atlas ttb 8tev

buildmaster/ATLAS_TTBAR_8TEV_2L_DIF/filter.py

@@ -0,0 +1,214 @@
+import yaml
+from validphys.commondata_utils import covmat_to_artunc as cta
+
+def processData():
+    with open('metadata.yaml', 'r') as file:
+        metadata = yaml.safe_load(file)
+
+    data_central_dSig_dmttBar = []
+    kin_dSig_dmttBar = []
+    error_dSig_dmttBar = []
+    data_central_dSig_dmttBar_norm = []
+    kin_dSig_dmttBar_norm = []
+    error_dSig_dmttBar_norm = []
+    data_central_dSig_dyttBar = []
+    kin_dSig_dyttBar = []
+    error_dSig_dyttBar = []
+    data_central_dSig_dyttBar_norm = []
+    kin_dSig_dyttBar_norm = []
+    error_dSig_dyttBar_norm = []
+
+    covMatArray_dSig_dmttBar = []
+    covMatArray_dSig_dmttBar_norm = []
+    covMatArray_dSig_dyttBar = []
+    covMatArray_dSig_dyttBar_norm = []
+
+# dSig_dmttBar data
+
+    hepdata_tables="rawdata/Table_10.yaml"
+    with open(hepdata_tables, 'r') as file:
+        input = yaml.safe_load(file)
+
+    covariance_matrix="rawdata/Table_22.yaml"
+    with open(covariance_matrix, 'r') as file:
+        input2 = yaml.safe_load(file)
+
+    sqrt_s = float(input['dependent_variables'][0]['qualifiers'][1]['value'])
+    mt_sqr = 29756.25
+    values = input['dependent_variables'][0]['values']
+
+    for i in range(len(values)*len(values)):
+        covMatArray_dSig_dmttBar.append(input2['dependent_variables'][0]['values'][i]['value'])
+    artUnc_dSig_dmttBar = cta(len(values), covMatArray_dSig_dmttBar)
+
+    for i in range(len(values)):
+        m_ttBar_min = input['independent_variables'][0]['values'][i]['low']
+        m_ttBar_max = input['independent_variables'][0]['values'][i]['high']
+        error_value = {}
+        for j in range(len(values)):
+            error_value['ArtUnc_'+str(j+1)] = artUnc_dSig_dmttBar[i][j]
+        data_central_value = values[i]['value']
+        data_central_dSig_dmttBar.append(data_central_value)
+        error_dSig_dmttBar.append(error_value)
+        kin_value = {'sqrt_s': {'min': None, 'mid': sqrt_s, 'max': None}, 'mt_sqr': {'min': None, 'mid': mt_sqr, 'max': None}, 'm_ttBar': {'min': m_ttBar_min, 'mid': None, 'max': m_ttBar_max}}
+        kin_dSig_dmttBar.append(kin_value)
+
+    error_definition_dSig_dmttBar = {}
+    for i in range(len(values)):
+        error_definition_dSig_dmttBar['ArtUnc_'+str(i+1)] = {'description': 'artificial uncertainty '+str(i+1), 'treatment': 'ADD', 'type': 'CORR'}
+
+    data_central_dSig_dmttBar_yaml = {'data_central': data_central_dSig_dmttBar}
+    kinematics_dSig_dmttBar_yaml = {'bins': kin_dSig_dmttBar}
+    uncertainties_dSig_dmttBar_yaml = {'definitions': error_definition_dSig_dmttBar, 'bins': error_dSig_dmttBar}
+
+    with open('data_dSig_dmttBar.yaml', 'w') as file:
+         yaml.dump(data_central_dSig_dmttBar_yaml, file, sort_keys=False)
+
+    with open('kinematics_dSig_dmttBar.yaml', 'w') as file:
+         yaml.dump(kinematics_dSig_dmttBar_yaml, file, sort_keys=False)
+
+    with open('uncertainties_dSig_dmttBar.yaml', 'w') as file:
+        yaml.dump(uncertainties_dSig_dmttBar_yaml, file, sort_keys=False)
+
+# dSig_dmttBar_norm data
+
+    hepdata_tables="rawdata/Table_4.yaml"
+    with open(hepdata_tables, 'r') as file:
+        input = yaml.safe_load(file)
+
+    covariance_matrix="rawdata/Table_16.yaml"
+    with open(covariance_matrix, 'r') as file:
+        input2 = yaml.safe_load(file)
+
+    sqrt_s = float(input['dependent_variables'][0]['qualifiers'][1]['value'])
+    mt_sqr = 29756.25
+    values = input['dependent_variables'][0]['values']
+
+    for i in range(len(values)*len(values)):
+        covMatArray_dSig_dmttBar_norm.append(input2['dependent_variables'][0]['values'][i]['value']*1e-6)
+    artUnc_dSig_dmttBar_norm = cta(len(values), covMatArray_dSig_dmttBar_norm, 1)
+
+    for i in range(len(values)):
+        m_ttBar_min = input['independent_variables'][0]['values'][i]['low']
+        m_ttBar_max = input['independent_variables'][0]['values'][i]['high']
+        error_value = {}
+        for j in range(len(values)):
+            error_value['ArtUnc_'+str(j+1)] = artUnc_dSig_dmttBar_norm[i][j]
+        data_central_value = values[i]['value']*1e-3
+        data_central_dSig_dmttBar_norm.append(data_central_value)
+        error_dSig_dmttBar_norm.append(error_value)
+        kin_value = {'sqrt_s': {'min': None, 'mid': sqrt_s, 'max': None}, 'mt_sqr': {'min': None, 'mid': mt_sqr, 'max': None}, 'm_ttBar': {'min': m_ttBar_min, 'mid': None, 'max': m_ttBar_max}}
+        kin_dSig_dmttBar_norm.append(kin_value)
+
+    error_definition_dSig_dmttBar_norm = {}
+    for i in range(len(values)):
+        error_definition_dSig_dmttBar_norm['ArtUnc_'+str(i+1)] = {'description': 'artificial uncertainty '+str(i+1), 'treatment': 'ADD', 'type': 'CORR'}
+
+    data_central_dSig_dmttBar_norm_yaml = {'data_central': data_central_dSig_dmttBar_norm}
+    kinematics_dSig_dmttBar_norm_yaml = {'bins': kin_dSig_dmttBar_norm}
+    uncertainties_dSig_dmttBar_norm_yaml = {'definitions': error_definition_dSig_dmttBar_norm, 'bins': error_dSig_dmttBar_norm}
+
+    with open('data_dSig_dmttBar_norm.yaml', 'w') as file:
+         yaml.dump(data_central_dSig_dmttBar_norm_yaml, file, sort_keys=False)
+
+    with open('kinematics_dSig_dmttBar_norm.yaml', 'w') as file:
+         yaml.dump(kinematics_dSig_dmttBar_norm_yaml, file, sort_keys=False)
+
+    with open('uncertainties_dSig_dmttBar_norm.yaml', 'w') as file:
+        yaml.dump(uncertainties_dSig_dmttBar_norm_yaml, file, sort_keys=False)
+
+# dSig_dyttBar data
+
+    hepdata_tables="rawdata/Table_12.yaml"
+    with open(hepdata_tables, 'r') as file:
+        input = yaml.safe_load(file)
+
+    covariance_matrix="rawdata/Table_24.yaml"
+    with open(covariance_matrix, 'r') as file:
+        input2 = yaml.safe_load(file)
+
+    sqrt_s = float(input['dependent_variables'][0]['qualifiers'][1]['value'])
+    mt_sqr = 29756.25
+    values = input['dependent_variables'][0]['values']
+
+    for i in range(len(values)*len(values)):
+        covMatArray_dSig_dyttBar.append(input2['dependent_variables'][0]['values'][i]['value'])
+    artUnc_dSig_dyttBar = cta(len(values), covMatArray_dSig_dyttBar)
+
+    for i in range(len(values)):
+        y_ttBar_min = input['independent_variables'][0]['values'][i]['low']
+        y_ttBar_max = input['independent_variables'][0]['values'][i]['high']
+        error_value = {}
+        for j in range(len(values)):
+            error_value['ArtUnc_'+str(j+1)] = artUnc_dSig_dyttBar[i][j]
+        data_central_value = values[i]['value']
+        data_central_dSig_dyttBar.append(data_central_value)
+        error_dSig_dyttBar.append(error_value)
+        kin_value = {'sqrt_s': {'min': None, 'mid': sqrt_s, 'max': None}, 'mt_sqr': {'min': None, 'mid': mt_sqr, 'max': None}, 'y_ttBar': {'min': y_ttBar_min, 'mid': None, 'max': y_ttBar_max}}
+        kin_dSig_dyttBar.append(kin_value)
+
+    error_definition_dSig_dyttBar = {}
+    for i in range(len(values)):
+        error_definition_dSig_dyttBar['ArtUnc_'+str(i+1)] = {'description': 'artificial uncertainty '+str(i+1), 'treatment': 'ADD', 'type': 'CORR'}
+
+    data_central_dSig_dyttBar_yaml = {'data_central': data_central_dSig_dyttBar}
+    kinematics_dSig_dyttBar_yaml = {'bins': kin_dSig_dyttBar}
+    uncertainties_dSig_dyttBar_yaml = {'definitions': error_definition_dSig_dyttBar, 'bins': error_dSig_dyttBar}
+
+    with open('data_dSig_dyttBar.yaml', 'w') as file:
+         yaml.dump(data_central_dSig_dyttBar_yaml, file, sort_keys=False)
+
+    with open('kinematics_dSig_dyttBar.yaml', 'w') as file:
+         yaml.dump(kinematics_dSig_dyttBar_yaml, file, sort_keys=False)
+
+    with open('uncertainties_dSig_dyttBar.yaml', 'w') as file:
+        yaml.dump(uncertainties_dSig_dyttBar_yaml, file, sort_keys=False)
+
+# dSig_dyttBar_norm data
+
+    hepdata_tables="rawdata/Table_6.yaml"
+    with open(hepdata_tables, 'r') as file:
+        input = yaml.safe_load(file)
+
+    covariance_matrix="rawdata/Table_18.yaml"
+    with open(covariance_matrix, 'r') as file:
+        input2 = yaml.safe_load(file)
+
+    sqrt_s = float(input['dependent_variables'][0]['qualifiers'][1]['value'])
+    mt_sqr = 29756.25
+    values = input['dependent_variables'][0]['values']
+
+    for i in range(len(values)*len(values)):
+        covMatArray_dSig_dyttBar_norm.append(input2['dependent_variables'][0]['values'][i]['value'])
+    artUnc_dSig_dyttBar_norm = cta(len(values), covMatArray_dSig_dyttBar_norm, 1)
+
+    for i in range(len(values)):
+        y_ttBar_min = input['independent_variables'][0]['values'][i]['low']
+        y_ttBar_max = input['independent_variables'][0]['values'][i]['high']
+        error_value = {}
+        for j in range(len(values)):
+            error_value['ArtUnc_'+str(j+1)] = artUnc_dSig_dyttBar_norm[i][j]
+        data_central_value = values[i]['value']
+        data_central_dSig_dyttBar_norm.append(data_central_value)
+        error_dSig_dyttBar_norm.append(error_value)
+        kin_value = {'sqrt_s': {'min': None, 'mid': sqrt_s, 'max': None}, 'mt_sqr': {'min': None, 'mid': mt_sqr, 'max': None}, 'y_ttBar': {'min': y_ttBar_min, 'mid': None, 'max': y_ttBar_max}}
+        kin_dSig_dyttBar_norm.append(kin_value)
+
+    error_definition_dSig_dyttBar_norm = {}
+    for i in range(len(values)):
+        error_definition_dSig_dyttBar_norm['ArtUnc_'+str(i+1)] = {'description': 'artificial uncertainty '+str(i+1), 'treatment': 'ADD', 'type': 'CORR'}
+
+    data_central_dSig_dyttBar_norm_yaml = {'data_central': data_central_dSig_dyttBar_norm}
+    kinematics_dSig_dyttBar_norm_yaml = {'bins': kin_dSig_dyttBar_norm}
+    uncertainties_dSig_dyttBar_norm_yaml = {'definitions': error_definition_dSig_dyttBar_norm, 'bins': error_dSig_dyttBar_norm}
+
+    with open('data_dSig_dyttBar_norm.yaml', 'w') as file:
+         yaml.dump(data_central_dSig_dyttBar_norm_yaml, file, sort_keys=False)
+
+    with open('kinematics_dSig_dyttBar_norm.yaml', 'w') as file:
+         yaml.dump(kinematics_dSig_dyttBar_norm_yaml, file, sort_keys=False)
+
+    with open('uncertainties_dSig_dyttBar_norm.yaml', 'w') as file:
+        yaml.dump(uncertainties_dSig_dyttBar_norm_yaml, file, sort_keys=False)
+
+processData()

buildmaster/ATLAS_TTBAR_8TEV_2L_DIF/metadata.yaml

@@ -0,0 +1,128 @@
+setname: "ATLAS_TTBAR_8TEV_2L_DIF"
+
+nnpdf_metadata:
+  nnpdf31_process: "TOP"
+  experiment: "ATLAS"
+
+arXiv:
+  url: "https://arxiv.org/abs/1607.07281"
+iNSPIRE:
+  url: "https://inspirehep.net/literature/1477814"
+hepdata:
+  url: "https://www.hepdata.net/record/ins1477814"
+  version: 1
+
+version: 1
+version_comment: "NA"
+
+implemented_observables:
+
+  - observable: {description: "differential cross section w.r.t. ttBar invariant mass", label: '$\frac{d\sigma}{dm_{t\bar{t}}}$', units: "$\frac{pb}{GeV}$"}
+    observable_name: MTTBAR
+    process_type: HQP_MQQ
+    ndata: 6
+    tables: [10]
+    kinematics:
+      variables:
+        m_ttBar: {description: "invariant mass of ttBar", label: '$m_{t\bar{t}}$', units: "$GeV$"}
+        mt_sqr: {description: "mass of top quark squared", label: '$m_{t}^2$', units: '$GeV^2$'}
+        sqrt_s: {description: "center of mass energy", label: '$\sqrt{s}$', units: "$GeV$"}
+      file: kinematics_dSig_dmttBar.yaml
+    data_central: data_dSig_dmttBar.yaml
+    data_uncertainties:
+      - uncertainties_dSig_dmttBar.yaml
+    kinematic_coverage: [m_ttBar, mt_sqr, sqrt_s]
+    plotting:
+      dataset_label: 'ATLAS 8 TeV top quark pair dilepton channel: $\frac{d\sigma}{dm_{t\bar{t}}}$'
+      kinematics_override: identity
+      x_scale: log
+      plot_x: m_ttBar
+      figure_by: 
+        - sqrt_s
+    theory:
+      FK_tables:
+        - - ATLAS_TTBAR_8TEV_2L_DIF_MTTBAR
+      operation: 'null'
+
+  - observable: {description: "normalized differential cross section w.r.t. ttBar invariant mass", label: '$\frac{1}{\sigma}\frac{d\sigma}{dm_{t\bar{t}}}$', units: "$\frac{1}{GeV}$"}
+    observable_name: MTTBAR-NORM
+    process_type: HQP_MQQ
+    ndata: 6
+    tables: [4]
+    kinematics:
+      variables:
+        m_ttBar: {description: "invariant mass of ttBar", label: '$m_{t\bar{t}}$', units: "$GeV$"}
+        mt_sqr: {description: "mass of top quark squared", label: '$m_{t}^2$', units: '$GeV^2$'}
+        sqrt_s: {description: "center of mass energy", label: '$\sqrt{s}$', units: "$GeV$"}
+      file: kinematics_dSig_dmttBar_norm.yaml
+    data_central: data_dSig_dmttBar_norm.yaml
+    data_uncertainties:
+      - uncertainties_dSig_dmttBar_norm.yaml
+    kinematic_coverage: [m_ttBar, mt_sqr, sqrt_s]
+    plotting:
+      dataset_label: 'ATLAS 8 TeV top quark pair dilepton channel: $\frac{1}{\sigma}\frac{d\sigma}{dm_{t\bar{t}}}$'
+      kinematics_override: identity
+      x_scale: log
+      plot_x: m_ttBar
+      figure_by: 
+        - sqrt_s
+    theory:
+      FK_tables:
+        - - ATLAS_TTBAR_8TEV_2L_DIF_MTTBAR
+        - - ATLAS_TTBAR_8TEV_TOT_X-SEC
+      operation: 'ratio'
+
+  - observable: {description: "differential cross section w.r.t. absolute rapidity of ttBar", label: '$\frac{d\sigma}{d|y_{t\bar{t}}|}$', units: "$pb$"}
+    observable_name: YTTBAR
+    process_type: HQP_YQQ
+    ndata: 5
+    tables: [12]
+    kinematics:
+      variables:
+        y_ttBar: {description: "absolute rapidity of ttBar", label: '$|y_{t\bar{t}}|$', units: ""}
+        mt_sqr: {description: "mass of top quark squared", label: '$m_{t}^2$', units: '$GeV^2$'}
+        sqrt_s: {description: "center of mass energy", label: '$\sqrt{s}$', units: "$GeV$"}
+      file: kinematics_dSig_dyttBar.yaml
+    data_central: data_dSig_dyttBar.yaml
+    data_uncertainties:
+      - uncertainties_dSig_dyttBar.yaml
+    kinematic_coverage: [y_ttBar, mt_sqr, sqrt_s]
+    plotting:
+      dataset_label: 'ATLAS 8 TeV top quark pair dilepton channel: $d\sigma/d|y_{t\bar{t}}|$'
+      kinematics_override: identity
+      x_scale: log
+      plot_x: y_ttBar
+      figure_by: 
+        - sqrt_s
+    theory:
+      FK_tables:
+        - - ATLAS_TTBAR_8TEV_2L_DIF_YTTBAR
+      operation: 'null'
+
+  - observable: {description: "normalized differential cross section w.r.t. absolute rapidity of ttBar", label: '$\frac{1}{\sigma}\frac{d\sigma}{d|y_{t\bar{t}}|}$', units: ""}
+    observable_name: YTTBAR-NORM
+    process_type: HQP_YQQ
+    ndata: 5
+    tables: [6]
+    kinematics:
+      variables:
+        y_ttBar: {description: "absolute rapidity of ttBar", label: '$|y_{t\bar{t}}|$', units: ""}
+        mt_sqr: {description: "mass of top quark squared", label: '$m_{t}^2$', units: '$GeV^2$'}
+        sqrt_s: {description: "center of mass energy", label: '$\sqrt{s}$', units: "$GeV$"}
+      file: kinematics_dSig_dyttBar_norm.yaml
+    data_central: data_dSig_dyttBar_norm.yaml
+    data_uncertainties:
+      - uncertainties_dSig_dyttBar_norm.yaml
+    kinematic_coverage: [y_ttBar, mt_sqr, sqrt_s]
+    plotting:
+      dataset_label: 'ATLAS 8 TeV top quark pair dilepton channel: $\frac{1}{\sigma}\frac{d\sigma}{d|y_{t\bar{t}}|}$'
+      kinematics_override: identity
+      x_scale: log
+      plot_x: y_ttBar
+      figure_by: 
+        - sqrt_s
+    theory:
+      FK_tables:
+        - - ATLAS_TTBAR_8TEV_2L_DIF_YTTBAR
+        - - ATLAS_TTBAR_8TEV_TOT_X-SEC
+      operation: 'ratio'

buildmaster/ATLAS_TTBAR_8TEV_2L_DIF/rawdata/Table_1.yaml

@@ -0,0 +1,34 @@
+dependent_variables:
+- header: {name: '1/SIG*DSIG/DM_TTBAR [10$^{-3}$/GEV]'}
+  qualifiers:
+  - {name: RE, value: P P --> TOP TOPBAR X}
+  - {name: SQRT(S), units: GeV, value: '7000.0'}
+  values:
+  - errors:
+    - {label: stat, symerror: 1.6%}
+    - {label: sys, symerror: 2.9%}
+    value: 2.41
+  - errors:
+    - {label: stat, symerror: 1.4%}
+    - {label: sys, symerror: 1.0%}
+    value: 2.79
+  - errors:
+    - {label: stat, symerror: 3.1%}
+    - {label: sys, symerror: 4.6%}
+    value: 1.09
+  - errors:
+    - {label: stat, symerror: 5.7%}
+    - {label: sys, symerror: 7.2%}
+    value: 0.252
+  - errors:
+    - {label: stat, symerror: 16.0%}
+    - {label: sys, symerror: 14.0%}
+    value: 0.0066
+independent_variables:
+- header: {name: M_TTBAR, units: GEV}
+  values:
+  - {high: 450.0, low: 250.0}
+  - {high: 550.0, low: 450.0}
+  - {high: 700.0, low: 550.0}
+  - {high: 950.0, low: 700.0}
+  - {high: 2700.0, low: 950.0}