diff --git a/llms-full.txt b/llms-full.txt
index 0257007a..02342769 100644
--- a/llms-full.txt
+++ b/llms-full.txt
@@ -69,6 +69,8 @@ AUTO-GENERATED by PyAutoHands — do not edit by hand; regenerate with generate.
   - Contents: Setup, Fit Imaging, Reconstruction, Inversion Plots, Mapper, Mesh Grids
 - [Simulator: Clumpy Galaxy](scripts/imaging/features/pixelization/simulator.py): This script simulates `Imaging` of a galaxy whose light has two distinct components:
   - Contents: Dataset Paths, Grid, Over Sampling, Galaxies, Output, Visualize, Mask Extra Galaxies, Plane Output
+- [Simulator: Point Source](scripts/imaging/features/point_source/simulator.py): This script simulates an `Imaging` dataset containing a galaxy with:
+  - Contents: Dataset Paths, Grid, PSF, Galaxies, Simulation, Output
 - [Modeling Features: Shapelets](scripts/imaging/features/shapelets/fit.py): A shapelet is a basis function that is appropriate for capturing the exponential / disk-like features of a galaxy. It has been employed in galaxy structure studies to model the light of the galaxy, because it can represent features of disky star forming galaxies that a single Sersic function cannot.
   - Contents: Advantages & Disadvantages, Dataset & Mask, Basis, Coefficients, Linear Light Profiles, Fit, Intensities, Model, Search & Analysis, Run Time, Model-Fit, Result, Cartesian Shapelets
 - [Modeling Features: Shapelets](scripts/imaging/features/shapelets/modeling.py): A shapelet is a basis function that is appropriate for capturing the exponential / disk-like features of a galaxy. It has been employed in galaxy structure studies to model the light of the galaxy, because it can represent features of disky star forming galaxies that a single Sersic function cannot.
diff --git a/notebooks/imaging/features/point_source/simulator.ipynb b/notebooks/imaging/features/point_source/simulator.ipynb
new file mode 100644
index 00000000..28bf079c
--- /dev/null
+++ b/notebooks/imaging/features/point_source/simulator.ipynb
@@ -0,0 +1,331 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Simulator: Point Source\n",
+    "=======================\n",
+    "\n",
+    "This script simulates an `Imaging` dataset containing a galaxy with:\n",
+    "\n",
+    " - An extended `Sersic` host galaxy.\n",
+    " - An unresolved point source represented by `ag.lp.PointSource`.\n",
+    "\n",
+    "A point source is a delta function in the image plane. Its `intensity` is the\n",
+    "total flux of the source, not a surface-brightness value evaluated independently\n",
+    "at every grid coordinate.\n",
+    "\n",
+    "Accurate point-source imaging requires an over-sampled PSF. The source is placed\n",
+    "on the nearest sub-pixel, convolved with the PSF at that finer resolution and\n",
+    "then binned back to the detector pixels while conserving total flux. Setting\n",
+    "`convolve_over_sample_size=1` remains a valid pixel-centred approximation, but\n",
+    "does not retain the source's sub-pixel position.\n",
+    "\n",
+    "For model fitting, `ag.lp_linear.PointSource` provides the same spatial model\n",
+    "with its total flux solved by the linear inversion. The source centre remains a\n",
+    "non-linear parameter.\n",
+    "\n",
+    "__Contents__\n",
+    "\n",
+    "- **Dataset Paths:** Define where the simulated dataset is written.\n",
+    "- **Grid:** Create a detector grid with sampling compatible with the PSF.\n",
+    "- **PSF:** Define a Gaussian PSF sampled at twice the detector resolution.\n",
+    "- **Galaxies:** Combine an extended host and unresolved point source.\n",
+    "- **Simulation:** Simulate noisy imaging using fine-grid PSF convolution.\n",
+    "- **Output:** Write the dataset, plots and input galaxies to disk."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "__Google Colab Setup__\n",
+    "\n",
+    "This cell sets up the environment when the notebook is run on Google Colab: it installs the\n",
+    "required PyAuto packages, clones the workspace (configuration files and example datasets) and\n",
+    "points the configuration at it. If you are running the notebook elsewhere (e.g. locally via\n",
+    "your own installation) it does nothing, and you can run it safely.\n",
+    "\n",
+    "Colab tip: model-fits run much faster on a GPU \u2014 enable one via \"Runtime\" -> \"Change runtime\n",
+    "type\" -> \"Hardware accelerator\" before running the notebook."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "try:\n",
+    "    import google.colab\n",
+    "except ImportError:\n",
+    "    from autogalaxy import setup_colab as _setup_colab\n",
+    "else:\n",
+    "    import importlib\n",
+    "    import subprocess\n",
+    "    import sys\n",
+    "\n",
+    "    subprocess.check_call(\n",
+    "        [sys.executable, \"-m\", \"pip\", \"install\", \"autonerves\", \"--no-deps\"]\n",
+    "    )\n",
+    "    _setup_colab = importlib.import_module(\"autonerves.setup_colab\")\n",
+    "\n",
+    "_setup_colab.setup(\"autogalaxy\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {},
+   "source": [
+    "\n",
+    "from autogalaxy import setup_notebook; setup_notebook()\n",
+    "\n",
+    "from pathlib import Path\n",
+    "\n",
+    "import autogalaxy as ag\n",
+    "import autogalaxy.plot as aplt"
+   ],
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "__Dataset Paths__\n",
+    "\n",
+    "The simulated data are written to `dataset/imaging/point_source`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {},
+   "source": [
+    "dataset_path = Path(\"dataset\", \"imaging\", \"point_source\")"
+   ],
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "__Grid__\n",
+    "\n",
+    "The detector pixels are 0.1 arcseconds across. A uniform over-sample size of 2\n",
+    "gives four sub-pixels per detector pixel and is compatible with the PSF\n",
+    "convolution factor defined below."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {},
+   "source": [
+    "over_sample_size = 2\n",
+    "\n",
+    "grid = ag.Grid2D.uniform(\n",
+    "    shape_native=(101, 101),\n",
+    "    pixel_scales=0.1,\n",
+    "    over_sample_size=over_sample_size,\n",
+    ")"
+   ],
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "__PSF__\n",
+    "\n",
+    "The PSF kernel is sampled on pixels that are twice as fine as the detector\n",
+    "pixels. Therefore its `pixel_scales` are `0.1 / 2 = 0.05` arcseconds and its\n",
+    "`convolve_over_sample_size` is 2.\n",
+    "\n",
+    "During simulation PyAutoGalaxy evaluates the point source on this fine grid,\n",
+    "convolves it with the fine PSF and bins the result back to the 0.1 arcsecond\n",
+    "detector grid."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {},
+   "source": [
+    "psf = ag.Convolver.from_gaussian(\n",
+    "    shape_native=(21, 21),\n",
+    "    sigma=0.12,\n",
+    "    pixel_scales=grid.pixel_scales[0] / over_sample_size,\n",
+    "    normalize=True,\n",
+    "    convolve_over_sample_size=over_sample_size,\n",
+    ")\n",
+    "\n",
+    "simulator = ag.SimulatorImaging(\n",
+    "    exposure_time=300.0,\n",
+    "    psf=psf,\n",
+    "    background_sky_level=0.1,\n",
+    "    add_poisson_noise_to_data=True,\n",
+    "    noise_seed=1,\n",
+    ")"
+   ],
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "__Galaxies__\n",
+    "\n",
+    "The point source is offset by a quarter of a detector pixel in each direction.\n",
+    "At over-sample size 2 this selects one of the central pixel's four sub-pixels,\n",
+    "so the blurred image retains the offset instead of forcing the source onto the\n",
+    "detector-pixel centre.\n",
+    "\n",
+    "The point source `intensity=25.0` is its total flux before noise and background\n",
+    "sky are added. It is conserved when the fine-grid image is binned back to the\n",
+    "detector resolution."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {},
+   "source": [
+    "point_source_centre = (0.025, -0.025)\n",
+    "\n",
+    "galaxy = ag.Galaxy(\n",
+    "    redshift=0.5,\n",
+    "    host=ag.lp.Sersic(\n",
+    "        centre=(0.0, 0.0),\n",
+    "        ell_comps=ag.convert.ell_comps_from(axis_ratio=0.8, angle=45.0),\n",
+    "        intensity=0.5,\n",
+    "        effective_radius=0.8,\n",
+    "        sersic_index=2.0,\n",
+    "    ),\n",
+    "    point_source=ag.lp.PointSource(\n",
+    "        centre=point_source_centre,\n",
+    "        intensity=25.0,\n",
+    "    ),\n",
+    ")\n",
+    "\n",
+    "galaxies = ag.Galaxies(galaxies=[galaxy])"
+   ],
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "__Simulation__\n",
+    "\n",
+    "Simulate the dataset. The simulator automatically uses the over-sampled PSF\n",
+    "path because `convolve_over_sample_size=2`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {},
+   "source": [
+    "dataset = simulator.via_galaxies_from(galaxies=galaxies, grid=grid)\n",
+    "\n",
+    "aplt.subplot_imaging_dataset(dataset=dataset)"
+   ],
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "__Output__\n",
+    "\n",
+    "Write the image, PSF and noise map as FITS files."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {},
+   "source": [
+    "aplt.fits_imaging(\n",
+    "    dataset=dataset,\n",
+    "    data_path=dataset_path / \"data.fits\",\n",
+    "    psf_path=dataset_path / \"psf.fits\",\n",
+    "    noise_map_path=dataset_path / \"noise_map.fits\",\n",
+    "    overwrite=True,\n",
+    ")"
+   ],
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Write PNG visualizations of the simulated dataset and its input galaxy."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {},
+   "source": [
+    "aplt.subplot_imaging_dataset(\n",
+    "    dataset=dataset,\n",
+    "    output_path=dataset_path,\n",
+    "    output_format=\"png\",\n",
+    ")\n",
+    "aplt.subplot_galaxies(\n",
+    "    galaxies=galaxies,\n",
+    "    grid=grid,\n",
+    "    output_path=dataset_path,\n",
+    "    output_format=\"png\",\n",
+    ")"
+   ],
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Save the exact input galaxy as JSON for reproducibility."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {},
+   "source": [
+    "ag.output_to_json(\n",
+    "    obj=galaxies,\n",
+    "    file_path=dataset_path / \"galaxies.json\",\n",
+    ")\n",
+    "\n",
+    "print(f\"Point-source dataset written to {dataset_path}\")\n"
+   ],
+   "outputs": [],
+   "execution_count": null
+  }
+ ],
+ "metadata": {
+  "anaconda-cloud": {},
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
\ No newline at end of file
diff --git a/scripts/imaging/features/point_source/simulator.py b/scripts/imaging/features/point_source/simulator.py
new file mode 100644
index 00000000..9ae2013c
--- /dev/null
+++ b/scripts/imaging/features/point_source/simulator.py
@@ -0,0 +1,167 @@
+"""
+Simulator: Point Source
+=======================
+
+This script simulates an `Imaging` dataset containing a galaxy with:
+
+ - An extended `Sersic` host galaxy.
+ - An unresolved point source represented by `ag.lp.PointSource`.
+
+A point source is a delta function in the image plane. Its `intensity` is the
+total flux of the source, not a surface-brightness value evaluated independently
+at every grid coordinate.
+
+Accurate point-source imaging requires an over-sampled PSF. The source is placed
+on the nearest sub-pixel, convolved with the PSF at that finer resolution and
+then binned back to the detector pixels while conserving total flux. Setting
+`convolve_over_sample_size=1` remains a valid pixel-centred approximation, but
+does not retain the source's sub-pixel position.
+
+For model fitting, `ag.lp_linear.PointSource` provides the same spatial model
+with its total flux solved by the linear inversion. The source centre remains a
+non-linear parameter.
+
+__Contents__
+
+- **Dataset Paths:** Define where the simulated dataset is written.
+- **Grid:** Create a detector grid with sampling compatible with the PSF.
+- **PSF:** Define a Gaussian PSF sampled at twice the detector resolution.
+- **Galaxies:** Combine an extended host and unresolved point source.
+- **Simulation:** Simulate noisy imaging using fine-grid PSF convolution.
+- **Output:** Write the dataset, plots and input galaxies to disk.
+"""
+
+# from autogalaxy import setup_notebook; setup_notebook()
+
+from pathlib import Path
+
+import autogalaxy as ag
+import autogalaxy.plot as aplt
+
+"""
+__Dataset Paths__
+
+The simulated data are written to `dataset/imaging/point_source`.
+"""
+dataset_path = Path("dataset", "imaging", "point_source")
+
+"""
+__Grid__
+
+The detector pixels are 0.1 arcseconds across. A uniform over-sample size of 2
+gives four sub-pixels per detector pixel and is compatible with the PSF
+convolution factor defined below.
+"""
+over_sample_size = 2
+
+grid = ag.Grid2D.uniform(
+    shape_native=(101, 101),
+    pixel_scales=0.1,
+    over_sample_size=over_sample_size,
+)
+
+"""
+__PSF__
+
+The PSF kernel is sampled on pixels that are twice as fine as the detector
+pixels. Therefore its `pixel_scales` are `0.1 / 2 = 0.05` arcseconds and its
+`convolve_over_sample_size` is 2.
+
+During simulation PyAutoGalaxy evaluates the point source on this fine grid,
+convolves it with the fine PSF and bins the result back to the 0.1 arcsecond
+detector grid.
+"""
+psf = ag.Convolver.from_gaussian(
+    shape_native=(21, 21),
+    sigma=0.12,
+    pixel_scales=grid.pixel_scales[0] / over_sample_size,
+    normalize=True,
+    convolve_over_sample_size=over_sample_size,
+)
+
+simulator = ag.SimulatorImaging(
+    exposure_time=300.0,
+    psf=psf,
+    background_sky_level=0.1,
+    add_poisson_noise_to_data=True,
+    noise_seed=1,
+)
+
+"""
+__Galaxies__
+
+The point source is offset by a quarter of a detector pixel in each direction.
+At over-sample size 2 this selects one of the central pixel's four sub-pixels,
+so the blurred image retains the offset instead of forcing the source onto the
+detector-pixel centre.
+
+The point source `intensity=25.0` is its total flux before noise and background
+sky are added. It is conserved when the fine-grid image is binned back to the
+detector resolution.
+"""
+point_source_centre = (0.025, -0.025)
+
+galaxy = ag.Galaxy(
+    redshift=0.5,
+    host=ag.lp.Sersic(
+        centre=(0.0, 0.0),
+        ell_comps=ag.convert.ell_comps_from(axis_ratio=0.8, angle=45.0),
+        intensity=0.5,
+        effective_radius=0.8,
+        sersic_index=2.0,
+    ),
+    point_source=ag.lp.PointSource(
+        centre=point_source_centre,
+        intensity=25.0,
+    ),
+)
+
+galaxies = ag.Galaxies(galaxies=[galaxy])
+
+"""
+__Simulation__
+
+Simulate the dataset. The simulator automatically uses the over-sampled PSF
+path because `convolve_over_sample_size=2`.
+"""
+dataset = simulator.via_galaxies_from(galaxies=galaxies, grid=grid)
+
+aplt.subplot_imaging_dataset(dataset=dataset)
+
+"""
+__Output__
+
+Write the image, PSF and noise map as FITS files.
+"""
+aplt.fits_imaging(
+    dataset=dataset,
+    data_path=dataset_path / "data.fits",
+    psf_path=dataset_path / "psf.fits",
+    noise_map_path=dataset_path / "noise_map.fits",
+    overwrite=True,
+)
+
+"""
+Write PNG visualizations of the simulated dataset and its input galaxy.
+"""
+aplt.subplot_imaging_dataset(
+    dataset=dataset,
+    output_path=dataset_path,
+    output_format="png",
+)
+aplt.subplot_galaxies(
+    galaxies=galaxies,
+    grid=grid,
+    output_path=dataset_path,
+    output_format="png",
+)
+
+"""
+Save the exact input galaxy as JSON for reproducibility.
+"""
+ag.output_to_json(
+    obj=galaxies,
+    file_path=dataset_path / "galaxies.json",
+)
+
+print(f"Point-source dataset written to {dataset_path}")
diff --git a/smoke_tests.txt b/smoke_tests.txt
index f8481de0..2dcc00a4 100644
--- a/smoke_tests.txt
+++ b/smoke_tests.txt
@@ -13,3 +13,4 @@ guides/galaxies.py
 guides/using_jax.py
 guides/modeling/cookbook.py
 imaging/simulator.py
+imaging/features/point_source/simulator.py
diff --git a/workspace_index.json b/workspace_index.json
index 7731b8b7..3b704653 100644
--- a/workspace_index.json
+++ b/workspace_index.json
@@ -1564,6 +1564,21 @@
     "summary": "This script simulates `Imaging` of a galaxy whose light has two distinct components:",
     "title": "Simulator: Clumpy Galaxy"
   },
+  {
+    "contents": [
+      "Dataset Paths",
+      "Grid",
+      "PSF",
+      "Galaxies",
+      "Simulation",
+      "Output"
+    ],
+    "cross_refs": [],
+    "notebook": "notebooks/imaging/features/point_source/simulator.ipynb",
+    "path": "scripts/imaging/features/point_source/simulator.py",
+    "summary": "This script simulates an `Imaging` dataset containing a galaxy with:",
+    "title": "Simulator: Point Source"
+  },
   {
     "contents": [
       "Advantages & Disadvantages",