Polarised proton fits

n3fit/runcards/examples/nnpdfpol20_dis_nnlo.yaml

@@ -0,0 +1,105 @@
+############################################################
+# Configuration file for N3FIT
+############################################################
+description: NNPDFpol2.0 NNLO DIS fit with ID822 (W2min=4)
+
+############################################################
+dataset_inputs:
+# -------------- Superseeded by COMPASS15 ---------------------
+# - {dataset: COMPASS_NC_17GEV_MUP_G1, frac: 0.60, cfac: [NRM]}
+# - {dataset: COMPASS_NC_25GEV_MUD_G1, frac: 0.60, cfac: [NRM]}
+# -------------------------------------------------------------
+- {dataset: COMPASS15_NC_17GEV_MUP_G1, frac: 0.60, cfac: [NRM]}
+- {dataset: COMPASS15_NC_25GEV_MUD_G1, frac: 0.60, cfac: [NRM]}
+- {dataset: E142_NC_58GEV_EN_G1, frac: 0.60, cfac: [NRM]}
+- {dataset: E143_NC_4GEV_EP_G1, frac: 0.60, cfac: [NRM]}
+- {dataset: E143_NC_8GEV_ED_G1, frac: 0.60, cfac: [NRM]}
+- {dataset: E154_NC_9GEV_EN_G1, frac: 0.60, cfac: [NRM]}
+- {dataset: E155_NC_9GEV_EN_G1F1RATIO, frac: 0.60}
+- {dataset: E155_NC_9GEV_EP_G1F1RATIO, frac: 0.60}
+- {dataset: EMC_NC_16GEV_MUP_G1, frac: 0.60, cfac: [NRM]}
+- {dataset: HERMES_NC_7GEV_ED_G1, frac: 0.60, cfac: [NRM]}
+- {dataset: HERMES_NC_7GEV_EP_G1, frac: 0.60, cfac: [NRM]}
+# ------------- Same as P+N due to Isospin --------------------
+# - {dataset: HERMES_NC_7GEV_EN_G1, frac: 0.60, cfac: [NRM]}
+# -------------------------------------------------------------
+- {dataset: HERMES97_NC_7GEV_EN_G1, frac: 0.60, cfac: [NRM]}
+- {dataset: JLABE06_NC_3GEV_EN_G1F1RATIO, frac: 0.60}
+- {dataset: JLABEG1B_NC_3GEV_EP_G1F1RATIO, frac: 0.60}
+- {dataset: JLABEG1B_NC_4GEV_ED_G1F1RATIO, frac: 0.60}
+- {dataset: JLABE97_NC_3GEV_EN_G1, frac: 0.60, cfac: [NRM]}
+- {dataset: JLABE99_NC_3GEV_EN_G1F1RATIO, frac: 0.60}
+- {dataset: JLABEG1DVCS_NC_5GEV_ED_G1F1RATIO, frac: 0.60}
+- {dataset: JLABEG1DVCS_NC_3GEV_EP_G1F1RATIO, frac: 0.60}
+- {dataset: SMC_NC_24GEV_MUD_G1, frac: 0.60, cfac: [NRM]}
+- {dataset: SMC_NC_17GEV_MUP_G1, frac: 0.60, cfac: [NRM]}
+- {dataset: SMCSX_NC_24GEV_MUD_G1F1RATIO, frac: 0.60}
+- {dataset: SMCSX_NC_17GEV_MUP_G1F1RATIO, frac: 0.60}
+
+############################################################
+datacuts:
+  t0pdfset: NNPDFpol10_100   # PDF set to generate t0 covmat
+  q2min: 1.00                # Q2 minimum
+  w2min: 4.00                # W2 minimum
+
+unpolpdf: NNPDF40_nnlo_pch_as_01180
+
+############################################################
+theory:
+  theoryid: 822
+
+############################################################
+trvlseed: 14613416
+nnseed: 1865683875
+mcseed: 746898422
+save: false
+genrep: true
+
+parameters:
+  nodes_per_layer: [25, 20, 4]
+  activation_per_layer: [tanh, tanh, linear]
+  initializer: glorot_normal
+  optimizer:
+    clipnorm: 6.073e-6
+    learning_rate: 2.621e-3
+    optimizer_name: Nadam
+  epochs: 20000
+  positivity:
+    initial: 1
+    multiplier:
+    threshold: 1e-7
+  integrability:
+    initial: 1
+    multiplier:
+    threshold: 1e-2
+  stopping_patience: 0.1
+  layer_type: dense
+  dropout: 0.0
+  threshold_chi2: 3.5
+
+fitting:
+  fitbasis: EVOL_POL
+  sum_rules: TSR
+  basis:
+  - {fl: sng, trainable: false, smallx: [1.094, 1.118], largex: [1.46, 3.003]}
+  - {fl: g, trainable: false, smallx: [0.8189, 1.844], largex: [2.591, 5.697]}
+  - {fl: t3, trainable: false, smallx: [-0.4401, 0.9163], largex: [1.773, 3.333]}
+  - {fl: t8, trainable: false, smallx: [0.5852, 0.8537], largex: [1.533, 3.436]}
+
+###########################################################
+positivity:
+  posdatasets:
+  - {dataset: NNPDF_POS_5GEV_XDELTAG, maxlambda: 1e8}
+  - {dataset: NNPDF_POS_5GEV_XDELTADQ, maxlambda: 1e8}
+  - {dataset: NNPDF_POS_5GEV_XDELTASQ, maxlambda: 1e8}
+  - {dataset: NNPDF_POS_5GEV_XDELTAUQ, maxlambda: 1e8}
+
+############################################################
+integrability:
+  integdatasets:
+  - {dataset: NNPDF_INTEG_1GEV_XDELTAG, maxlambda: 1e2}
+  - {dataset: NNPDF_INTEG_1GEV_XDELTASIGMA, maxlambda: 1e2}
+
+############################################################
+debug: false
+maxcores: 4

n3fit/src/n3fit/backends/keras_backend/operations.py

@@ -315,6 +315,21 @@ def pow(tensor, power):
     return tf.pow(tensor, power)
 
 
+@tf.function
+def absolute(tensor):
+    """
+    Compute the absolute value of a tensor
+    """
+    return K.abs(tensor)
+
+
+def multiply_minusone(tensor):
+    """
+    Multiply the elements of a given tensor by (-1)
+    """
+    return keras_Lambda(lambda x: -1 * x)(tensor)
+
+
 @tf.function(reduce_retracing=True)
 def op_log(o_tensor, **kwargs):
     """

n3fit/src/n3fit/checks.py

@@ -5,12 +5,9 @@
 import numbers
 import os
 
-import numpy as np
-
 from n3fit.hyper_optimization import penalties as penalties_module
 from n3fit.hyper_optimization import rewards as rewards_module
 from reportengine.checks import CheckError, make_argcheck
-from validphys.core import PDF
 from validphys.pdfbases import check_basis
 
 log = logging.getLogger(__name__)
@@ -71,6 +68,15 @@ def check_stopping(parameters):
         raise CheckError(f"Needs to run at least 1 epoch, got: {epochs}")
 
 
+@make_argcheck
+def check_polarised(fitbasis, fitting):
+    """Checks that if the polarised basis is used, then the necessary entries
+    are specified correctly.
+    """
+    if "POL" in fitbasis and fitting.get("sum_rules") != "TSR":
+        raise CheckError("'sum_rules' needs to be 'TSR' for polarised fits.")
+
+
 def check_basis_with_layers(basis, parameters):
     """Check that the last layer matches the number of flavours defined in the runcard"""
     number_of_flavours = len(basis)
@@ -334,7 +340,7 @@ def check_sumrules(sum_rules):
     """Checks that the chosen option for the sum rules are sensible"""
     if isinstance(sum_rules, bool):
         return
-    accepted_options = ["ALL", "MSR", "VSR", "ALLBUTCSR"]
+    accepted_options = ["ALL", "MSR", "VSR", "TSR", "ALLBUTCSR"]
     if sum_rules.upper() in accepted_options:
         return
     raise CheckError(f"The only accepted options for the sum rules are: {accepted_options}")

n3fit/src/n3fit/layers/DIS.py

@@ -12,6 +12,9 @@
 
 from .observable import Observable
 
+POS_POLSD_IDEX = [0, 2]  # Polarised POS Datasets
+POS_UNPOL_IDEX = [1, 3]  # Unpolarised POS Datasets
+
 
 class DIS(Observable):
     """
@@ -64,16 +67,21 @@ def call(self, pdf):
             raise ValueError("DIS layer call with a dataset that needs more than one xgrid?")
 
         results = []
-        # Separate the two possible paths this layer can take
-        if self.many_masks:
-            for mask, fktable in zip(self.all_masks, self.fktables):
+        for idx, fktable in enumerate(self.fktables):
+            mask = self.all_masks[idx] if self.many_masks else self.all_masks[0]
+            if self.is_polarised_pos() and idx in POS_UNPOL_IDEX:
+                # Convolute the FK tables with the pre-computed Unpolarised PDFs
+                pdf_masked = op.boolean_mask(self.computed_pdfs[idx], mask, axis=3)
+            else:
                 pdf_masked = op.boolean_mask(pdf, mask, axis=3)
+
+            if self.is_polarised_pos() and idx in POS_POLSD_IDEX:
+                # Compute the absolute value of `x \Delta f(x)` and multiply with (-1)
                 res = op.tensor_product(pdf_masked, fktable, axes=[(2, 3), (2, 1)])
-                results.append(res)
-        else:
-            pdf_masked = op.boolean_mask(pdf, self.all_masks[0], axis=3)
-            for fktable in self.fktables:
+                res = op.multiply_minusone(op.absolute(res))
+            else:
                 res = op.tensor_product(pdf_masked, fktable, axes=[(2, 3), (2, 1)])
-                results.append(res)
+
+            results.append(res)
 
         return self.operation(results)

n3fit/src/n3fit/layers/losses.py

@@ -154,7 +154,7 @@ class LossPositivity(LossLagrange):
     True
     """
 
-    def __init__(self, alpha=1e-7, **kwargs):
+    def __init__(self, alpha=0.0, **kwargs):
         self.alpha = alpha
         super().__init__(**kwargs)
 

n3fit/src/n3fit/layers/msr_normalization.py

@@ -11,7 +11,22 @@
 from n3fit.backends import MetaLayer
 from n3fit.backends import operations as op
 
-IDX = {'photon': 0, 'sigma': 1, 'g': 2, 'v': 3, 'v3': 4, 'v8': 5, 'v15': 6, 'v24': 7, 'v35': 8}
+IDX = {
+    'photon': 0,
+    'sigma': 1,
+    'g': 2,
+    'v': 3,
+    'v3': 4,
+    'v8': 5,
+    'v15': 6,
+    'v24': 7,
+    'v35': 8,
+    't3': 9,
+    't8': 10,
+    't15': 11,
+    't24': 12,
+    't35': 13,
+}
 MSR_COMPONENTS = ['g']
 MSR_DENOMINATORS = {'g': 'g'}
 # The VSR normalization factor of component f is given by
@@ -20,12 +35,42 @@
 VSR_CONSTANTS = {'v': 3.0, 'v35': 3.0, 'v24': 3.0, 'v3': 1.0, 'v8': 3.0, 'v15': 3.0}
 VSR_DENOMINATORS = {'v': 'v', 'v35': 'v', 'v24': 'v', 'v3': 'v3', 'v8': 'v8', 'v15': 'v15'}
 
-CSR_COMPONENTS = ['v','v35','v24']
+CSR_COMPONENTS = ['v', 'v35', 'v24']
 CSR_DENOMINATORS = {'v': 'v', 'v35': 'v', 'v24': 'v'}
 NOV15_COMPONENTS = ['v3', 'v8']
 NOV15_CONSTANTS = {'v3': 1.0, 'v8': 3.0}
 NOV15_DENOMINATORS = {'v3': 'v3', 'v8': 'v8'}
 
+# The following lays out the SR for Polarised PDFs
+TSR_COMPONENTS = ['t3', 't8']
+TSR_DENOMINATORS = {'t3': 't3', 't8': 't8'}
+# Sum Rules defined as in PDG 2023
+TSR_CONSTANTS = {'t3': 1.2756, 't8': 0.5850}
+TSR_CONSTANTS_UNC = {'t3': 0.0013, 't8': 0.025}
+
+
+def sample_tsr(v: dict, e: dict, t: list, nr: int) -> list:
+    """
+    Sample the Triplets sum rules according to the PDG uncertainties.
+
+    Parameters
+    ----------
+    v: dict
+        dictionary that maps the triplet component to its PDG value
+    e: dict
+        dictionary that maps the triplet component to its denominator
+    t: list
+        list of triplet component for which SR should be applied
+    nr: int
+        number of replicas that are fitter simultaneously
+
+    Returns
+    -------
+    list:
+        list of sum rule values sampled according to a normal distribution
+    """
+    return [[np.random.normal(v[c], e[c]) for _ in range(nr)] for c in t]
+
 
 class MSR_Normalization(MetaLayer):
     """
@@ -34,7 +79,8 @@ class MSR_Normalization(MetaLayer):
 
     _msr_enabled = False
     _vsr_enabled = False
-    _csr_enabled = False 
+    _tsr_enabled = False
+    _csr_enabled = False
 
     def __init__(self, mode: str = "ALL", replicas: int = 1, **kwargs):
         if mode == True or mode.upper() == "ALL":
@@ -44,7 +90,8 @@ def __init__(self, mode: str = "ALL", replicas: int = 1, **kwargs):
             self._msr_enabled = True
         elif mode.upper() == "VSR":
             self._vsr_enabled = True
-
+        elif mode.upper() == "TSR":
+            self._tsr_enabled = True
         elif mode.upper() == "ALLBUTCSR":
             self._msr_enabled = True
             self._csr_enabled = True
@@ -63,6 +110,13 @@ def __init__(self, mode: str = "ALL", replicas: int = 1, **kwargs):
             self.vsr_factors = op.numpy_to_tensor(
                 [np.repeat(VSR_CONSTANTS[c], replicas) for c in VSR_COMPONENTS]
             )
+        if self._tsr_enabled:
+            self.divisor_indices += [IDX[TSR_DENOMINATORS[c]] for c in TSR_COMPONENTS]
+            indices += [IDX[c] for c in TSR_COMPONENTS]
+            self.tsr_factors = op.numpy_to_tensor(
+                sample_tsr(TSR_CONSTANTS, TSR_CONSTANTS_UNC, TSR_COMPONENTS, replicas)
+            )
+
         if self._csr_enabled:
             # modified vsr for V, V24, V35
             indices += [IDX[c] for c in CSR_COMPONENTS]
@@ -114,11 +168,13 @@ def call(self, pdf_integrated, photon_integral):
             ]
         if self._vsr_enabled:
             numerators += [self.vsr_factors]
+        if self._tsr_enabled:
+            numerators += [self.tsr_factors]
         if self._csr_enabled:
-            numerators += len(CSR_COMPONENTS)*[op.batchit(4.0 - 1./3. * y[IDX['v15']], batch_dimension=0)]
+            numerators += len(CSR_COMPONENTS) * [
+                op.batchit(4.0 - 1.0 / 3.0 * y[IDX['v15']], batch_dimension=0)
+            ]
             numerators += [self.vsr_factors]
-
-
 
         numerators = op.concatenate(numerators, axis=0)
         divisors = op.gather(y, self.divisor_indices, axis=0)

n3fit/src/n3fit/layers/observable.py

@@ -38,11 +38,27 @@ class Observable(MetaLayer, ABC):
             number of flavours in the pdf (default:14)
     """
 
-    def __init__(self, fktable_data, fktable_arr, operation_name, nfl=14, **kwargs):
+    def __init__(
+        self,
+        fktable_data,
+        fktable_arr,
+        dataset_name,
+        fitbasis,
+        extern_lhapdf,
+        operation_name,
+        n_replicas=1,
+        nfl=14,
+        **kwargs
+    ):
         super(MetaLayer, self).__init__(**kwargs)
 
+        self.dataset_name = dataset_name
         self.nfl = nfl
+        self.fitbasis = fitbasis
+        self.fktable_data = fktable_data
+        self.nfks = len(fktable_data)
 
+        self.computed_pdfs = []
         basis = []
         xgrids = []
         self.fktables = []
@@ -51,6 +67,12 @@ def __init__(self, fktable_data, fktable_arr, operation_name, nfl=14, **kwargs):
             basis.append(fkdata.luminosity_mapping)
             self.fktables.append(op.numpy_to_tensor(fk))
 
+            if self.is_polarised_pos():
+                resx = extern_lhapdf(fkdata.xgrid.tolist())
+                mult_resx = np.repeat([resx], n_replicas, axis=0)
+                resx = np.expand_dims(mult_resx, axis=0)
+                self.computed_pdfs.append(op.numpy_to_tensor(resx))
+
         # check how many xgrids this dataset needs
         if is_unique(xgrids):
             self.splitting = None
@@ -71,6 +93,12 @@ def __init__(self, fktable_data, fktable_arr, operation_name, nfl=14, **kwargs):
     def compute_output_shape(self, input_shape):
         return (self.output_dim, None)
 
+    def is_polarised_pos(self):
+        if "POL" in self.fitbasis and "_POS_" in self.dataset_name:
+            # Polarised POS contains at least 2 FK tables
+            return self.nfks >= 2
+        return False
+
     # Overridables
     @abstractmethod
     def gen_mask(self, basis):