diff --git a/notebooks/ACS/acs_cte_forward_model/acs_cte_forward_model_example.ipynb b/notebooks/ACS/acs_cte_forward_model/acs_cte_forward_model_example.ipynb
index 68f6366d2..d65619284 100644
--- a/notebooks/ACS/acs_cte_forward_model/acs_cte_forward_model_example.ipynb
+++ b/notebooks/ACS/acs_cte_forward_model/acs_cte_forward_model_example.ipynb
@@ -89,7 +89,6 @@
     "- *os* for setting environment variables\n",
     "- *shutil* for managing directories\n",
     "- *numpy* for math and array calculations\n",
-    "- *collections OrderedDict* for making dictionaries easily\n",
     "- *matplotlib pyplot* for plotting\n",
     "- *matplotlib.colors LogNorm* for scaling images\n",
     "- *astropy.io fits* for working with FITS files\n",
@@ -110,10 +109,10 @@
     "import os\n",
     "import shutil\n",
     "import numpy as np\n",
-    "from collections import OrderedDict\n",
     "import matplotlib.pyplot as plt\n",
     "from astropy.io import fits\n",
-    "from photutils import datasets\n",
+    "from astropy.modeling.models import Gaussian2D\n",
+    "from photutils.datasets import make_noise_image, make_model_params, make_model_image\n",
     "from astroquery.mast import Observations\n",
     "from acstools import acsccd\n",
     "from acstools import acscte\n",
@@ -298,7 +297,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "First, we generate a table of random Gaussian sources of typical brightness for our 47 Tuc field with $\\mathrm{FWHM}\\sim2.5$ pixels. Because $\\mathrm{FWHM} = 2.355\\sigma$, we will generate Gaussian sources with $\\sigma \\sim 1.06$ pixels in both $x$ and $y$. "
+    "First, we generate a table of random Gaussian sources of typical brightness for our 47 Tuc field with $\\mathrm{FWHM}\\sim2.5$ pixels. Because $\\mathrm{FWHM} = 2.355\\sigma$, we will generate Gaussian sources with $\\sigma \\sim 1.06$ pixels in both $x$ and $y$. We get use the shape of one of the flc image SCI extensions for creating the (x, y) coordinates of the sources."
    ]
   },
   {
@@ -307,27 +306,31 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "n_sources = 300\n",
-    "param_ranges = [('amplitude', [500, 30000]),\n",
-    "                ('x_mean', [0, 4095]),\n",
-    "                ('y_mean', [0, 2047]),\n",
-    "                ('x_stddev', [1.05, 1.07]),\n",
-    "                ('y_stddev', [1.05, 1.07]),\n",
-    "                ('theta', [0, np.pi])]\n",
+    "wfc2 = fits.getdata('jd0q14ctq_flc.fits', ext=1)\n",
     "\n",
-    "param_ranges = OrderedDict(param_ranges)\n",
+    "shape = wfc2.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "n_sources = 300\n",
     "\n",
-    "sources = datasets.make_random_gaussians_table(n_sources, param_ranges, \n",
-    "                                               seed=12345)\n",
+    "sources = make_model_params(shape, n_sources, x_name='x_mean', y_name='y_mean',\n",
+    "                            amplitude=(500, 30000), x_stddev=(1.05, 1.07), \n",
+    "                            y_stddev=(1.05, 1.07), theta=(0, np.pi), seed=12345)\n",
     "\n",
-    "print(sources)"
+    "sources"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Next, we get the shape of one of the `flc` image `SCI` extensions and make an image from the table of Gaussian sources. Note that this step may take a few minutes. Finally, we run the synthetic image through a Poisson sampler in order to simulate the Poisson noise of the scene."
+    "Next, we make an image from the table of Gaussian sources. Finally, we run the synthetic image through a Poisson sampler in order to simulate the Poisson noise of the scene."
    ]
   },
   {
@@ -336,11 +339,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "wfc2 = fits.getdata('jd0q14ctq_flc.fits', ext=1)\n",
-    "\n",
-    "shape = wfc2.shape\n",
-    "\n",
-    "synth_stars_image = datasets.make_gaussian_sources_image(shape, sources)\n",
+    "model = Gaussian2D()\n",
+    "synth_stars_image = make_model_image(shape, model, sources, \n",
+    "                                     x_name='x_mean', y_name='y_mean', progress_bar=True)\n",
     "\n",
     "synth_stars_image = np.random.poisson(synth_stars_image)"
    ]
@@ -382,7 +383,7 @@
     "ax.imshow(flc, vmin=0, vmax=200, interpolation='nearest', cmap='Greys_r', origin='lower')\n",
     "\n",
     "ax.set_xlim(2000, 2800)\n",
-    "ax.set_ylim(1200, 1700)"
+    "ax.set_ylim(800, 1300)"
    ]
   },
   {
@@ -442,7 +443,7 @@
     "        markerfacecolor='none', markeredgecolor='red', linestyle='none')\n",
     "\n",
     "ax.set_xlim(2000, 2800)\n",
-    "ax.set_ylim(1200, 1700)"
+    "ax.set_ylim(800, 1300)"
    ]
   },
   {
@@ -722,9 +723,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "scrolled": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "flt_stars = fits.getdata('jd0q14ctq_stars_ctefmod_flt.fits', ext=1)\n",
@@ -737,15 +736,13 @@
     "        markerfacecolor='none', markeredgecolor='red', linestyle='none')\n",
     "\n",
     "ax.set_xlim(2000, 2800)\n",
-    "ax.set_ylim(1200, 1700)"
+    "ax.set_ylim(800, 1300)"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "scrolled": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "flc_stars = fits.getdata('jd0q14ctq_stars_ctefmod_flc.fits', ext=1)\n",
@@ -758,7 +755,7 @@
     "        markerfacecolor='none', markeredgecolor='red', linestyle='none')\n",
     "\n",
     "ax.set_xlim(2000, 2800)\n",
-    "ax.set_ylim(1200, 1700)"
+    "ax.set_ylim(800, 1300)"
    ]
   },
   {
@@ -829,7 +826,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "noise_image = datasets.make_noise_image(shape, distribution='poisson', mean=40, seed=12345)\n",
+    "noise_image = make_noise_image(shape, distribution='poisson', mean=40, seed=12345)\n",
     "\n",
     "wfc1 += noise_image + synth_stars_image\n",
     "wfc2 += noise_image + synth_stars_image\n",
@@ -860,7 +857,7 @@
     "        markerfacecolor='none', markeredgecolor='red', linestyle='none')\n",
     "\n",
     "ax.set_xlim(2000, 2800)\n",
-    "ax.set_ylim(1200, 1700)"
+    "ax.set_ylim(800, 1300)"
    ]
   },
   {
@@ -986,14 +983,14 @@
     "rn_C = hdr['READNSEC']\n",
     "rn_D = hdr['READNSED']\n",
     "\n",
-    "img_rn_A = datasets.make_noise_image((shape[0], int(shape[1]/2)), distribution='gaussian', \n",
-    "                                     mean=0., stddev=rn_A)\n",
-    "img_rn_B = datasets.make_noise_image((shape[0], int(shape[1]/2)), distribution='gaussian', \n",
-    "                                     mean=0., stddev=rn_B)\n",
-    "img_rn_C = datasets.make_noise_image((shape[0], int(shape[1]/2)), distribution='gaussian', \n",
-    "                                     mean=0., stddev=rn_C)\n",
-    "img_rn_D = datasets.make_noise_image((shape[0], int(shape[1]/2)), distribution='gaussian', \n",
-    "                                     mean=0., stddev=rn_D)\n",
+    "img_rn_A = make_noise_image((shape[0], int(shape[1]/2)), distribution='gaussian', \n",
+    "                            mean=0., stddev=rn_A)\n",
+    "img_rn_B = make_noise_image((shape[0], int(shape[1]/2)), distribution='gaussian', \n",
+    "                            mean=0., stddev=rn_B)\n",
+    "img_rn_C = make_noise_image((shape[0], int(shape[1]/2)), distribution='gaussian', \n",
+    "                            mean=0., stddev=rn_C)\n",
+    "img_rn_D = make_noise_image((shape[0], int(shape[1]/2)), distribution='gaussian', \n",
+    "                            mean=0., stddev=rn_D)\n",
     "\n",
     "wfc1_rn = np.hstack((img_rn_A, img_rn_B))\n",
     "wfc2_rn = np.hstack((img_rn_C, img_rn_D))"
@@ -1290,7 +1287,7 @@
     "        markerfacecolor='none', markeredgecolor='red', linestyle='none')\n",
     "\n",
     "ax.set_xlim(2000, 2800)\n",
-    "ax.set_ylim(1200, 1700)"
+    "ax.set_ylim(800, 1300)"
    ]
   },
   {
@@ -1309,7 +1306,7 @@
     "        markerfacecolor='none', markeredgecolor='red', linestyle='none')\n",
     "\n",
     "ax.set_xlim(2000, 2800)\n",
-    "ax.set_ylim(1200, 1700)"
+    "ax.set_ylim(800, 1300)"
    ]
   },
   {
@@ -1365,7 +1362,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.12"
+   "version": "3.12.7"
   }
  },
  "nbformat": 4,
diff --git a/notebooks/ACS/acs_cte_forward_model/requirements.txt b/notebooks/ACS/acs_cte_forward_model/requirements.txt
index 5f2e62ee7..ee44cb0f3 100644
--- a/notebooks/ACS/acs_cte_forward_model/requirements.txt
+++ b/notebooks/ACS/acs_cte_forward_model/requirements.txt
@@ -3,6 +3,6 @@ astropy>=5.3.3
 astroquery>=0.4.6
 matplotlib>=3.7.0
 numpy>=1.23.4
-photutils>=1.6.0
+photutils>=2.0.2
 crds>=11.17.7
-stsci.tools>=4.1.0
\ No newline at end of file
+stsci.tools>=4.1.0