diff --git a/sbibm/tasks/__init__.py b/sbibm/tasks/__init__.py
index babbb6b7..4c79db0e 100644
--- a/sbibm/tasks/__init__.py
+++ b/sbibm/tasks/__init__.py
@@ -63,6 +63,11 @@ def get_task(task_name: str, *args: Any, **kwargs: Any) -> Task:
 
         return TwoMoons(*args, **kwargs)
 
+    elif task_name == "noref_beam":
+        from sbibm.tasks.noref_beam.task import NorefBeam
+
+        return NorefBeam(*args, **kwargs)
+
     else:
         raise NotImplementedError()
 
diff --git a/sbibm/tasks/noref_beam/__init__.py b/sbibm/tasks/noref_beam/__init__.py
new file mode 100644
index 00000000..e69de29b
diff --git a/sbibm/tasks/noref_beam/files/num_observation_1/observation.csv b/sbibm/tasks/noref_beam/files/num_observation_1/observation.csv
new file mode 100644
index 00000000..90e15808
--- /dev/null
+++ b/sbibm/tasks/noref_beam/files/num_observation_1/observation.csv
@@ -0,0 +1,2 @@
+data_1,data_2,data_3,data_4,data_5,data_6,data_7,data_8,data_9,data_10,data_11,data_12,data_13,data_14,data_15,data_16,data_17,data_18,data_19,data_20,data_21,data_22,data_23,data_24,data_25,data_26,data_27,data_28,data_29,data_30,data_31,data_32,data_33,data_34,data_35,data_36,data_37,data_38,data_39,data_40,data_41,data_42,data_43,data_44,data_45,data_46,data_47,data_48,data_49,data_50,data_51,data_52,data_53,data_54,data_55,data_56,data_57,data_58,data_59,data_60,data_61,data_62,data_63,data_64,data_65,data_66,data_67,data_68,data_69,data_70,data_71,data_72,data_73,data_74,data_75,data_76,data_77,data_78,data_79,data_80,data_81,data_82,data_83,data_84,data_85,data_86,data_87,data_88,data_89,data_90,data_91,data_92,data_93,data_94,data_95,data_96,data_97,data_98,data_99,data_100,data_101,data_102,data_103,data_104,data_105,data_106,data_107,data_108,data_109,data_110,data_111,data_112,data_113,data_114,data_115,data_116,data_117,data_118,data_119,data_120,data_121,data_122,data_123,data_124,data_125,data_126,data_127,data_128,data_129,data_130,data_131,data_132,data_133,data_134,data_135,data_136,data_137,data_138,data_139,data_140,data_141,data_142,data_143,data_144,data_145,data_146,data_147,data_148,data_149,data_150,data_151,data_152,data_153,data_154,data_155,data_156,data_157,data_158,data_159,data_160,data_161,data_162,data_163,data_164,data_165,data_166,data_167,data_168,data_169,data_170,data_171,data_172,data_173,data_174,data_175,data_176,data_177,data_178,data_179,data_180,data_181,data_182,data_183,data_184,data_185,data_186,data_187,data_188,data_189,data_190,data_191,data_192,data_193,data_194,data_195,data_196,data_197,data_198,data_199,data_200,data_201,data_202,data_203,data_204,data_205,data_206,data_207,data_208,data_209,data_210,data_211,data_212,data_213,data_214,data_215,data_216,data_217,data_218,data_219,data_220,data_221,data_222,data_223,data_224,data_225,data_226,data_227,data_228,data_229,data_230,data_231,data_232,data_233,data_234,data_235,data_236,data_237,data_238,data_239,data_240,data_241,data_242,data_243,data_244,data_245,data_246,data_247,data_248,data_249,data_250,data_251,data_252,data_253,data_254,data_255,data_256,data_257,data_258,data_259,data_260,data_261,data_262,data_263,data_264,data_265,data_266,data_267,data_268,data_269,data_270,data_271,data_272,data_273,data_274,data_275,data_276,data_277,data_278,data_279,data_280,data_281,data_282,data_283,data_284,data_285,data_286,data_287,data_288,data_289,data_290,data_291,data_292,data_293,data_294,data_295,data_296,data_297,data_298,data_299,data_300,data_301,data_302,data_303,data_304,data_305,data_306,data_307,data_308,data_309,data_310,data_311,data_312,data_313,data_314,data_315,data_316,data_317,data_318,data_319,data_320,data_321,data_322,data_323,data_324,data_325,data_326,data_327,data_328,data_329,data_330,data_331,data_332,data_333,data_334,data_335,data_336,data_337,data_338,data_339,data_340,data_341,data_342,data_343,data_344,data_345,data_346,data_347,data_348,data_349,data_350,data_351,data_352,data_353,data_354,data_355,data_356,data_357,data_358,data_359,data_360,data_361,data_362,data_363,data_364,data_365,data_366,data_367,data_368,data_369,data_370,data_371,data_372,data_373,data_374,data_375,data_376,data_377,data_378,data_379,data_380,data_381,data_382,data_383,data_384,data_385,data_386,data_387,data_388,data_389,data_390,data_391,data_392,data_393,data_394,data_395,data_396,data_397,data_398,data_399,data_400
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diff --git a/sbibm/tasks/noref_beam/files/num_observation_1/observation_seed.csv b/sbibm/tasks/noref_beam/files/num_observation_1/observation_seed.csv
new file mode 100644
index 00000000..254eecaa
--- /dev/null
+++ b/sbibm/tasks/noref_beam/files/num_observation_1/observation_seed.csv
@@ -0,0 +1,2 @@
+observation_seed,num_observation
+1000000,1
diff --git a/sbibm/tasks/noref_beam/files/num_observation_1/true_parameters.csv b/sbibm/tasks/noref_beam/files/num_observation_1/true_parameters.csv
new file mode 100644
index 00000000..f1015735
--- /dev/null
+++ b/sbibm/tasks/noref_beam/files/num_observation_1/true_parameters.csv
@@ -0,0 +1,2 @@
+parameter_1,parameter_2,parameter_3,parameter_4
+21.418789,45.55487,14.912247,12.066019
diff --git a/sbibm/tasks/noref_beam/files/num_observation_10/observation_seed.csv b/sbibm/tasks/noref_beam/files/num_observation_10/observation_seed.csv
new file mode 100644
index 00000000..29389840
--- /dev/null
+++ b/sbibm/tasks/noref_beam/files/num_observation_10/observation_seed.csv
@@ -0,0 +1,2 @@
+observation_seed,num_observation
+1000009,10
diff --git a/sbibm/tasks/noref_beam/files/num_observation_10/true_parameters.csv b/sbibm/tasks/noref_beam/files/num_observation_10/true_parameters.csv
new file mode 100644
index 00000000..7b91f861
--- /dev/null
+++ b/sbibm/tasks/noref_beam/files/num_observation_10/true_parameters.csv
@@ -0,0 +1,2 @@
+parameter_1,parameter_2,parameter_3,parameter_4
+71.7957,20.15931,11.602015,8.399567
diff --git a/sbibm/tasks/noref_beam/files/num_observation_2/observation.csv b/sbibm/tasks/noref_beam/files/num_observation_2/observation.csv
new file mode 100644
index 00000000..d301dddf
--- /dev/null
+++ b/sbibm/tasks/noref_beam/files/num_observation_2/observation.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/noref_beam/files/num_observation_2/observation_seed.csv b/sbibm/tasks/noref_beam/files/num_observation_2/observation_seed.csv
new file mode 100644
index 00000000..828175ed
--- /dev/null
+++ b/sbibm/tasks/noref_beam/files/num_observation_2/observation_seed.csv
@@ -0,0 +1,2 @@
+observation_seed,num_observation
+1000001,2
diff --git a/sbibm/tasks/noref_beam/files/num_observation_2/true_parameters.csv b/sbibm/tasks/noref_beam/files/num_observation_2/true_parameters.csv
new file mode 100644
index 00000000..fea8f067
--- /dev/null
+++ b/sbibm/tasks/noref_beam/files/num_observation_2/true_parameters.csv
@@ -0,0 +1,2 @@
+parameter_1,parameter_2,parameter_3,parameter_4
+41.330093,23.721622,14.767863,6.902104
diff --git a/sbibm/tasks/noref_beam/files/num_observation_3/observation.csv b/sbibm/tasks/noref_beam/files/num_observation_3/observation.csv
new file mode 100644
index 00000000..788a33cc
--- /dev/null
+++ b/sbibm/tasks/noref_beam/files/num_observation_3/observation.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/noref_beam/files/num_observation_3/observation_seed.csv b/sbibm/tasks/noref_beam/files/num_observation_3/observation_seed.csv
new file mode 100644
index 00000000..365a9526
--- /dev/null
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diff --git a/sbibm/tasks/noref_beam/files/num_observation_3/true_parameters.csv b/sbibm/tasks/noref_beam/files/num_observation_3/true_parameters.csv
new file mode 100644
index 00000000..fdb57137
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diff --git a/sbibm/tasks/noref_beam/files/num_observation_4/observation.csv b/sbibm/tasks/noref_beam/files/num_observation_4/observation.csv
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index 00000000..b2149fc0
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new file mode 100644
index 00000000..8081e389
--- /dev/null
+++ b/sbibm/tasks/noref_beam/files/num_observation_4/observation_seed.csv
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new file mode 100644
index 00000000..447673a1
--- /dev/null
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new file mode 100644
index 00000000..bcf964bd
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diff --git a/sbibm/tasks/noref_beam/files/num_observation_5/observation_seed.csv b/sbibm/tasks/noref_beam/files/num_observation_5/observation_seed.csv
new file mode 100644
index 00000000..b8b8507b
--- /dev/null
+++ b/sbibm/tasks/noref_beam/files/num_observation_5/observation_seed.csv
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diff --git a/sbibm/tasks/noref_beam/files/num_observation_5/true_parameters.csv b/sbibm/tasks/noref_beam/files/num_observation_5/true_parameters.csv
new file mode 100644
index 00000000..0fe8cf8a
--- /dev/null
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diff --git a/sbibm/tasks/noref_beam/files/num_observation_6/observation.csv b/sbibm/tasks/noref_beam/files/num_observation_6/observation.csv
new file mode 100644
index 00000000..ea8fea5c
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new file mode 100644
index 00000000..c651be0f
--- /dev/null
+++ b/sbibm/tasks/noref_beam/files/num_observation_6/observation_seed.csv
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diff --git a/sbibm/tasks/noref_beam/files/num_observation_6/true_parameters.csv b/sbibm/tasks/noref_beam/files/num_observation_6/true_parameters.csv
new file mode 100644
index 00000000..a6aaaa79
--- /dev/null
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diff --git a/sbibm/tasks/noref_beam/files/num_observation_7/observation.csv b/sbibm/tasks/noref_beam/files/num_observation_7/observation.csv
new file mode 100644
index 00000000..51bff129
--- /dev/null
+++ b/sbibm/tasks/noref_beam/files/num_observation_7/observation.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/noref_beam/files/num_observation_7/observation_seed.csv b/sbibm/tasks/noref_beam/files/num_observation_7/observation_seed.csv
new file mode 100644
index 00000000..53f81265
--- /dev/null
+++ b/sbibm/tasks/noref_beam/files/num_observation_7/observation_seed.csv
@@ -0,0 +1,2 @@
+observation_seed,num_observation
+1000010,7
diff --git a/sbibm/tasks/noref_beam/files/num_observation_7/true_parameters.csv b/sbibm/tasks/noref_beam/files/num_observation_7/true_parameters.csv
new file mode 100644
index 00000000..5dcb8efb
--- /dev/null
+++ b/sbibm/tasks/noref_beam/files/num_observation_7/true_parameters.csv
@@ -0,0 +1,2 @@
+parameter_1,parameter_2,parameter_3,parameter_4
+79.77455,34.36153,5.7742496,10.748118
diff --git a/sbibm/tasks/noref_beam/files/num_observation_8/observation.csv b/sbibm/tasks/noref_beam/files/num_observation_8/observation.csv
new file mode 100644
index 00000000..b6532613
--- /dev/null
+++ b/sbibm/tasks/noref_beam/files/num_observation_8/observation.csv
@@ -0,0 +1,2 @@
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diff --git a/sbibm/tasks/noref_beam/files/num_observation_8/observation_seed.csv b/sbibm/tasks/noref_beam/files/num_observation_8/observation_seed.csv
new file mode 100644
index 00000000..f8295d62
--- /dev/null
+++ b/sbibm/tasks/noref_beam/files/num_observation_8/observation_seed.csv
@@ -0,0 +1,2 @@
+observation_seed,num_observation
+1000012,8
diff --git a/sbibm/tasks/noref_beam/files/num_observation_8/true_parameters.csv b/sbibm/tasks/noref_beam/files/num_observation_8/true_parameters.csv
new file mode 100644
index 00000000..cb388c4d
--- /dev/null
+++ b/sbibm/tasks/noref_beam/files/num_observation_8/true_parameters.csv
@@ -0,0 +1,2 @@
+parameter_1,parameter_2,parameter_3,parameter_4
+21.618145,25.919987,14.983539,8.789967
diff --git a/sbibm/tasks/noref_beam/files/num_observation_9/observation_seed.csv b/sbibm/tasks/noref_beam/files/num_observation_9/observation_seed.csv
new file mode 100644
index 00000000..85e191b8
--- /dev/null
+++ b/sbibm/tasks/noref_beam/files/num_observation_9/observation_seed.csv
@@ -0,0 +1,2 @@
+observation_seed,num_observation
+1000008,9
diff --git a/sbibm/tasks/noref_beam/files/num_observation_9/true_parameters.csv b/sbibm/tasks/noref_beam/files/num_observation_9/true_parameters.csv
new file mode 100644
index 00000000..478411e4
--- /dev/null
+++ b/sbibm/tasks/noref_beam/files/num_observation_9/true_parameters.csv
@@ -0,0 +1,2 @@
+parameter_1,parameter_2,parameter_3,parameter_4
+70.3257,50.39463,7.1057105,13.09858
diff --git a/sbibm/tasks/noref_beam/task.py b/sbibm/tasks/noref_beam/task.py
new file mode 100644
index 00000000..558cdc9e
--- /dev/null
+++ b/sbibm/tasks/noref_beam/task.py
@@ -0,0 +1,301 @@
+from pathlib import Path
+from typing import Any, Callable, Dict, Optional
+
+import pyro
+import torch
+from pyro import distributions as pdist
+
+from sbibm import get_logger
+from sbibm.tasks.simulator import Simulator
+from sbibm.tasks.task import Task
+from sbibm.utils.io import get_tensor_from_csv, save_tensor_to_csv
+
+
+def torch_average(a, weights=None, axis=0):
+    """
+    emulates np.average interface minimally for pytorch
+    (see
+    https://numpy.org/doc/stable/reference/generated/numpy.average.html#numpy-average)
+
+    Args:
+        a : array/tensor to containing data to average
+        weights : An array of weights associated with the values in a. Each value in a contributes to the average according to its associated weight.
+        axis : Axis or axes along which to average a. The default, axis=0.
+    """
+
+    if isinstance(weights, type(None)):
+        return a.mean(axis=axis)
+    else:
+        assert weights.sum() > 0, f"received all 0 weights tensor"
+        value = torch.sum(a * weights, axis=axis) / torch.sum(weights, axis=axis)
+        return value
+
+
+def base_coordinate_field(min_axis=-16, max_axis=16):
+    """returns a torch tensor that contains the coordinates of a regular
+    grid between <min_axis> and <max_axis> broadcasted/cloned
+    <batchsize> times, i.e.
+
+    >>> arr = quadratic_coordinate_field(-3,3)
+    >>> arr.shape
+    (6,6,2)
+     ^^^---- dimensions of max_axis - min_axis, 3-(-3)
+    """
+    size_axis = max_axis - min_axis
+
+    x = torch.arange(min_axis, max_axis).detach().float()
+    y = torch.arange(min_axis, max_axis).detach().float()
+
+    xx, yy = torch.meshgrid(x, y)
+    val = torch.swapaxes(torch.stack((xx.flatten(), yy.flatten())), 1, 0).float()
+
+    value = val.reshape(size_axis, size_axis, 2)
+
+    return value
+
+
+def bcast_coordinate_field(base_field, num_samples):
+    """utility function that replicates the torch.Tensor <base_field> by <num_samples>
+    and moves the last axis to the front
+
+    example:
+
+    >>> arr = torch.from_numpy([[1,2,3],[4,5,6]])
+    >>> arr.shape
+    (2,3)
+    >>> barr = bcast_coordinate_field(arr, 4)
+    >>> barr.shape
+    (3,2,4)
+
+    Args:
+        base_field: the torch tensor to replicate
+        num_samples: number of replicates to produce
+    """
+    # boadcast to <num_samples> doublicates
+    valr_ = torch.broadcast_to(base_field, (num_samples, *base_field.shape)).detach()
+
+    # move axis from position 2 to front
+    value = torch.swapaxes(valr_, 2, 0)
+
+    return value
+
+
+def quadratic_coordinate_field(min_axis=-16, max_axis=16, batch_size=32):
+    """returns a torch tensor that contains the coordinates of a regular
+    grid between <min_axis> and <max_axis> broadcasted/cloned
+    <batchsize> times, i.e.
+    >>> arr = quadratic_coordinate_field(-3,3,4)
+    >>> arr.shape
+    #    --- batch_size
+    #    v
+    (6,6,4,2)
+    #^ ^
+    #| |
+    #------- dimensions of max_axis - min_axis, 3-(-3)
+
+    given size_axis=max_axis-min_axis, at every point of the image width=size_axis times height=size_axis
+    we store the (x,y) coordinate of a regular grid
+    so we get:
+    valr[0,0] = (-16,-16),
+    valr[0,1] = (-16,-15),
+    valr[0,2] = (-16,-14)
+
+    Args:
+        min_axis: minimum extent of coordinate field
+        max_axis: minimum extent of coordinate field
+        batch_size: number of replicas to produce
+    """
+
+    valr = base_coordinate_field(min_axis, max_axis)
+
+    # broadcast to <batchsize> doublIcates
+    valr_ = torch.broadcast_to(valr, (batch_size, *valr.shape)).detach()
+
+    # move axis from position 2 to front
+    value = torch.swapaxes(valr_, 2, 0)
+
+    return value
+
+
+class NorefBeam(Task):
+    def __init__(
+        self, min_axis: int = 0, max_axis: int = 200, flood_samples: int = 1 * 1024
+    ):
+        """Forward-only simulator (without a reference posterior)
+
+        Inference the parameters of a 2D multivariate normal
+        distribution from it's projections onto x and y only
+        (surrogate model for a accelerator physics application)
+
+        Args:
+            min_axis: minimum extent of the multivariate normal
+            max_axis: minimum extent of the multivariate normal
+            flood_samples: number of draws of the binomial wrapping the
+        multivariate normal distribution
+        """
+
+        self.min_axis = min_axis
+        self.max_axis = max_axis
+        self.flood_samples = flood_samples
+        dim_data = 2 * self.max_axis
+        name_display = "noref_beam"
+
+        # Observation seeds to use when generating ground truth
+        # used to generate the frozen observations (only done once)
+        # in case we were to regenerate them
+        observation_seeds = [
+            1000000,  # observation 1
+            1000001,  # observation 2
+            1000002,  # observation 3
+            1000003,  # observation 4
+            1000004,  # observation 5
+            1000005,  # observation 6
+            1000010,  # observation 7
+            1000012,  # observation 8
+            1000008,  # observation 9
+            1000009,  # observation 10
+        ]
+
+        super().__init__(
+            dim_parameters=4,
+            dim_data=dim_data,
+            name=Path(__file__).parent.name,
+            name_display=name_display,
+            num_observations=10,
+            num_posterior_samples=10000,
+            num_reference_posterior_samples=10000,
+            num_simulations=[1000, 10000, 100000, 1000000],
+            path=Path(__file__).parent.absolute(),
+            observation_seeds=observation_seeds,
+        )
+
+        self.prior_params = {
+            "low": torch.tensor([20, 20, 5, 5]).float(),
+            "high": torch.tensor([80, 80, 15, 15]).float(),
+        }
+        self.prior_dist = pdist.Uniform(**self.prior_params).to_event(1)
+
+        self.base_coordinate_field = base_coordinate_field(
+            self.min_axis, self.max_axis
+        ).detach()
+
+    def get_prior(self) -> Callable:
+        def prior(num_samples: int = 1):
+            return pyro.sample("parameters", self.prior_dist.expand_by([num_samples]))
+
+        return prior
+
+    def get_simulator(self, max_calls: Optional[int] = None) -> Simulator:
+        """Get function returning samples from simulator given parameters
+
+        Args:
+            max_calls: Maximum number of function calls. Additional calls will
+                result in SimulationBudgetExceeded exceptions. Defaults to None
+                for infinite budget
+
+        Return:
+            Simulator callable
+        """
+
+        def simulator(parameters):
+            """
+            Args:
+                parameters: theta parameters coming in (can be batched)
+            """
+            num_samples = parameters.shape[0]
+
+            m_ = torch.stack(
+                (parameters[:, [0]].squeeze(), parameters[:, [1]].squeeze())
+            ).T
+            if m_.dim() == 1:
+                m_.unsqueeze_(0)
+
+            m = torch.broadcast_to(m_, (self.max_axis, self.max_axis, *m_.shape))
+
+            s1 = parameters[:, [2]].squeeze()  # ** 2
+            s2 = parameters[:, [3]].squeeze()  # ** 2
+
+            # Note: checking the covariance_matrix for valid inputs
+            #       (being positive semidefinite) is expense, so
+            #       `S` needs to be PSD compliant
+            #       for the future: consider rotating img for more variability
+            S = torch.empty((self.max_axis, self.max_axis, num_samples, 2, 2))
+            S[..., 0, 0] = s1 ** 2
+            S[..., 0, 1] = s1 * s2
+            S[..., 1, 0] = 0.0  # s1 * s2
+            S[..., 1, 1] = s2 ** 2
+
+            # Add eps to diagonal to ensure PSD
+            eps = 0.000001
+            S[..., 0, 0] += eps
+            S[..., 1, 1] += eps
+
+            assert S.shape == (
+                self.max_axis,
+                self.max_axis,
+                num_samples,
+                2,
+                2,
+            ), f"{name_display} :: cov matrix {S.shape} != expectation"
+            assert m.shape == (
+                self.max_axis,
+                self.max_axis,
+                num_samples,
+                2,
+            ), f"{name_display} :: mean vector {m.shape} != expectation"
+
+            # define the probility distribution of our beamspot
+            # on a 2D grid (in batches)
+            data_dist = pdist.MultivariateNormal(
+                m.float(),
+                S.float(),
+                # `S` is constructed positive semidefinite
+                # validation is expensive
+                validate_args=False,
+            )
+
+            valb = bcast_coordinate_field(
+                self.base_coordinate_field, num_samples
+            ).detach()
+
+            # create images from log probabilities
+            img = torch.exp(data_dist.log_prob(valb)).detach()
+
+            # sample through binomial with fixed prob map
+            bdist = pdist.Binomial(total_count=self.flood_samples, probs=img)
+
+            # TODO: should this be a pyro.sample call?
+            samples = pyro.sample("data", bdist)
+
+            # project on the axes
+            first = torch.sum(samples, axis=0)
+            second = torch.sum(samples, axis=1)
+
+            # concatenate and return
+            return torch.cat([first, second], axis=-1)
+
+        return Simulator(task=self, simulator=simulator, max_calls=max_calls)
+
+
+if __name__ == "__main__":
+
+    log = get_logger(__file__)
+    log.warning(
+        "[noref_beam] producing observations may result in errors/exceptions thrown!"
+    )
+    ## run this to generate the `files` infrastructure in this folder
+    ## repo/sbibm/sbibm/tasks/noref_beam/files
+    ## ├── num_observation_1
+    ## ├── num_observation_10
+    ## ├── num_observation_2
+    ## ├── num_observation_3
+    ## ├── num_observation_4
+    ## ├── num_observation_5
+    ## ├── num_observation_6
+    ## ├── num_observation_7
+    ## ├── num_observation_8
+    ## └── num_observation_9
+    task = noref_beam()
+
+    task._setup()
+    ## note: the folders mentioned above
diff --git a/sbibm/tasks/task.py b/sbibm/tasks/task.py
index 6f80c074..93c818cd 100644
--- a/sbibm/tasks/task.py
+++ b/sbibm/tasks/task.py
@@ -2,6 +2,7 @@
 from pathlib import Path
 from typing import Any, Callable, Dict, List, Optional, Union
 
+import logging
 import numpy as np
 import pandas as pd
 import pyro
@@ -362,7 +363,12 @@ def _sample_reference_posterior(
         Returns:
             Samples from reference posterior
         """
-        raise NotImplementedError
+        log = logging.getLogger(__name__)
+        log.warning(
+            f"_sample_reference_posterior not implemented, some metrics and benchmarks will fail"
+        )
+        pass
+        #raise NotImplementedError
 
     def _save_observation_seed(self, num_observation: int, observation_seed: int):
         """Save observation seed for a given observation number"""
diff --git a/tests/tasks/test_noref_beam.py b/tests/tasks/test_noref_beam.py
new file mode 100644
index 00000000..ec1ae864
--- /dev/null
+++ b/tests/tasks/test_noref_beam.py
@@ -0,0 +1,410 @@
+import numpy as np
+import pytest
+import torch
+from pyro import distributions as pdist
+from pyro import util as putil
+
+import sbibm
+from sbibm.tasks.noref_beam.task import (
+    NorefBeam,
+    bcast_coordinate_field,
+    quadratic_coordinate_field,
+    torch_average,
+)
+
+putil.set_rng_seed(47)
+
+########### sbibm related ################
+## testing the actual task
+
+
+def test_task_constructs():
+
+    t = NorefBeam()
+
+    assert t
+
+
+def test_obtain_task():
+
+    task = sbibm.get_task("noref_beam")
+
+    assert task is not None
+
+
+def test_obtain_task_modified():
+
+    task = sbibm.get_task("noref_beam", min_axis=0, max_axis=20, flood_samples=64)
+
+    assert task is not None
+    assert task.flood_samples == 64
+    assert task.min_axis == 0
+    assert task.max_axis == 20
+
+
+def test_obtain_prior():
+
+    task = sbibm.get_task("noref_beam")  # See sbibm.get_available_tasks() for all tasks
+    prior = task.get_prior()
+
+    assert prior is not None
+
+
+def test_obtain_simulator():
+
+    task = sbibm.get_task("noref_beam")
+
+    simulator = task.get_simulator()
+
+    assert simulator is not None
+
+
+def test_observe_once():
+
+    task = sbibm.get_task("noref_beam")
+
+    x_o = task.get_observation(num_observation=1)
+
+    assert x_o is not None
+    assert hasattr(x_o, "shape")
+
+
+def test_obtain_prior_samples():
+
+    task = sbibm.get_task("noref_beam")
+    prior = task.get_prior()
+    nsamples = 10
+
+    thetas = prior(num_samples=nsamples)
+
+    assert thetas.shape == (nsamples, 4)
+
+
+def test_simulate_from_thetas():
+
+    task = sbibm.get_task("noref_beam")
+    prior = task.get_prior()
+    sim = task.get_simulator()
+    nsamples = 10
+
+    thetas = prior(num_samples=nsamples)
+    xs = sim(thetas)
+
+    assert xs.shape == (nsamples, 400)
+
+
+def test_no_reference_posterior():
+
+    task = sbibm.get_task("noref_beam")
+
+    with pytest.raises(FileNotFoundError):
+        reference_samples = task.get_reference_posterior_samples(num_observation=1)
+
+
+################################################
+## sbibm compliant API tests as documented in
+## the top-level README.md of sbibm
+
+# @pytest.fixture
+# def vanilla_samples():
+
+#     task = sbibm.get_task("noref_beam")
+#     prior = task.get_prior()
+#     sim = task.get_simulator()
+#     nsamples = 10
+
+#     thetas = prior(num_samples=nsamples)
+#     xs = sim(thetas)
+
+#     return task, thetas, xs
+
+
+def test_quick_demo_rej_abc():
+
+    from sbibm.algorithms import rej_abc
+
+    task = sbibm.get_task("noref_beam")
+    posterior_samples, _, _ = rej_abc(
+        task=task, num_samples=50, num_observation=1, num_simulations=500
+    )
+
+    assert posterior_samples != None
+
+
+def test_quick_demo_c2st():
+
+    from sbibm.algorithms import rej_abc
+
+    task = sbibm.get_task("noref_beam")
+    posterior_samples, _, _ = rej_abc(
+        task=task, num_samples=50, num_observation=1, num_simulations=500
+    )
+
+    from sbibm.metrics import c2st
+
+    # TODO: catch the error as we don't have a reference posterior
+    reference_samples = task.get_reference_posterior_samples(num_observation=1)
+    c2st_accuracy = c2st(reference_samples, posterior_samples)
+
+    assert c2st_accuracy > 0.0
+    assert c2st_accuracy < 1.0
+
+
+def test_benchmark_metrics_selfobserved():
+
+    from sbibm.algorithms.sbi.snpe import run
+    from sbibm.metrics.ppc import median_distance
+
+    task = sbibm.get_task("noref_beam")
+
+    nobs = 1
+    theta_o = task.get_prior()(num_samples=nobs)
+    sim = task.get_simulator()
+    x_o = sim(theta_o)
+
+    outputs, nsim, logprob_truep = run(
+        task,
+        observation=x_o,
+        num_samples=16,
+        num_simulations=64,
+        neural_net="mdn",
+        hidden_features=4,
+        simulation_batch_size=32,
+        training_batch_size=32,
+        num_rounds=1,  # let's do NPE not SNPE (to avoid MCMC)
+    )
+
+    assert outputs.shape
+    assert outputs.shape[0] > 0
+    assert logprob_truep == None
+
+    predictive_samples = sim(outputs)
+    value = median_distance(predictive_samples, x_o)
+
+    assert value > 0
+
+
+################################################
+## API tests that related the internal task code
+
+
+def test_multivariate_normal_constructs():
+
+    m = torch.ones((2,))
+    S = torch.eye(2)
+
+    data_dist = pdist.MultivariateNormal(m.float(), S.float())
+
+    assert data_dist
+
+    sample = data_dist.sample()
+    assert sample.shape == (2,)
+
+    nensemble = 32
+    sample = data_dist.sample((nensemble,))
+    assert sample.shape == (nensemble, 2)
+
+
+def test_multivariate_normal_constructs_asbatch():
+
+    batch_size = 8
+    m = torch.ones((2,))
+    m_ = torch.broadcast_to(m, (batch_size, 2))
+    S = torch.eye(2)
+    S_ = torch.broadcast_to(S, (batch_size, 2, 2))
+
+    data_dist = pdist.MultivariateNormal(m_.float(), S_.float())
+
+    assert data_dist
+
+    sample = data_dist.sample()
+    assert sample.shape == (batch_size, 2)
+
+    nensemble = 32
+    sample = data_dist.sample((nensemble,))
+    assert sample.shape == (nensemble, batch_size, 2)
+
+
+def test_multivariate_normal_constructs_asbatch_onrange():
+
+    batch_size = 8
+    m_ = torch.arange(0, 2 * batch_size).reshape((batch_size, 2)).float()
+
+    S = torch.eye(2)
+    S_ = torch.broadcast_to(S, (batch_size, 2, 2))
+
+    data_dist = pdist.MultivariateNormal(m_.float(), S_.float())
+
+    assert data_dist
+
+    sample = data_dist.sample()
+    assert sample.shape == (batch_size, 2)
+
+    nensemble = 1024
+    samples = data_dist.sample((nensemble,))
+    assert samples.shape == (nensemble, batch_size, 2)
+
+    m_hat = samples.mean(axis=0)
+
+    assert torch.allclose(m_hat[0, :], m_[0, :], atol=75e-2)
+    assert torch.allclose(m_hat, m_, atol=2e-1)
+
+
+def test_prepare_coordinates():
+
+    batch_size = 8
+    max_axis = batch_size * 2
+    min_axis = -max_axis
+    size_axis = max_axis - min_axis
+
+    # prepare two grids for x and y
+    x = torch.arange(min_axis, max_axis).detach().float()
+    y = torch.arange(min_axis, max_axis).detach().float()
+
+    xx, yy = torch.meshgrid(x, y)
+    val = torch.swapaxes(torch.stack((xx.flatten(), yy.flatten())), 1, 0).float()
+
+    valr = val.reshape(size_axis, size_axis, 2)
+
+    # at every point of the image w=size_axis x w=size_axis
+    # we store the (x,y) coordinate of a regular grid
+    # so we get:
+    # valr[0,0] = (-16,-16),
+    # valr[0,1] = (-16,-15),
+    # valr[0,2] = (-16,-14)
+
+    assert valr.shape == (size_axis, size_axis, 2)
+    assert not torch.allclose(valr[0, 0], valr[0, 1])
+    assert torch.allclose(valr[0, 0], valr[1, 1] - 1.0)
+    assert valr[-1, 0, 0] == valr[0, -1, 1]
+
+    # broadcast to <batchsize> doublicates
+    valr_ = torch.broadcast_to(valr, (batch_size, *valr.shape)).detach()
+
+    # move axis from position 2 to front
+    valb = torch.swapaxes(valr_, 2, 0)
+
+    # we store the x and y coordinates as 2 images
+    assert valb.shape == (size_axis, size_axis, batch_size, 2)
+    assert torch.allclose(valb[:, :, 0, :], valb[:, :, 1, :])
+
+
+def test_quadratic_coordinate_field():
+
+    batch_size = 8
+    max_axis = batch_size * 2
+    min_axis = -max_axis
+    size_axis = max_axis - min_axis
+
+    arr = quadratic_coordinate_field(min_axis, max_axis, batch_size)
+
+    assert arr.shape == (size_axis, size_axis, batch_size, 2)
+    assert torch.allclose(arr[:, :, 0, :], arr[:, :, 1, :])
+    assert torch.allclose(arr[:, :, 0, :], arr[:, :, -1, :])
+    assert torch.allclose(arr[:, :, batch_size // 2, :], arr[:, :, -1, :])
+
+
+def test_binomial_api():
+
+    img = torch.tensor([[0.05, 0.1, 0.05], [0.1, 0.4, 0.1], [0.05, 0.1, 0.05]])
+
+    assert img.sum() == 1.0
+    bdist = pdist.Binomial(total_count=1024, probs=img)
+    samples = bdist.sample()
+    assert samples.shape == img.shape
+    assert samples.max() < 1024 * 0.5
+
+    lims = np.arange(3)
+    mean = np.average(lims, weights=samples.sum(axis=0).numpy(), axis=0)
+    assert np.allclose(mean, 1, atol=1e-1)
+
+
+def test_multivariate_normal_sample_binomial_from_logprob():
+
+    batch_size = 8
+    max_axis = batch_size * 2
+    min_axis = -max_axis
+    size_axis = max_axis - min_axis
+    m_ = torch.arange(-batch_size, batch_size).reshape((batch_size, 2)).float()
+
+    S = torch.eye(2)
+    S_ = torch.broadcast_to(S, (batch_size, 2, 2))
+
+    data_dist = pdist.MultivariateNormal(m_.float(), S_.float(), validate_args=False)
+
+    x = torch.arange(min_axis, max_axis).detach().float()
+    y = torch.arange(min_axis, max_axis).detach().float()
+    xx, yy = torch.meshgrid(x, y)
+    val = torch.swapaxes(torch.stack((xx.flatten(), yy.flatten())), 1, 0).float()
+    valr = val.reshape(size_axis, size_axis, 2)
+
+    valr_ = torch.broadcast_to(valr, (batch_size, *valr.shape)).detach()
+    valb = torch.swapaxes(valr_, 2, 0)
+
+    # TODO: replace this with sampling
+    # create images from probabilities
+    img = torch.exp(data_dist.log_prob(valb))
+    assert img.shape == (size_axis, size_axis, batch_size)
+
+    # sample this using a binomial
+    bdist = pdist.Binomial(total_count=1024 * 16, probs=img)
+    samples = bdist.sample()
+
+    # shapes are correct of the sampled image
+    assert samples.shape == valb.shape[:-1]
+    assert samples.shape == img.shape
+
+    samples_tox = torch.sum(samples, axis=0)
+    samples_toy = torch.sum(samples, axis=1)
+
+    # shapes of projections to specific axes are correct
+    assert samples_tox.shape == samples_toy.shape
+    assert samples_tox.shape == (size_axis, batch_size)
+
+    x_ = torch.broadcast_to(x, (batch_size, size_axis))
+    xt = torch.swapaxes(x_, 1, 0)
+
+    assert xt.shape == samples_tox.shape
+
+    # compare mean values per axis with the originals
+    # defined at the beginning of this function
+    m_hat0 = torch.sum(xt * samples_tox, axis=0) / torch.sum(samples_tox, axis=0)
+    assert m_hat0.shape == (batch_size,)
+    assert torch.allclose(m_hat0, m_[:, 0], atol=1e-1)
+
+    m_hat1 = torch.sum(xt * samples_toy, axis=0) / torch.sum(samples_toy, axis=0)
+    assert m_hat1.shape == (batch_size,)
+    assert torch.allclose(m_hat1, m_[:, 1], atol=1e-1)
+
+
+def test_torch_average():
+
+    m_ = 5 * torch.arange(1, 3).float()
+    S = torch.eye(2).float()
+
+    data_dist = pdist.MultivariateNormal(m_, S)
+
+    samples = data_dist.sample((2048,))
+
+    bins0, edges0 = np.histogram(samples[:, 0].numpy(), bins=15)
+
+    m0 = torch_average(torch.from_numpy(edges0[:-1]), torch.from_numpy(bins0))
+
+    assert m0 > 4.0
+    assert m0 < 6.0
+
+    bins1, edges1 = np.histogram(samples[:, 1].numpy(), bins=15)
+
+    m1 = torch_average(torch.from_numpy(edges1[:-1]), torch.from_numpy(bins1))
+
+    assert m1 > 9.0
+    assert m1 < 11.0
+
+
+def test_bcast_coordinate_field():
+
+    m_ = torch.arange(12).reshape(4, 3).float()
+    assert m_.shape == (4, 3)
+    arr = bcast_coordinate_field(m_, 2)
+
+    assert arr.shape == (3, 4, 2)