Handling of nonperturbative parameter Lambda4, N3LO corrections support

scripts/corrections/corrs_by_helicity/make_alphaS_gen_hists.py

@@ -16,6 +16,8 @@
     "scetlib_dyturbo_LatticeNP_CT18Z_N3p0LL_N2LO_pdfas": {"pdf": "ct18z"},
     "scetlib_dyturbo_LatticeNP_CT18Z_N3p1LL_N2LO_pdfas": {"pdf": "ct18z"},
     "scetlib_dyturbo_LatticeNP_CT18Z_N4p0LL_N2LO_pdfas": {"pdf": "ct18z"},
+    "scetlib_nnlojet_LatticeNPCoarse_CT18Z_N3p1LL_N3LO_pdfas": {"pdf": "ct18z"},
+    "scetlib_nnlojet_LatticeNPCoarse_CT18Z_N4p0LL_N3LO_pdfas": {"pdf": "ct18z"},
     "scetlib_nnlojet_CT18Z_N3p1LL_N3LO_pdfas": {"pdf": "ct18z"},
     "scetlib_nnlojet_CT18Z_N4p0LL_N3LO_pdfas": {"pdf": "ct18z"},
     "scetlib_nnlojet_MSHT20an3lo_N4p0LL_N3LO_pdfas": {"pdf": "msht20an3lo"},

scripts/corrections/corrs_by_helicity/make_pdf_from_corr_gen_hists.py

@@ -16,6 +16,8 @@
     "scetlib_dyturbo_LatticeNP_CT18Z_N3p0LL_N2LO_pdfvars": {"pdf": "ct18z"},
     "scetlib_dyturbo_LatticeNP_CT18Z_N3p1LL_N2LO_pdfvars": {"pdf": "ct18z"},
     "scetlib_dyturbo_LatticeNP_CT18Z_N4p0LL_N2LO_pdfvars": {"pdf": "ct18z"},
+    "scetlib_nnlojet_LatticeNPCoarse_CT18Z_N3p1LL_N3LO_pdfvars": {"pdf": "ct18z"},
+    "scetlib_nnlojet_LatticeNPCoarse_CT18Z_N4p0LL_N3LO_pdfvars": {"pdf": "ct18z"},
     "scetlib_dyturbo_LatticeNP_CT18_N3p0LL_N2LO_pdfvars": {"pdf": "ct18"},
     "scetlib_dyturbo_LatticeNP_HERAPDF20_N3p0LL_N2LO_pdfvars": {
         "pdf": "herapdf20 herapdf20ext"

scripts/corrections/make_rescaled_theory_corr.py

@@ -16,6 +16,25 @@ def corr_name(corrf):
     return match[1]
 
 
+def find_corr_hist_name(corr_dict, proc, corrf, suffix):
+    base = corr_name(corrf)
+    candidates = [
+        f"{base}{suffix}",
+        f"{base.rstrip('_')}_{suffix}",
+    ]
+    candidates = list(dict.fromkeys(candidates))
+
+    for candidate in candidates:
+        if candidate in corr_dict[proc]:
+            return candidate
+
+    available = [k for k in corr_dict[proc].keys() if k.endswith(suffix)]
+    raise KeyError(
+        f"Could not find histogram with suffix '{suffix}' for file {corrf}. "
+        f"Tried {candidates}. Available matches: {available}"
+    )
+
+
 def parse_args():
     parser = parsing.base_parser()
     parser.add_argument(
@@ -63,10 +82,11 @@ def main():
 
     proc = "Z" if "CorrZ" in args.refCorr else "W"
 
-    refcorr = ref[proc][corr_name(args.refCorr) + "_minnlo_ratio"]
+    refcorr_name = find_corr_hist_name(ref, proc, args.refCorr, "minnlo_ratio")
+    refcorr = ref[proc][refcorr_name]
 
     rescale_corr_name = (
-        f"{corr_name(args.rescaleCorr)}_minnlo_ratio"
+        find_corr_hist_name(rescale, proc, args.rescaleCorr, "minnlo_ratio")
         if "dataPtll" not in args.rescaleCorr
         else "MC_data_ratio"
     )
@@ -82,7 +102,7 @@ def main():
     # Broadcast syst axis
     new_corr[...] = (refcorr.view().T * central_val_corr.T).T
 
-    new_name = args.newCorrName + "_minnlo_ratio"
+    new_name = f"{args.newCorrName.rstrip('_')}_minnlo_ratio"
     if "dataPtll" in args.newCorrName:
         new_name = "MC_data_ratio"
         # Avoid overwriting

scripts/rabbit/setupRabbit.py

@@ -724,6 +724,12 @@ def make_parser(parser=None, argv=None):
         type=float,
         help="Scale the minnlo qcd scale uncertainties by this factor",
     )
+    parser.add_argument(
+        "--scaleNPLambda4",
+        default=1.0,
+        type=float,
+        help="Scale the nonperturbative lambda4 uncertainty by this factor",
+    )
     parser.add_argument(
         "--symmetrizeTheoryUnc",
         default="quadratic",
@@ -1772,6 +1778,7 @@ def setup(
             pdf_from_corr=args.pdfUncFromCorr,
             as_from_corr=not args.asUncFromUncorr,
             scale_pdf_unc=args.scalePdf,
+            scale_np_lambda4=args.scaleNPLambda4,
             samples=theorySystSamples,
             minnlo_unc=args.minnloScaleUnc,
             minnlo_scale=args.scaleMinnloScale,

wremnants/postprocessing/rabbit_theory_helper.py

@@ -81,6 +81,7 @@ def __init__(self, label, datagroups, args, hasNonsigSamples=False):
         self.np_model = "Delta_Lambda"
         self.pdf_from_corr = False
         self.scale_pdf_unc = -1.0
+        self.scale_np_lambda4 = 1.0
         self.mirror_tnp = True
         self.minnlo_unc = "byHelicityPt"
         self.helicity_fit_unc = False
@@ -112,6 +113,7 @@ def configure(
         pdf_operation=None,
         samples=[],
         scale_pdf_unc=-1.0,
+        scale_np_lambda4=1.0,
         minnlo_unc="byHelicityPt",
         minnlo_scale=1.0,
         from_hels=False,
@@ -131,6 +133,7 @@ def configure(
         self.as_from_corr = pdf_from_corr or as_from_corr
         self.pdf_operation = pdf_operation
         self.scale_pdf_unc = scale_pdf_unc
+        self.scale_np_lambda4 = scale_np_lambda4
         self.samples = samples
         self.helicity_fit_unc = False
         self.minnlo_scale = minnlo_scale
@@ -824,7 +827,7 @@ def add_correlated_np_uncertainties(self):
             np_map = {
                 "lambda2": ["0.0", "0.5"],
                 "delta_lambda2": ["-0.02", "0.02"],
-                "lambda4": ["0.01", "0.16"],
+                "lambda4": ["0.01", "0.12"],
             }
         elif "Lambda" in self.np_model:
             np_map = {
@@ -860,6 +863,7 @@ def operation(h, entries):
         for nuisance, vals in np_map.items():
             entries = [nuisance + v for v in vals]
             rename = f"scetlibNP{nuisance}"
+            scale = self.scale_np_lambda4 if nuisance.lower() == "lambda4" else 1.0
             # operation = lambda h : h[{self.syst_ax : entries}]
             self.datagroups.addSystematic(
                 self.corr_hist_name,
@@ -870,6 +874,7 @@ def operation(h, entries):
                 preOp=operation,
                 preOpArgs=dict(entries=entries),
                 outNames=[f"{rename}Down", f"{rename}Up"],
+                scale=scale,
                 name=rename,
             )
 
@@ -878,7 +883,7 @@ def add_uncorrelated_np_uncertainties(self):
             np_map = {
                 "lambda2": ["0.0", "0.5"],
                 "delta_lambda2": ["-0.02", "0.02"],
-                "lambda4": ["0.01", "0.16"],
+                "lambda4": ["0.01", "0.12"],
             }
         elif "Lambda" in self.np_model:
             np_map = {
@@ -945,6 +950,7 @@ def add_uncorrelated_np_uncertainties(self):
             for nuisance, vals in np_map.items():
                 entries = [nuisance + v for v in vals]
                 rename = f"scetlibNP{label}{nuisance}"
+                scale = self.scale_np_lambda4 if nuisance.lower() == "lambda4" else 1.0
                 self.datagroups.addSystematic(
                     self.np_hist_name,
                     processes=[sample_group],
@@ -965,6 +971,7 @@ def add_uncorrelated_np_uncertainties(self):
                         (entries[0], f"{rename}Down"),
                     ],
                     skipEntries=[{self.syst_ax: ["central", "pdf0"]}],
+                    scale=scale,
                     name=rename,
                 )
 

wremnants/production/theory_corrections.py

@@ -95,6 +95,12 @@ def make_theory_corr_weight_info(pdf, *, alphas=False, renorm=False):
     "scetlib_dyturbo_LatticeNP_CT18Z_N4p0LL_N2LO_pdfas": make_theory_corr_weight_info(
         "ct18z", alphas=True, renorm=True
     ),
+    "scetlib_nnlojet_LatticeNPCoarse_CT18Z_N3p1LL_N3LO_pdfas": make_theory_corr_weight_info(
+        "ct18z", alphas=True, renorm=True
+    ),
+    "scetlib_nnlojet_LatticeNPCoarse_CT18Z_N4p0LL_N3LO_pdfas": make_theory_corr_weight_info(
+        "ct18z", alphas=True, renorm=True
+    ),
     "scetlib_dyturbo_LatticeNP_CT18_N3p0LL_N2LO_pdfas": make_theory_corr_weight_info(
         "ct18", alphas=True, renorm=True
     ),
@@ -128,6 +134,12 @@ def make_theory_corr_weight_info(pdf, *, alphas=False, renorm=False):
     "scetlib_dyturbo_LatticeNP_CT18Z_N4p0LL_N2LO_pdfvars": make_theory_corr_weight_info(
         "ct18z"
     ),
+    "scetlib_nnlojet_LatticeNPCoarse_CT18Z_N3p1LL_N3LO_pdfvars": make_theory_corr_weight_info(
+        "ct18z"
+    ),
+    "scetlib_nnlojet_LatticeNPCoarse_CT18Z_N4p0LL_N3LO_pdfvars": make_theory_corr_weight_info(
+        "ct18z"
+    ),
     "scetlib_dyturbo_LatticeNP_CT18_N3p0LL_N2LO_pdfvars": make_theory_corr_weight_info(
         "ct18"
     ),
@@ -199,6 +211,7 @@ def load_corr_helpers(
                 (generator == generators[0])
                 and ("nnlojet" in generator.lower())
                 and ("pdfas" not in generator.lower())
+                and ("pdfvars" not in generator.lower())
             ):
                 logger.info(
                     f"Adding statistical uncertainties for correction {generator}"

wremnants/utilities/io_tools/input_tools.py

@@ -275,13 +275,32 @@ def read_nnlojet_file(
     return h * 1e-3
 
 
-def read_nnlojet_ybin(refname, ybins, charge=None):
+def resolve_nnlojet_ybin_filename(refname, ybins):
     format_decimal = lambda x: (
         "0" if x == 0 else f"{round(x, 1+(x % 1 in [0.25, 0.75]))}".replace(".", "p")
     )
+
+    def alternate_decimal(value):
+        formatted = format_decimal(value)
+        return formatted[:-1] if formatted.endswith("p0") else formatted
+
+    bounds = tuple(format_decimal(y) for y in ybins)
+    candidates = [
+        f"{refname}__{bounds[0]}__{bounds[1]}.dat",
+        f"{refname}__{alternate_decimal(ybins[0])}__{bounds[1]}.dat",
+        f"{refname}__{bounds[0]}__{alternate_decimal(ybins[1])}.dat",
+        f"{refname}__{alternate_decimal(ybins[0])}__{alternate_decimal(ybins[1])}.dat",
+    ]
+    for candidate in dict.fromkeys(candidates):
+        if os.path.isfile(candidate):
+            return candidate
+    return candidates[0]
+
+
+def read_nnlojet_ybin(refname, ybins, charge=None):
     yax = hist.axis.Variable(ybins, name="Y")
     return read_nnlojet_file(
-        f"{refname}__{format_decimal(ybins[0])}__{format_decimal(ybins[1])}.dat",
+        resolve_nnlojet_ybin_filename(refname, ybins),
         other_axes=[yax],
         charge=charge,
     )