diff --git a/fink_science/bogus/__init__.py b/fink_science/bogus/__init__.py
new file mode 100644
index 00000000..e69de29b
diff --git a/fink_science/bogus/processor.py b/fink_science/bogus/processor.py
new file mode 100644
index 00000000..84aaeace
--- /dev/null
+++ b/fink_science/bogus/processor.py
@@ -0,0 +1,78 @@
+# Copyright 2025 AstroLab Software
+# Author: Julien Peloton
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+from line_profiler import profile
+
+from pyspark.sql.functions import pandas_udf, PandasUDFType
+from pyspark.sql.types import IntegerType
+
+from fink_science import __file__
+import os
+
+import pandas as pd
+import numpy as np
+
+from fink_science.tester import spark_unit_tests
+
+@pandas_udf(IntegerType(), PandasUDFType.SCALAR)
+@profile
+def ztf_bogus(rb, nbad):
+    """ Determine if an alert is a potential bogus
+
+    Parameters
+    ----------
+    rb: Spark DataFrame Column
+        Real-bogus score calculated by ZTF
+    nbad: Spark DataFrame Column
+        Number of prior-tagged bad pixels in a 5 x 5 pixel stamp
+
+    Returns
+    ----------
+    out: integer
+        1 if the alert is likely a bogus
+        0 if the alert is likely not a bogus
+
+    Examples
+    ----------
+    >>> df = spark.read.load(ztf_alert_sample)
+    >>> args = ['candidate.rb', 'candidate.nbad']
+    >>> df = df.withColumn('bogusness', ztf_bogus(*args))
+
+    >>> df.filter(df['bogusness'] == 0).count()
+    3
+
+    >>> df.filter(df['bogusness'] == 1).count()
+    3
+    """
+    # All non-bogus
+    flags = np.zeros_like(nbad.to_numpy(), dtype=int)
+
+    # Conditions for being a bogus
+    f0 = rb >= 0.55
+    f1 = nbad == 0
+    flags[f0 & f1] = 1
+
+    return pd.Series(flags)
+
+
+if __name__ == "__main__":
+    """ Execute the test suite """
+
+    globs = globals()
+    path = os.path.dirname(__file__)
+    ztf_alert_sample = 'file://{}/data/alerts/datatest'.format(path)
+    globs["ztf_alert_sample"] = ztf_alert_sample
+
+    # Run the test suite
+    spark_unit_tests(globs)