HF jets: Hard Probes 2024 Preliminaries

machine_learning_hep/analysis/do_systematics.py

@@ -343,7 +343,7 @@ def do_jet_systematics(self, var: str):
             input_histograms_default.append(input_file_default.Get(name_his))
             if not input_histograms_default[iptjet]:
                 self.logger.critical(make_message_notfound(name_his, path_def))
-            self.crop_histogram(input_histograms_default[iptjet], var)
+            # self.crop_histogram(input_histograms_default[iptjet], var)
             print(f"Default histogram ({range_ptjet[0]} to {range_ptjet[1]})")
             print_histogram(input_histograms_default[iptjet], self.verbose)
             # name_eff = f"h_ptjet-pthf_effnew_pr_{string_range_ptjet(range_ptjet)}"
@@ -411,7 +411,7 @@ def do_jet_systematics(self, var: str):
                     path_eff_file = path_eff.replace(self.string_default, string_catvar)
                     if not sys_var_histo_eff:
                         self.logger.critical(make_message_notfound(name_eff, path_eff_file))
-                    self.crop_histogram(sys_var_histo, var)
+                    # self.crop_histogram(sys_var_histo, var)
                     input_histograms_syscatvar.append(sys_var_histo)
                     input_histograms_eff.append(sys_var_histo_eff)
                     print_histogram(sys_var_histo_eff, self.verbose)
@@ -1017,8 +1017,8 @@ def do_jet_systematics(self, var: str):
             leg_finalwsys = TLegend(0.7, 0.78, 0.85, 0.88)
             setup_legend(leg_finalwsys)
             leg_finalwsys.AddEntry(input_histograms_default[iptjet], "data", "P")
-            self.crop_histogram(input_histograms_default[iptjet], var)
-            self.crop_graph(tgsys[iptjet], var)
+            # self.crop_histogram(input_histograms_default[iptjet], var)
+            # self.crop_graph(tgsys[iptjet], var)
             setup_histogram(input_histograms_default[iptjet], get_colour(0, 0))
             y_min_g, y_max_g = get_y_window_gr([tgsys[iptjet]])
             y_min_h, y_max_h = get_y_window_his([input_histograms_default[iptjet]])
@@ -1032,9 +1032,9 @@ def do_jet_systematics(self, var: str):
             # input_histograms_default[iptjet].GetXaxis().SetRangeUser(
             #     round(obs_gen_min, 2), round(obs_gen_max, 2)
             # )
-            input_histograms_default[iptjet].GetXaxis().SetRangeUser(
-                round(x_range[var][0], 2), round(x_range[var][1], 2)
-            )
+            # input_histograms_default[iptjet].GetXaxis().SetRangeUser(
+            #     round(x_range[var][0], 2), round(x_range[var][1], 2)
+            # )
             input_histograms_default[iptjet].SetTitle("")
             input_histograms_default[iptjet].SetXTitle(latex_obs)
             input_histograms_default[iptjet].SetYTitle(latex_y)
@@ -1091,9 +1091,9 @@ def do_jet_systematics(self, var: str):
             crelativesys.SetRightMargin(0.25)
             leg_relativesys = TLegend(0.77, 0.2, 0.95, 0.85)
             setup_legend(leg_relativesys, textsize=self.fontsize)
-            for g in tgsys_cat[iptjet]:
-                self.crop_graph(g, var)
-            self.crop_histogram(h_default_stat_err[iptjet], var)
+            # for g in tgsys_cat[iptjet]:
+            #     self.crop_graph(g, var)
+            # self.crop_histogram(h_default_stat_err[iptjet], var)
             y_min_g, y_max_g = get_y_window_gr(tgsys_cat[iptjet])
             y_min_h, y_max_h = get_y_window_his([h_default_stat_err[iptjet]])
             y_min = min(y_min_g, y_min_h)
@@ -1198,9 +1198,9 @@ def do_jet_systematics(self, var: str):
             # leg_relativesys_gr = TLegend(.77, .2, 0.95, .85)
             leg_relativesys_gr = TLegend(0.77 * 9 / 10, 0.5, 0.95, 0.85)  # scale for width 900 -> 1000
             setup_legend(leg_relativesys_gr, textsize=self.fontsize)
-            for g in tgsys_gr[iptjet]:
-                self.crop_graph(g, var)
-            self.crop_histogram(h_default_stat_err[iptjet], var)
+            # for g in tgsys_gr[iptjet]:
+            #     self.crop_graph(g, var)
+            # self.crop_histogram(h_default_stat_err[iptjet], var)
             y_min_g, y_max_g = get_y_window_gr(tgsys_gr[iptjet])
             y_min_h, y_max_h = get_y_window_his([h_default_stat_err[iptjet]])
             y_min = min(y_min_g, y_min_h)

machine_learning_hep/data/data_run3/database_ml_parameters_LcJet_pp.yml

@@ -485,7 +485,7 @@ LcJet_pp:
       usejetptbinned_deff: false
       doeff_resp: true #efficiency correction for the response matrix
       unmatched_gen: true
-      latexnamehadron: "#Lambda_{c}"
+      latexnamehadron: "#Lambda_{c}^{#plus}"
       latexnamedecay: "pK#pi"
       var_binning2: pt_jet
       var_binning2_gen: pt_gen_jet

machine_learning_hep/data/data_run3/database_variations_D0Jet_pp_jet_obs.yml

@@ -36,7 +36,7 @@ categories:
         rms_both_sides: true
         variations:
             bkgfunc:
-                activate: [yes]
+                activate: [no]
                 label: ["bkg. func."]
                 diffs:
                     analysis:
@@ -119,7 +119,7 @@ categories:
                                       model:
                                         fn: 'SUM::refl(frac_l[0.,1.]*refl_l, refl_r)'
                                   - level: mcrefl
-                                    ptrange: [12., 36.]
+                                    ptrange: [12., 48.]
                                     datasel: mcrefl
                                     range: [1.40, 2.4]
                                     components:
@@ -172,7 +172,7 @@ categories:
                                       model:
                                         fn: 'SUM::sigrefl(frac_refl[0.,1.]*refl, sig)'
                                   - level: mc
-                                    ptrange: [12., 36.]
+                                    ptrange: [12., 48.]
                                     range: [1.40, 2.40]
                                     fix_params: ['frac_l', 'mean_l', 'mean_r', 'sigma_l', 'sigma_r', 'frac_wide', 'sigma_g1', 'sigma_wide']
                                     components:
@@ -262,7 +262,7 @@ categories:
                         jet_obs:
                             todo: [0]
             massmin:
-                activate: [yes]
+                activate: [no]
                 label:
                     - "#it{m} min. 1.75"
                 diffs:
@@ -346,7 +346,7 @@ categories:
                                       model:
                                         fn: 'SUM::refl(frac_l[0.,1.]*refl_l, refl_r)'
                                   - level: mcrefl
-                                    ptrange: [12., 36.]
+                                    ptrange: [12., 48.]
                                     datasel: mcrefl
                                     range: [1.40, 2.4]
                                     components:
@@ -399,7 +399,7 @@ categories:
                                       model:
                                         fn: 'SUM::sigrefl(frac_refl[0.,1.]*refl, sig)'
                                   - level: mc
-                                    ptrange: [12., 36.]
+                                    ptrange: [12., 48.]
                                     range: [1.40, 2.40]
                                     fix_params: ['frac_l', 'mean_l', 'mean_r', 'sigma_l', 'sigma_r', 'frac_wide', 'sigma_g1', 'sigma_wide']
                                     components:
@@ -678,7 +678,7 @@ categories:
                                         signal: [-2.5, 2.5]  # variation
                                         right: [3., 5.5]
             ranges:
-                activate: [yes, yes, yes, yes, yes, yes, yes, yes]
+                activate: [yes, yes, yes, yes, no, no, no, no]
                 label:
                     - "SB 3#minus6#it{#sigma}"
                     - "SB 3#minus5#it{#sigma}"
@@ -866,7 +866,7 @@ categories:
         rms_both_sides: true
         variations:
             flatness:
-                activate: [yes, yes, yes, yes, yes, yes, yes, yes, yes, yes]
+                activate: [yes, yes, yes, yes, yes, yes, yes, yes, yes, no]
                 label: ["flat 0.1", "flat 0.2", "flat 0.3", "flat 0.4", "flat 0.5", "flat 0.6", "flat 0.7", "flat 0.8", "flat 0.9", "flat 1.0"]
                 diffs:
                     analysis:

machine_learning_hep/data/data_run3/database_variations_LcJet_pp_jet_obs.yml

@@ -434,7 +434,7 @@ categories:
         rms_both_sides: true
         variations:
             flatness:
-                activate: [yes, yes, yes, yes, yes, yes, yes, yes, yes, yes]
+                activate: [yes, yes, yes, yes, yes, yes, yes, yes, yes, no]
                 label: ["flat 0.1", "flat 0.2", "flat 0.3", "flat 0.4", "flat 0.5", "flat 0.6", "flat 0.7", "flat 0.8", "flat 0.9", "flat 1.0"]
                 diffs:
                     analysis:
@@ -559,7 +559,7 @@ categories:
         rms_both_sides: true
         variations:
             ml:
-                activate: [no, no, no, yes, yes, yes, yes, yes, yes, yes, yes, yes, yes]
+                activate: [no, no, no, yes, yes, yes, yes, no, yes, yes, yes, yes, yes]
                 label:
                     # default: working point (for tests, should be same as the default result)
                     # null: no cuts (for tests, whatever was applied on Hyperloop)