Extend n3fit and vp to deal with two different initial state distributions

.github/workflows/fitbot.yml

@@ -10,7 +10,7 @@ on:
 env:
   N3FIT_MAXNREP: 20 # total number of replicas to fit
   POSTFIT_NREP: 16 # requested replicas for postfit
-  REFERENCE_SET: NNBOT-03c99fd73-2024-07-26 # reference set for exact results
+  REFERENCE_SET: NNBOT-06f20cf6a-2024-07-26 # reference set for exact results
   STABLE_REFERENCE_SET: NNBOT-c0f99b7b3-2024-02-28 # reference set for last tag
   CONDA_PY: 312
   PYTHONHASHSEED: "0"

doc/sphinx/source/tutorials/polarized_fits.rst

@@ -33,6 +33,42 @@ as a boundary condition while ``n_std`` specifies the shift in terms of the stan
 deviation to be applied to the PDF central values. If ``n_std=0.0`` then the
 PDF central values will be used to constrain their polarized counterparts.
 
+.. note::
+
+   This will also be the PDF set to be used to compute observable predictions
+   during the fit.
+
+Instead, the unpolarized PDF set to be used to compute the `t0` covariance matrix need to be specified under the ``datacuts`` entry:
+
+.. code-block:: yaml
+
+  ...
+  datacuts:
+    t0pdfset: NNPDFpol11_100   # PDF set to generate t0 covmat
+    unpolarized_bc: NNPDF40_nnlo_pch_as_01180
+    q2min: 1.00                # Q2 minimum
+    w2min: 4.00                # W2 minimum
+  ...
+
+And if a theory covariance matrix is included in the fit
+(see :ref:`how to include a theory covariance matrix in a fit <_thcov_tutorial>`
+for more details), then the unpolarized PDF set also needs to be specified in the
+``theorycovmatconfig`` together with the polarized PDF to be used:
+
+.. code-block:: yaml
+
+  # Define the unpolarized PDF set to be used to compute predictions with validphys actions
+  ...
+  theorycovmatconfig:
+    point_prescription: '7 point'
+    theoryids:
+      from_: scale_variation_theories
+    pdf: NNPDFpol11_100
+    unpolarized_bc: NNPDF40_nnlo_pch_as_01180
+    use_thcovmat_in_fitting: true
+    use_thcovmat_in_sampling: true
+  ...
+
 Given that polarized fits require different fitting bases and different theory
 constraints, the fields under the ``fitting`` key require some adjustments.
 Specifically:
@@ -41,7 +77,7 @@ Specifically:
 
   ...
   fitting:
-    fitbasis: POLARIZED_EVOL
+    fitbasis: POLARIZED_EVOL_CMP
     sum_rules: TSR
     basis:
     - {fl: sng, trainable: false, smallx: [1.094, 1.118], largex: [1.46, 3.003]}
@@ -89,8 +125,8 @@ and then followed by the basis type (for example ``EVOL`` or ``FLAVOUR``).
 
    where the subscript :math:`r` indicates that the random seed per replica is always
    different. In practice, when imposing the positivity at the level of PDFs, we enforce
-   the constraints on the flavor combination :math:`\left( \Delta f_i + \Delta \bar{f}_i \right)`,
-   that is :math:`(\Delta) \mathcal{F}_k \equiv (\Delta) f_k + (\Delta) \bar{f}_k`.
+   the constraints on the individual flavor :math:`\Delta f_i` (or respectively :math:`\Delta \bar{f}_i`),
+   that is :math:`(\Delta) \mathcal{F}_k \equiv (\Delta) f_k` (or respectively :math:`(\Delta) \mathcal{F}_k \equiv (\Delta) \bar{f}_k`).
 
 Once the runcard is ready, the user can follow the guidelines :ref:`here <run-n3fit-fits>`
 to set up and run fits.
@@ -106,8 +142,6 @@ can simply be done by supplementing a flag to the ``evolven3fit``:
 
   evolven3fit evolve $runcard_folder --use_polarized
 
-Alternatively, the user can explicitly specify the path to the EKO using the flag ``--load``.
-
 
 Comparing polarized fits
 ------------------------

extra_tests/regression_fits/central_16.json

@@ -51,45 +51,45 @@
   ],
   "stop_epoch": 1100,
   "best_epoch": 1099,
-  "erf_tr": 2.6700103282928467,
-  "erf_vl": 2.596097469329834,
-  "chi2": 2.1046993732452393,
+  "erf_tr": 2.6700093746185303,
+  "erf_vl": 2.5960960388183594,
+  "chi2": 2.1047041416168213,
   "pos_state": "POS_VETO",
   "arc_lengths": [
-    1.3207828111870983,
-    1.4752518408583124,
-    1.1834879869850787,
-    1.001142499763364,
-    1.1960909456851319
+    1.3207826202191888,
+    1.4752515128602395,
+    1.1834880064420275,
+    1.0011424664941873,
+    1.1960902816154753
   ],
   "integrability": [
-    0.000920317608690574,
-    0.0009203176086889364,
-    4.833287800831698e-05,
-    0.46257802098989376,
-    0.0069634128594759925
+    0.000920316459089382,
+    0.0009203164590875224,
+    4.833296725237357e-05,
+    0.46257925033569225,
+    0.0069633987150139465
   ],
   "timing": {
     "walltime": {
-      "Total": 10.047237396240234,
+      "Total": 10.90460753440857,
       "start": 0.0,
-      "replica_set": 0.210205078125,
-      "replica_fitted": 10.047173023223877,
-      "replica_set_to_replica_fitted": 9.836967945098877
+      "replica_set": 0.22848939895629883,
+      "replica_fitted": 10.904474258422852,
+      "replica_set_to_replica_fitted": 10.675984859466553
     },
     "cputime": {
-      "Total": 11.469310126,
+      "Total": 12.365805662,
       "start": 0.0,
-      "replica_set": 0.2088275399999997,
-      "replica_fitted": 11.469243942000002,
-      "replica_set_to_replica_fitted": 11.260416402
+      "replica_set": 0.22668013299999945,
+      "replica_fitted": 12.365671202000001,
+      "replica_set_to_replica_fitted": 12.138991069000003
     }
   },
   "version": {
     "keras": "3.4.1",
     "tensorflow": "2.17.0, mkl=False",
     "numpy": "1.26.4",
-    "nnpdf": "4.0.9.post1058.dev0+0a1b331ae",
-    "validphys": "4.0.9.post1058.dev0+0a1b331ae"
+    "nnpdf": "4.0.9.post1210.dev0+d36bcce78",
+    "validphys": "4.0.9.post1210.dev0+d36bcce78"
   }
 }