m, v0m_eq, v0m_corr, v0m_eq_corr]
mc: [z_vtx_gen, centrality, z_vtx_reco, n_vtx_contributors, n_tracks, is_ev_rej, run_number,
ev_id, n_tracklets, V0Amult, trigger_hasbit_INT7, trigger_hasbit_HighMultSPD,
trigger_hasbit_HighMultV0, trigger_hasclass_INT7, trigger_hasclass_HighMultSPD,
trigger_hasclass_HighMultV0, n_tracklets_corr, v0m, v0m_eq, v0m_corr, v0m_eq_corr,
mult_gen, mult_gen_v0a, mult_gen_v0c]
var_gen: [y_cand, pt_cand, eta_cand, phi_cand, cand_type, pt_jet, eta_jet, phi_jet, delta_eta_jet, delta_phi_jet, delta_r_jet, run_number, ev_id]
var_evt_match: [run_number, ev_id]
var_training: [cos_t_star, signd0, dca_K0s, imp_par_K0s, d_len_K0s, armenteros_K0s, ctau_K0s, cos_p_K0s,
imp_par_prong0, imp_par_prong1, imp_par_prong2, inv_mass_K0s, nsigTOF_Pr_0, nsigTPC_Pr_0]
var_boundaries: [cos_t_star, pt_cand]
var_correlation:
- [cos_t_star]
- [pt_cand]
var_signal: signal
var_inv_mass: inv_mass
var_cuts:
- [pt_prong0, lt, null]
- [pt_prong1, lt, null]
- [pt_prong2, lt, null]
- [inv_mass_K0s, absst, 0.4977]
- [nsigTPC_Pr_0, absst, 0.]
- [nsigTOF_Pr_0, absst, 0.]
- [imp_par_prong0, absst, 0.]
- [cos_p_K0s, lt, null]
- [armenteros_K0s, st, null]
- [imp_par_K0s, absst, 0.]
- [dca_K0s, absst, 0.]
- [signd0, lt, null]
- [cos_t_star, st, null]
In this block of the frames you define:
-
var_all
: list of variables you want to extract from the ROOT TTree and include in the Pandas dataframe of the reco candidates -
var_gen
: list of variables you want to extract from the ROOT TTree and include in the Pandas dataframe of the gen candidates -
var_evt
: list of variables you want to extract from the ROOT TTree and include in the Pandas dataframe of the event -
var_evt_match
: variables used to match candidates to events from where they come from -
var_training
: list of training variables. ML optimization will consider this list of variables -
var_boundaries
: list of variables for decision boundary studies -
var_correlation
: list of variables for correlation studies -
var_signal
: signal -
var_inv_mass
: invariant mass -
var_cuts
: list of cut variables to perform standard rectangular cut analysis. Cut type and value are set in the arrays for each variable.
plot_options:
prob_cut_scan:
cos_t_star:
xlim:
- -1
- 1
pt_K0s:
xlim:
- 0
- 1
pt_prong0:
xlim:
- 0
- 1
pt_prong1:
xlim:
- 0
- 1
pt_prong2:
xlim:
- 0
- 1
nsigTOF_Pr_0:
xlim:
- -4
- 4
armenteros_K0s:
xlim:
- 0
- 2
signd0:
xlim:
- 0
- 0.3
nsigTPC_Pr_0:
xlim:
- -4
- 4
eff_cut_scan:
cos_t_star:
xlim:
- -1
- 1
pt_K0s:
xlim:
- 0
- 1
pt_prong0:
xlim:
- 0
- 1
pt_prong1:
xlim:
- 0
- 1
pt_prong2:
xlim:
- 0
- 1
inv_mass_K0s:
xlim:
- 0.
- 0.04
armenteros_K0s:
xlim:
- 0
- 2
signd0:
xlim:
- 0
- 0.3
nsigTOF_Pr_0:
xlim:
- 0
- 1000
nsigTPC_Pr_0:
xlim:
- 0
- 4
imp_par_prong0:
xlim:
- 0
- 0.3
imp_par_K0s:
xlim:
- 0
- 1.
dca_K0s:
xlim:
- 0
- 1.
Few plotting options (axes ranges) to display properly the distributions of various relevant variables during the ML probability/efficiency cut scan.
files_names:
namefile_unmerged_tree: AnalysisResults.root
namefile_reco: AnalysisResultsReco.pkl.lz4
namefile_evt: AnalysisResultsEvt.pkl.lz4
namefile_evtvalroot: AnalysisResultsROOTEvtVal.root
namefile_evtorig: AnalysisResultsEvtOrig.pkl.lz4
namefile_gen: AnalysisResultsGen.pkl.lz4
namefile_reco_applieddata: AnalysisResultsRecoAppliedData.pkl.lz4
namefile_reco_appliedmc: AnalysisResultsRecoAppliedMC.pkl.lz4
namefile_reco: AnalysisResultsReco.pkl.lz4
treeoriginreco: 'PWGHF_TreeCreator/tree_Lc2V0bachelor'
treeorigingen: 'PWGHF_TreeCreator/tree_Lc2V0bachelor_gen'
treeoriginevt: 'PWGHF_TreeCreator/tree_event_char'
namefile_reco_ml_applied: AnalysisResultsRecoML.pkl.lz4
treeoutput: "Lctree"
histofilename: "masshisto.root"
efffilename: "effhisto.root"
crossfilename: "cross_section_tot.root"
Set file names and directories where to store files.
multi:
data:
nperiods: 4
nprocessesparallel: 20
maxfiles : [-1,-1,-1,-1] #list of periods
chunksizeunp : [100,100,100,100] #list of periods
chunksizeskim: [100,100,100,100] #list of periods
fracmerge : [0.05,0.05,0.05,0.05] #list of periods
seedmerge: [12,12,12,12] #list of periods
period: [LHC16pp,LHC16pp,LHC17pp,LHC18pp] #list of periods
unmerged_tree_dir: [/data/TTree/D0DsLckINT7HighMultwithJets/vAN-20190810_ROOT6-1/pp_2016_data/136_20190811-0107/merged,
/data/TTree/D0DsLckINT7HighMultwithJets/vAN-20190810_ROOT6-1/pp_2016_data/137_20190811-0108/merged,
/data/TTree/D0DsLckINT7HighMultwithJets/vAN-20190810_ROOT6-1/pp_2017_data/138_20190811-0108/merged,
/data/TTree/D0DsLckINT7HighMultwithJets/vAN-20190810_ROOT6-1/pp_2018_data/139_20190811-0108/merged] #list of periods
pkl: [/data/Derived/LckINT7HighMultwithJets/vAN-20190810_ROOT6-1/pp_2016_data/136_20190811-0107/pkl,
/data/Derived/LckINT7HighMultwithJets/vAN-20190810_ROOT6-1/pp_2016_data/137_20190811-0108/pkl,
/data/Derived/LckINT7HighMultwithJets/vAN-20190810_ROOT6-1/pp_2017_data/138_20190811-0108/pkl,
/data/Derived/LckINT7HighMultwithJets/vAN-20190810_ROOT6-1/pp_2018_data/139_20190811-0108/pkl] #list of periods
pkl_skimmed: [/data/Derived/LckINT7HighMultwithJets/vAN-20190810_ROOT6-1/pp_2016_data/136_20190811-0107/pklsk,
/data/Derived/LckINT7HighMultwithJets/vAN-20190810_ROOT6-1/pp_2016_data/137_20190811-0108/pklsk,
/data/Derived/LckINT7HighMultwithJets/vAN-20190810_ROOT6-1/pp_2017_data/138_20190811-0108/pklsk,
/data/Derived/LckINT7HighMultwithJets/vAN-20190810_ROOT6-1/pp_2018_data/139_20190811-0108/pklsk] #list of periods
pkl_skimmed_merge_for_ml: [/data/Derived/LckINT7HighMultwithJets/vAN-20190810_ROOT6-1/pp_2016_data/136_20190811-0107/pklskml,
/data/Derived/LckINT7HighMultwithJets/vAN-20190810_ROOT6-1/pp_2016_data/137_20190811-0108/pklskml,
/data/Derived/LckINT7HighMultwithJets/vAN-20190810_ROOT6-1/pp_2017_data/138_20190811-0108/pklskml,
/data/Derived/LckINT7HighMultwithJets/vAN-20190810_ROOT6-1/pp_2018_data/139_20190811-0108/pklskml] #list of periods
pkl_skimmed_merge_for_ml_all: /data/Derived/LckINT7HighMultwithJets/vAN-20190810_ROOT6-1/pp_data_mltot
pkl_evtcounter_all: /data/Derived/LckINT7HighMultwithJets/vAN-20190810_ROOT6-1/pp_data_evttot
mc:
nperiods: 4
nprocessesparallel: 20
maxfiles : [-1,-1,-1,-1] #list of periods
chunksizeunp : [100,100,100,100] #list of periods
chunksizeskim: [1000,1000,1000,1000] #list of periods
fracmerge : [1.0,1.0,1.0,1.0] #list of periods
seedmerge: [12,12,12,12] #list of periods
period: [LHC16pp,LHC16pp,LHC17pp,LHC18pp] #list of periods
unmerged_tree_dir: [/data/TTree/D0DsLckINT7HighMultwithJets/vAN-20190810_ROOT6-1/pp_2016_mc_prodD2H/129_20190811-0106/merged,
/data/TTree/D0DsLckINT7HighMultwithJets/vAN-20190810_ROOT6-1/pp_2016_mc_prodLcpK0s/131_20190811-0106/merged,
/data/TTree/D0DsLckINT7HighMultwithJets/vAN-20190810_ROOT6-1/pp_2017_mc_prodD2H/134_20190811-0107/merged,
/data/TTree/D0DsLckINT7HighMultwithJets/vAN-20190810_ROOT6-1/pp_2018_mc_prodD2H/135_20190811-0107/merged] #list of periods
pkl: [/data/Derived/LckINT7HighMultwithJets/vAN-20190810_ROOT6-1/pp_2016_mc_prodD2H/129_20190811-0106/pkl,
/data/Derived/LckINT7HighMultwithJets/vAN-20190810_ROOT6-1/pp_2016_mc_prodLcpK0s/131_20190811-0106/pkl,
/data/Derived/LckINT7HighMultwithJets/vAN-20190810_ROOT6-1/pp_2017_mc_prodD2H/134_20190811-0107/pkl,
/data/Derived/LckINT7HighMultwithJets/vAN-20190810_ROOT6-1/pp_2018_mc_prodD2H/135_20190811-0107/pkl] #list of periods
pkl_skimmed: [/data/Derived/LckINT7HighMultwithJets/vAN-20190810_ROOT6-1/pp_2016_mc_prodD2H/129_20190811-0106/pklsk,
/data/Derived/LckINT7HighMultwithJets/vAN-20190810_ROOT6-1/pp_2016_mc_prodLcpK0s/131_20190811-0106/pklsk,
/data/Derived/LckINT7HighMultwithJets/vAN-20190810_ROOT6-1/pp_2017_mc_prodD2H/134_20190811-0107/pklsk,
/data/Derived/LckINT7HighMultwithJets/vAN-20190810_ROOT6-1/pp_2018_mc_prodD2H/135_20190811-0107/pklsk] #list of periods
pkl_skimmed_merge_for_ml: [/data/Derived/LckINT7HighMultwithJets/vAN-20190810_ROOT6-1/pp_2016_mc_prodD2H/129_20190811-0106/pklskml,
/data/Derived/LckINT7HighMultwithJets/vAN-20190810_ROOT6-1/pp_2016_mc_prodLcpK0s/131_20190811-0106/pklskml,
/data/Derived/LckINT7HighMultwithJets/vAN-20190810_ROOT6-1/pp_2017_mc_prodD2H/134_20190811-0107/pklskml,
/data/Derived/LckINT7HighMultwithJets/vAN-20190810_ROOT6-1/pp_2018_mc_prodD2H/135_20190811-0107/pklskml] #list of periods
pkl_skimmed_merge_for_ml_all: /data/Derived/LckINT7HighMultwithJets/vAN-20190810_ROOT6-1/pklskMLallperiods
pkl_evtcounter_all: /data/Derived/LckINT7HighMultwithJets/vAN-20190810_ROOT6-1/pklevtppallperiod
Processing details.
-
nperiods
: number of periods to be analysed (e.g. LHC16a, b, c, ...) -
nprocessesparallel
: max number of parallel processes -
maxfiles
: max number of files to be processed (-1: all files) -
chunksizeunp
: max number of files per process at unpacking step -
chunksizeskim
: max number of files per process at skimming step -
fracmerge
: fraction of the number of files to be merged per period -
seedmerge
: seed for random merginf from different periods -
period
: list of periods names -
unmerged_tree_dir
: directories where to store unmerged trees -
pkl
: directories where to store unmerged converted trees -
pkl_skimmed
: directories where to store skimmed dataframes -
pkl_skimmed_merge_for_ml
: directories where to store partial merging of skimmed dataframes from ML training/testing -
pkl_skimmed_merge_for_ml_all
: directories where to store merged dataframes after model application -
pkl_evtcounter_all
: directories where to store event dataframes
ml:
nbkg: 500000
nsig: 500000
sampletagforsignal: 1
sampletagforbkg: 0
sel_sigml: ismcprompt == 1
sel_bkgml: inv_mass<2.186 or inv_mass>2.386
nkfolds: 5
rnd_shuffle: 12
rnd_splt: 12
test_frac: 0.2
binmin: [1,2,4,8,12] #list of nbins
binmax: [2,4,8,12,24] #list of nbins
mltype: BinaryClassification
ncorescrossval: 10
mlplot: /data/Derived/LckINT7HighMultwithJets/vAN-20190810_ROOT6-1/mlplot # to be removed
mlout: /data/Derived/LckINT7HighMultwithJets/vAN-20190810_ROOT6-1/mlout # to be removed
opt:
filename_fonll: 'data/fonll/fo_pp_d0meson_5TeV_y0p5.csv' # file with FONLL predictions
fonll_pred: 'max' # edge of the FONLL prediction
FF: 0.1281 # fragmentation fraction
sigma_MB: 57.8e-3 # Minimum Bias cross section (pp) 50.87e-3 [b], 1 for Pb-Pb
Taa: 1 # 23260 [b^-1] in 0-10% Pb-Pb, 3917 [b^-1] in 30-50% Pb-Pb, 1 for pp
BR: 1.09e-2 # branching ratio of the decay Lc->pKpi
f_prompt: 0.9 # estimated fraction of prompt candidates
bkg_data_fraction: 0.1 # fraction of real data used in the estimation
num_steps: 111 # number of steps used in efficiency and signif. estimation
save_fit: True # save bkg fits with the various cuts on ML output
raahp: [1,1,1,1,1] #list of nbins
presel_gen_eff: "abs(y_cand) < 0.5 and abs(z_vtx_gen) < 10"
Machine Learning optimization configuration block. It includes parameters for significance optimization process (to be set for the case under analysis).
-
nbkg
: number of background candidates included in the training/validation/testing sample -
nsig
: number of signal candidates included in the training/validation/testing sample -
sampletagforsignal
: tag for signal sample -
sampletagforbkg
: tag for background sample -
sel_sigml
: signal candidates selections -
sel_bkgml
: background candidates selections (typically side-bands of the invariant mass distribution) -
nkfolds
: number of k folds -
rnd_shuffle
: rnadom shuffle number -
rnd_splt
: number of sub-samples for cross validation -
test_frac
: fraction of candidates kept for testing process -
binmin
: min pt values of the bins -
binmax
: max pt values of the bins -
mltype
: Machine Learning problem type -
ncorescrossval
: number of cores to be used for cross validation -
mlplot
: output directory - control plots -
mlout
: output directory - models
Significance optimization parameters
-
filename_fonll
: file with FONLL predictions -
fonll_pred
: choose which FONLL curve use -
FF
: fragmentation fraction -
sigma_MB
: Minimum Bias cross section for pp, 1 for Pb-Pb -
Taa
: 23260 [b^-1] in 0-10% Pb-Pb, 3917 [b^-1] in 30-50% Pb-Pb, 1 for pp -
BR
: branching ratio of the decay under study -
f_prompt
: estimated fraction of prompt candidates -
bkg_data_fraction
: fraction of real data used in the estimation -
num_steps
: number of steps used in efficiency and signif. estimation -
save_fit
: decide wether to save bkg fits with the various cuts on ML output -
raahp
: array of RAA hypotheses -
presel_gen_eff
: preselections for efficiency estimate
mlapplication:
data:
pkl_skimmed_dec: [/data/Derived/LckINT7HighMultwithJets/vAN-20190810_ROOT6-1/pp_2016_data/136_20190811-01077/pklskdec,
/data/Derived/LckINT7HighMultwithJets/vAN-20190810_ROOT6-1/pp_2016_data/137_20190811-0108/pklskdec,
/data/Derived/LckINT7HighMultwithJets/vAN-20190810_ROOT6-1/pp_2017_data/138_20190811-0108/pklskdec,
/data/Derived/LckINT7HighMultwithJets/vAN-20190810_ROOT6-1/pp_2018_data/139_20190811-0108/pklskdec] #list of periods
pkl_skimmed_decmerged: [/data/Derived/LckINT7HighMultwithJets/vAN-20190810_ROOT6-1/pp_2016_data/136_20190811-01077/pklskdecmerged,
/data/Derived/LckINT7HighMultwithJets/vAN-20190810_ROOT6-1/pp_2016_data/137_20190811-0108/pklskdecmerged,
/data/Derived/LckINT7HighMultwithJets/vAN-20190810_ROOT6-1/pp_2017_data/138_20190811-0108/pklskdecmerged,
/data/Derived/LckINT7HighMultwithJets/vAN-20190810_ROOT6-1/pp_2018_data/139_20190811-0108/pklskdecmerged] #list of periods
mc:
pkl_skimmed_dec: [/data/Derived/LckINT7HighMultwithJets/vAN-20190810_ROOT6-1/pp_2016_mc_prodD2H/129_20190811-0106/pklskdec,
/data/Derived/LckINT7HighMultwithJets/vAN-20190810_ROOT6-1/pp_2016_mc_prodLcpK0s/131_20190811-0106/pklskdec,
/data/Derived/LckINT7HighMultwithJets/vAN-20190810_ROOT6-1/pp_2017_mc_prodD2H/134_20190811-0107/pklskdec,
/data/Derived/LckINT7HighMultwithJets/vAN-20190810_ROOT6-1/pp_2018_mc_prodD2H/135_20190811-0107/pklskdec] #list of periods
pkl_skimmed_decmerged: [/data/Derived/LckINT7HighMultwithJets/vAN-20190810_ROOT6-1/pp_2016_mc_prodD2H/129_20190811-0106/pklskdecmerged,
/data/Derived/LckINT7HighMultwithJets/vAN-20190810_ROOT6-1/pp_2016_mc_prodLcpK0s/131_20190811-0106/pklskdecmerged,
/data/Derived/LckINT7HighMultwithJets/vAN-20190810_ROOT6-1/pp_2017_mc_prodD2H/134_20190811-0107/pklskdecmerged,
/data/Derived/LckINT7HighMultwithJets/vAN-20190810_ROOT6-1/pp_2018_mc_prodD2H/135_20190811-0107/pklskdecmerged] #list of periods
modelname: xgboost
modelsperptbin: [xgboost_classifierLcpK0spp_dfselection_pt_cand_1.0_2.0.sav,
xgboost_classifierLcpK0spp_dfselection_pt_cand_2.0_4.0.sav,
xgboost_classifierLcpK0spp_dfselection_pt_cand_4.0_8.0.sav,
xgboost_classifierLcpK0spp_dfselection_pt_cand_8.0_12.0.sav,
xgboost_classifierLcpK0spp_dfselection_pt_cand_12.0_24.0.sav]
probcutpresel:
data: [0.3,0.3,0.3,0.3,0.3] #list of nbins
mc: [0.3,0.3,0.3,0.3,0.3] #list of nbins
probcutoptimal: [0.4,0.4,0.4,0.3,0.3] #list of nbins
Block where to configure ML model application.
-
pkl_skimmed_dec
,pkl_skimmed_decmerged
: set directories where to store data and Monte Carlo files after model application -
modelname
: name of the chosen model -
modelsperptbin
: model files -
probcutpresel
: loose probability cut to select candidates to be stored in te files -
probcutoptimal
: optimal probability cut used to get invariant mass distributions and efficiencies
analysis:
MBvspt:
plotbin: [1,1,1,0]
usesinglebineff: 0
sel_binmin2: [0,0,30,60] #list of var2 splittng nbins
sel_binmax2: [9999,30,60,100] #list of var2 splitting nbins
var_binning2: n_tracklets_corr
sel_an_binmin: [1,2,3,4,5,6,8,12] #list of pt nbins
sel_an_binmax: [2,3,4,5,6,8,12,24] #list of pt nbins
binning_matching: [0,1,1,2,2,2,3,4] #list of pt nbins
presel_gen_eff: "abs(y_cand) < 0.5 and abs(z_vtx_gen) < 10"
evtsel: is_ev_rej==0
triggersel:
data: "trigger_hasclass_INT7==1 and trigger_hasbit_INT7==1"
mc: null
data:
results: [LckINT7HighMultwithJets/vAN-20190810_ROOT6-1/pp_2016_data/136_20190811-01077/resultsMBvspt,
LckINT7HighMultwithJets/vAN-20190810_ROOT6-1/pp_2016_data/137_20190811-0108/resultsMBvspt,
LckINT7HighMultwithJets/vAN-20190810_ROOT6-1/pp_2017_data/138_20190811-0108/resultsMBvspt,
LckINT7HighMultwithJets/vAN-20190810_ROOT6-1/pp_2018_data/139_20190811-0108/resultsMBvspt] #list of periods
resultsallp: LckINT7HighMultwithJets/vAN-20190810_ROOT6-1/pp_data/resultsMBvspt
mc:
results: [LckINT7HighMultwithJets/vAN-20190810_ROOT6-1/pp_2016_mc_prodD2H/129_20190811-0106/resultsMBvspt,
LckINT7HighMultwithJets/vAN-20190810_ROOT6-1/pp_2016_mc_prodLcpK0s/131_20190811-0106/resultsMBvspt,
LckINT7HighMultwithJets/vAN-20190810_ROOT6-1/pp_2017_mc_prodD2H/134_20190811-0107/resultsMBvspt,
LckINT7HighMultwithJets/vAN-20190810_ROOT6-1/pp_2018_mc_prodD2H/135_20190811-0107/resultsMBvspt] #list of periods
resultsallp: LckINT7HighMultwithJets/vAN-20190810_ROOT6-1/pp_mc/prodD2H/resultsMBvspt
mass_fit_lim: [2.14, 2.48] # region for the fit of the invariant mass distribution [GeV/c^2]
bin_width: 0.001 # bin width of the invariant mass histogram
usefit: true
sgnfunc: [kGaus,kGaus,kGaus,kGaus,kGaus,kGaus,kGaus,kGaus]
bkgfunc: [Pol2,Pol2,Pol2,Pol2,Pol2,Pol2,Pol2,Pol2]
masspeak: 2.2864
massmin: [2.14,2.14,2.14,2.14,2.14,2.14,2.14,2.14]
massmax: [2.436,2.436,2.436,2.436,2.436,2.436,2.436,2.436]
rebin: [6,6,6,6,6,6,6,6]
includesecpeak: [0,0,0,0,0,0,0,0]
masssecpeak: 2.2864
FixedMean: true
SetFixGaussianSigma: true
SetInitialGaussianMean: true
dolikelihood: true
sigmaarray: [0.0078, 0.0078, 0.0082, 0.0091, 0.0097, 0.0109, 0.0117, 0.0156]
FixedSigma: true
fitcase: Lc
latexnamemeson: "L_{c}^{K0s}"
latexbin2var: "n_{trkl}"
nevents: 1700000000.
dodoublecross: false
Block where to configure final analysis. There can be different types of analysis, e.g. MBjetvspt
, SPDvspt
, ...
-
plotbin
: set which bins have to plotted [1,1,1,0] -
usesinglebineff
: -
sel_binmin2
,sel_binmax2
: select min and max values of a an additional variable w.r.t. pt, e.g. multiplicity -
var_binning2
: additional variable to be considered for a double-differential analysis -
sel_an_binmin
,sel_an_binmax
: list of min and max values for the first variable -
binning_matching
: decide wether to merge or not first variable bins -
presel_gen_eff
: preselections for efficiency estimate -
evtsel
: event selection -
triggersel
: trigger selection for data and MC -
results
,resultsallp
: output directories -
mass_fit_lim
: region for the fit of the invariant mass distribution [GeV/c^2] -
bin_width
: bin width of the invariant mass histogram -
usefit
: decide wether to perform the fit or not -
sgnfunc
: signal fit function -
bkgfunc
: background fit function -
masspeak
: PDG mass -
massmin
,massmax
: invariant mass range of the fit -
rebin
: histogram rebinning -
includesecpeak
: include fit of a second peak, e.g. D+ in case of Ds->KKpi -
masssecpeak
: PDG mass of the second peak -
FixedMean
: decide wether to fix mean or not -
SetFixGaussianSigma
: decide wether to fix sigma or not -
SetInitialGaussianMean
: set initial mean values -
dolikelihood
: decide wether to use likelihood fit option -
sigmaarray
: array of sigma values -
FixedSigma
: decide wether to fix sigma or not -
fitcase
: fit case -
latexnamemeson
: Latex format name of the particle under study -
latexbin2var
: Latex format name of the second variable -
nevents
: number of events dodoublecross
systematics:
probvariation:
prob_range: [0.5,0.6,0.7]
Block to configure the variation of the ML probability cut for the estimate of the systematic uncertainty.
validation:
data:
dir: [dataval_16, dataval_16, dataval_17, dataval_18] #list of periods
dirmerged: datavaltot
mc:
dir: [mcval_16, mcval_16, mcval_17, mcval_18] #list of periods
dirmerged: mcvaltot
Directories where to store number of event information.