From b0a790dc4042b4b3bdf5fc9db4faf624536ccdfe Mon Sep 17 00:00:00 2001
From: Troy Raen <raen@ipac.caltech.edu>
Date: Thu, 25 Jun 2026 11:34:44 -0700
Subject: [PATCH 1/5] Standardize formatting

---
 .../techniques-and-tools/MOST_queries.md      | 462 +++++++++---------
 1 file changed, 231 insertions(+), 231 deletions(-)

diff --git a/tutorials/techniques-and-tools/MOST_queries.md b/tutorials/techniques-and-tools/MOST_queries.md
index b49bbacb..113219cb 100644
--- a/tutorials/techniques-and-tools/MOST_queries.md
+++ b/tutorials/techniques-and-tools/MOST_queries.md
@@ -11,7 +11,7 @@ kernelspec:
   language: python
 ---
 
-# Running Moving Object Search Tool (MOST) queries
+# Run Moving Object Search Tool (MOST) Queries
 
 ## Learning Goals
 
@@ -20,7 +20,7 @@ By the end of this tutorial, you will be able to:
 - Run a MOST query from Python.
 - Identify and retrieve images intersecting the path of a moving object.
 - Create cutouts, centered on a moving object, from remote images on-the-fly.
-- Make quick co-adds of the single exposures, centered on the moving object. 
+- Make quick co-adds of the single exposures, centered on the moving object.
 
 ## Introduction
 
@@ -42,34 +42,34 @@ Metadata tables containing the location of the target as a function of time and
 ```
 
 ```{code-cell} ipython3
-#For array math, deep copying, and plotting
+# For array math, deep copying, and plotting
 import numpy as np
 import copy
 import matplotlib.pyplot as plt
 from matplotlib.patches import Polygon
 
-#Main function for running a MOST query
+# Main function for running a MOST query
 from astroquery.ipac.irsa.most import Most
 
-#Key astroquery module for accessing other IRSA services
+# Key astroquery module for accessing other IRSA services
 from astroquery.ipac.irsa import Irsa
 
-#Tools for opening FITS files, making cutout, and displaying images
+# Tools for opening FITS files, making cutout, and displaying images
 import astropy.io.fits as fits
 from astropy.nddata import Cutout2D
 from astropy.utils.data import conf
 from astropy.visualization import ImageNormalize, ZScaleInterval
 
-#For handling physical units and positions on the sky
+# For handling physical units and positions on the sky
 import astropy.units as u
 from astropy.coordinates import SkyCoord
 from astropy.wcs import WCS
 
-#Package for working with DS9 region files
+# Package for working with DS9 region files
 from regions import Regions
 from regions import PixCoord, PolygonSkyRegion
 
-#Package for reprojecting images
+# Package for reprojecting images
 from reproject import reproject_interp
 
 # Astropy's default timeout limit can be too short in some cases. Increase it to 2 minutes.
@@ -91,8 +91,8 @@ Most.clear_cache()
 To run the MOST query we will use the `query_object` function from astroquery's [`Most` module](https://astroquery.readthedocs.io/en/latest/ipac/irsa/most.html).
 When querying a well-known asteroid or comet, a MOST search can usually be run with simply its name and a time window.
 
-In all the examples in this notebook we use the (default) output mode as `"Regular"`. 
-We note that although `"Full"` is an output option, the additional output is not returned by astroquery, so the only meaningful difference will be that it takes longer to run. 
+In all the examples in this notebook we use the (default) output mode as `"Regular"`.
+We note that although `"Full"` is an output option, the additional output is not returned by astroquery, so the only meaningful difference will be that it takes longer to run.
 For more details on output modes, see the [`query_object` docs page](https://astroquery.readthedocs.io/en/latest/ipac/irsa/most.html#output-modes) or the [MOST instructions page](https://irsa.ipac.caltech.edu/applications/MOST/instructions.html).
 
 +++
@@ -125,7 +125,7 @@ We will make use of both of these columns below.
 most_output['results']
 ```
 
-Next, the `'metadata'` object is another astropy `Table` that lists more information about the exposures, including the four corners of each image. 
+Next, the `'metadata'` object is another astropy `Table` that lists more information about the exposures, including the four corners of each image.
 If the MOST query was run in `"Brief"`, rather than `"Regular"`, mode then this table would be the only output.
 
 ```{code-cell} ipython3
@@ -157,7 +157,7 @@ Now convert the four corners of every exposure FoV (given in the `'metadata'` ta
 ```{code-cell} ipython3
 FoV_list = []
 for row in most_output['metadata']:
-    vertices = SkyCoord([row['ra1'], row['ra2'], row['ra3'], row['ra4']], 
+    vertices = SkyCoord([row['ra1'], row['ra2'], row['ra3'], row['ra4']],
                         [row['dec1'], row['dec2'], row['dec3'], row['dec4']], unit=u.deg)
     FoV_list.append(PolygonSkyRegion(vertices=vertices))
 ```
@@ -170,30 +170,30 @@ The cell below steps through every segment of the orbital path and extracts the
 It then determines the extremes of RA and Dec, which will be used for setting the orbital path plot area below.
 
 ```{code-cell} ipython3
-#Check for a zero crossing
+# Check for a zero crossing
 RA_zero_cross = False
-for i,segment in enumerate(orb_path): 
+for i,segment in enumerate(orb_path):
     if abs(segment.end.ra.deg-segment.start.ra.deg) > 180:
         print("Caution: Likely RA = 0 crossing.")
         RA_zero_cross = True
         break
 
-#If there is a zero crossing then switch wrap to 180 deg
+# If there is a zero crossing then switch wrap to 180 deg
 if RA_zero_cross:
-    for i,segment in enumerate(orb_path): 
+    for i,segment in enumerate(orb_path):
         segment.start.ra.wrap_at(180*u.deg, inplace=True)
     RA_zero_cross = False
 
-#Extract all RA and Dec values of segments
+# Extract all RA and Dec values of segments
 ra_list = np.zeros(len(orb_path)*2)
 dec_list = np.zeros(len(orb_path)*2)
-for i,segment in enumerate(orb_path): 
+for i,segment in enumerate(orb_path):
     ra_list[2*i] = segment.start.ra.deg
     ra_list[2*i+1] = segment.end.ra.deg
     dec_list[2*i] = segment.start.dec.deg
     dec_list[2*i+1] = segment.end.dec.deg
 
-#Find extremes of RA and Dec
+# Find extremes of RA and Dec
 ra_min = min(ra_list)
 ra_max = max(ra_list)
 dec_min = min(dec_list)
@@ -203,70 +203,70 @@ dec_max = max(dec_list)
 Now that the RA and Dec extremes have been determined we can set up a [World Coordinate System (WCS)](https://docs.astropy.org/en/stable/wcs/index.html) to define the projection of the orbit plot.
 
 ```{code-cell} ipython3
-#Initialize 2D WCS
+# Initialize 2D WCS
 plot_wcs = WCS(naxis=2)
 plot_wcs.wcs.crpix = [0,0]
 plot_wcs.wcs.ctype = ["RA---SIN", "DEC--SIN"]
 
-#Set pixel size as 1 arcmin
+# Set pixel size as 1 arcmin
 plot_wcs.wcs.cdelt = [-1./60., 1/60.]
 
-#Determine the position of plot center (midway between extremes)
+# Determine the position of plot center (midway between extremes)
 extremes = SkyCoord(ra=[ra_min,ra_max],dec=[dec_min,dec_max],unit=u.deg)
 separation = extremes[0].separation(extremes[1])
 pa = extremes[0].position_angle(extremes[1])
 midpoint = extremes[0].directional_offset_by(pa, separation/2)
 
-#Set coordinates of WCS center
+# Set coordinates of WCS center
 plot_wcs.wcs.crval = [midpoint.ra.deg,midpoint.dec.deg]
 ```
 
 ### 2.3 Producing the plot
 
-Finally, we can make the plot. 
+Finally, we can make the plot.
 The cell below plots the orbital path, after first ensuring the plot area is large enough to include the full path over the period in the query.
 It then overplots in red the fields of view of all of the exposures that intersect the path.
 
 ```{code-cell} ipython3
-#Determine the range of RA and Dec values
-#in order to set the number of pixel on each axis
+# Determine the range of RA and Dec values
+# in order to set the number of pixel on each axis
 ra_sep = separation.deg*abs(np.sin(pa))
 dec_sep = separation.deg*abs(np.cos(pa))
 x_npix = max(60,abs(ra_sep/plot_wcs.wcs.cdelt[0]))
 y_npix = max(60,abs(dec_sep/plot_wcs.wcs.cdelt[1]))
 
-#Don't allow aspect ratio to exceed 2:1
+# Don't allow aspect ratio to exceed 2:1
 if not 1/2 < x_npix/y_npix < 2:
     if x_npix > y_npix:
         y_npix = x_npix/2
     else:
         x_npix = y_npix/2
 
-#Initialize the figure, setting its dimensions
+# Initialize the figure, setting its dimensions
 if x_npix > y_npix:
     fig = plt.figure(figsize=(8,8*y_npix/x_npix))
 else:
     fig = plt.figure(figsize=(8*x_npix/y_npix,8))
 
-#Set the axis projection with the WCS
+# Set the axis projection with the WCS
 ax = fig.add_subplot(projection=plot_wcs)
 
-#Plot each segment of the orbital path
+# Plot each segment of the orbital path
 for segment in orb_path:
     pixel_segment = segment.to_pixel(plot_wcs)
     pixel_segment.plot(ax=ax, color='k', lw=1, ls='-')
 
-#Overlay the exposure FoVs
+# Overlay the exposure FoVs
 for FoV in FoV_list:
     pixel_region = FoV.to_pixel(plot_wcs)
     pixel_region.plot(ax=ax, color='red', lw=1, ls='-')
 
-#Set the limits of the x and y axes with a little padding
+# Set the limits of the x and y axes with a little padding
 ax.set_xlim(np.array([-0.5,0.5])*x_npix + 0.2*min(x_npix,y_npix)*np.array([-1,1]))
 ax.set_ylim(np.array([-0.5,0.5])*y_npix + 0.2*min(x_npix,y_npix)*np.array([-1,1]))
 ax.set_aspect('equal')
 
-#Set axes labels
+# Set axes labels
 ax.set_xlabel('RA')
 ax.set_ylabel('Dec')
 ```
@@ -284,9 +284,9 @@ jupyter:
 ---
 def plot_path(most_output):
     '''
-    Function to plot the path of a moving object 
+    Function to plot the path of a moving object
     with the FoVs of intersecting exposures overlaid.
-    
+
     Parameters
     ----------
     most_output: dictionary
@@ -298,107 +298,107 @@ def plot_path(most_output):
 
     FoV_list = []
     for row in most_output['metadata']:
-        vertices = SkyCoord([row['ra1'], row['ra2'], row['ra3'], row['ra4']], 
+        vertices = SkyCoord([row['ra1'], row['ra2'], row['ra3'], row['ra4']],
                             [row['dec1'], row['dec2'], row['dec3'], row['dec4']], unit=u.deg)
         FoV_list.append(PolygonSkyRegion(vertices=vertices))
-    
-    #Check for a zero crossing
+
+    # Check for a zero crossing
     RA_zero_cross = False
-    for i,segment in enumerate(orb_path): 
+    for i,segment in enumerate(orb_path):
         if abs(segment.end.ra.deg-segment.start.ra.deg) > 180:
             print("Caution: Likely RA = 0 crossing.")
             RA_zero_cross = True
             break
-    
-    #If there is a zero crossing then switch wrap to 180 deg
+
+    # If there is a zero crossing then switch wrap to 180 deg
     if RA_zero_cross:
-        for i,segment in enumerate(orb_path): 
+        for i,segment in enumerate(orb_path):
             segment.start.ra.wrap_at(180*u.deg, inplace=True)
         RA_zero_cross = False
 
-    #Extract all RA and Dec values of segments
+    # Extract all RA and Dec values of segments
     ra_list = np.zeros(len(orb_path)*2)
     dec_list = np.zeros(len(orb_path)*2)
-    for i,segment in enumerate(orb_path): 
+    for i,segment in enumerate(orb_path):
         ra_list[2*i] = segment.start.ra.deg
         ra_list[2*i+1] = segment.end.ra.deg
         dec_list[2*i] = segment.start.dec.deg
         dec_list[2*i+1] = segment.end.dec.deg
 
-    #Find extremes of RA and Dec
+    # Find extremes of RA and Dec
     ra_min = min(ra_list)
     ra_max = max(ra_list)
     dec_min = min(dec_list)
     dec_max = max(dec_list)
 
 
-    #Initialize 2D WCS
+    # Initialize 2D WCS
     plot_wcs = WCS(naxis=2)
     plot_wcs.wcs.crpix = [0,0]
     plot_wcs.wcs.ctype = ["RA---SIN", "DEC--SIN"]
-    
-    #Set pixel size as 1 arcmin
+
+    # Set pixel size as 1 arcmin
     plot_wcs.wcs.cdelt = [-1./60., 1/60.]
-    
-    #Determine the position of plot center (midway between extremes)
+
+    # Determine the position of plot center (midway between extremes)
     extremes = SkyCoord(ra=[ra_min,ra_max],dec=[dec_min,dec_max],unit=u.deg)
     separation = extremes[0].separation(extremes[1])
     pa = extremes[0].position_angle(extremes[1])
     midpoint = extremes[0].directional_offset_by(pa, separation/2)
-    
-    #Set coordinates of WCS center
+
+    # Set coordinates of WCS center
     plot_wcs.wcs.crval = [midpoint.ra.deg,midpoint.dec.deg]
 
 
-    #Determine the range of RA and Dec values
-    #in order to set the number of pixel on each axis
+    # Determine the range of RA and Dec values
+    # in order to set the number of pixel on each axis
     ra_sep = separation.deg*abs(np.sin(pa))
     dec_sep = separation.deg*abs(np.cos(pa))
     x_npix = max(60,abs(ra_sep/plot_wcs.wcs.cdelt[0]))
     y_npix = max(60,abs(dec_sep/plot_wcs.wcs.cdelt[1]))
-    
-    #Don't allow aspect ratio to exceed 2:1
+
+    # Don't allow aspect ratio to exceed 2:1
     if not 1/2 < x_npix/y_npix < 2:
         if x_npix > y_npix:
             y_npix = x_npix/2
         else:
             x_npix = y_npix/2
-    
-    #Initialize the figure, setting its dimensions
+
+    # Initialize the figure, setting its dimensions
     if x_npix > y_npix:
         fig = plt.figure(figsize=(8,8*y_npix/x_npix))
     else:
         fig = plt.figure(figsize=(8*x_npix/y_npix,8))
-    
-    #Set the axis projection with the WCS
+
+    # Set the axis projection with the WCS
     ax = fig.add_subplot(projection=plot_wcs)
-    
-    #Plot each segment of the orbital path
+
+    # Plot each segment of the orbital path
     for segment in orb_path:
         pixel_segment = segment.to_pixel(plot_wcs)
         pixel_segment.plot(ax=ax, color='k', lw=1, ls='-')
-    
-    #Overlay the exposure FoVs
+
+    # Overlay the exposure FoVs
     for FoV in FoV_list:
         pixel_region = FoV.to_pixel(plot_wcs)
         pixel_region.plot(ax=ax, color='red', lw=1, ls='-')
-    
-    #Set the limits of the x and y axes with a little padding
+
+    # Set the limits of the x and y axes with a little padding
     ax.set_xlim(np.array([-0.5,0.5])*x_npix + 0.2*min(x_npix,y_npix)*np.array([-1,1]))
     ax.set_ylim(np.array([-0.5,0.5])*y_npix + 0.2*min(x_npix,y_npix)*np.array([-1,1]))
     ax.set_aspect('equal')
-    
-    #Set axes labels
+
+    # Set axes labels
     ax.set_xlabel('RA')
     ax.set_ylabel('Dec')
 
-    #Show plot
+    # Show plot
     plt.show()
 ```
 
 ### 2.5 Plotting orbital path over a very wide field
 
-If, for example, you enter a date range than spans many months or even years, then the plotting method above (which uses a local sine projection) will likely fail and return a nonsensical plot. 
+If, for example, you enter a date range than spans many months or even years, then the plotting method above (which uses a local sine projection) will likely fail and return a nonsensical plot.
 In these cases you can instead use a Mollweide projection to plot the entire sky.
 
 For example, if we greatly expand the date range on the original query:
@@ -409,48 +409,48 @@ most_long_output = Most.query_object(output_mode="Regular",obj_name="Nemesis",
 ```
 
 ```{code-cell} ipython3
-#Extract the orbital path from the query results
+# Extract the orbital path from the query results
 long_orb_path = Regions.read(most_long_output['region'], format='ds9')
 
-#Extract the FoVs of the overlapping images
+# Extract the FoVs of the overlapping images
 FoV_list = []
 for row in most_long_output['metadata']:
-    vertices = SkyCoord([row['ra1'], row['ra2'], row['ra3'], row['ra4']], 
+    vertices = SkyCoord([row['ra1'], row['ra2'], row['ra3'], row['ra4']],
                         [row['dec1'], row['dec2'], row['dec3'], row['dec4']], unit=u.deg)
     FoV_list.append(PolygonSkyRegion(vertices=vertices))
 
-#Set up the figure and projection
+# Set up the figure and projection
 fig = plt.figure(figsize=(8, 6))
 ax = fig.add_subplot(111, projection="mollweide")
 
-#Manually set/label the RA values
+# Manually set/label the RA values
 ax.set_xticklabels(["10h", "8h", "6h", "4h", "2h", "0h", "22h", "20h", "18h", "16h", "14h"])
 
-#Overlay a coordinate grid
+# Overlay a coordinate grid
 ax.grid(True,alpha=0.5)
 
-#Plot each segment of the orbital path
+# Plot each segment of the orbital path
 for segment in long_orb_path:
-    #Exclude any segments that would cross the entire projection
+    # Exclude any segments that would cross the entire projection
     if (segment.start.ra.wrap_at(360 * u.deg).deg < 180 and segment.end.ra.wrap_at(360 * u.deg).deg >= 180):
         pass
     elif (segment.start.ra.wrap_at(360 * u.deg).deg >= 180 and segment.end.ra.wrap_at(360 * u.deg).deg < 180):
         pass
     else:
-        #The RA values need to be made negative such that they plot correctly
-        ax.plot([-segment.start.ra.wrap_at(180 * u.deg).radian,-segment.end.ra.wrap_at(180 * u.deg).radian], 
+        # The RA values need to be made negative such that they plot correctly
+        ax.plot([-segment.start.ra.wrap_at(180 * u.deg).radian,-segment.end.ra.wrap_at(180 * u.deg).radian],
                 [segment.start.dec.radian,segment.end.dec.radian], color='k', lw=1, ls='-', zorder=1)
 
-#Overlay the exposure FoVs
+# Overlay the exposure FoVs
 for FoV in FoV_list:
-    FoV_patch = Polygon(list(zip(-FoV.vertices.ra.radian,FoV.vertices.dec.radian)), 
+    FoV_patch = Polygon(list(zip(-FoV.vertices.ra.radian,FoV.vertices.dec.radian)),
                         closed=True, facecolor=None, edgecolor='r',zorder=2)
     ax.add_patch(FoV_patch)
 ```
 
 ## 3. Displaying an image and identifying the moving object
 
-We see above that the exposures listed in the results table do indeed intersect with the orbital path of Nemesis. 
+We see above that the exposures listed in the results table do indeed intersect with the orbital path of Nemesis.
 As this is a fairly large and nearby asteroid, it should be visible in the single NEOWISE exposures identified.
 
 +++
@@ -458,7 +458,7 @@ As this is a fairly large and nearby asteroid, it should be visible in the singl
 ### 3.1 Displaying a full image
 
 Images can be retrieved interactively by using the solar system object tab of the mission GUIs from [WISE](https://irsa.ipac.caltech.edu/applications/wise), [ZTF](https://irsa.ipac.caltech.edu/applications/ztf), and [Spitzer](https://irsa.ipac.caltech.edu/applications/Spitzer/SHA).
-However, if you wish to do this in a script or notebook, then these can also be accessed programatically.
+However, if you wish to do this in a script or notebook, then these can also be accessed programmatically.
 
 Start by taking the URL of the first image listed in the results table.
 
@@ -467,23 +467,23 @@ img_access_url = most_output['results']['image_url'][0]
 print(img_access_url)
 ```
 
-Even though the FITS file is located on a remote server it can be opened with the astropy `fits` package exactly as if it were a local file by passing the URL in place of the local file path. 
+Even though the FITS file is located on a remote server it can be opened with the astropy `fits` package exactly as if it were a local file by passing the URL in place of the local file path.
 
 :::{note}
 The following cell will likely take several seconds to run as here we download the entire image to work with it locally.
 :::
 
 ```{code-cell} ipython3
-#Open FITS file
+# Open FITS file
 hdul = fits.open(img_access_url)
 
-#Extract header
+# Extract header
 header = hdul[0].header
 
-#Extract image data
+# Extract image data
 image = hdul[0].data
 
-#Construct WCS from header
+# Construct WCS from header
 img_wcs = WCS(header)
 ```
 
@@ -493,32 +493,32 @@ We will also want to locate the target object on the image, so we should extract
 ra_obj = most_output['results']['ra_obj'][0]*u.deg
 dec_obj = most_output['results']['dec_obj'][0]*u.deg
 
-#Create SkyCoord object of object position
+# Create SkyCoord object of object position
 pos_obj = SkyCoord(ra=ra_obj, dec=dec_obj)
 
-#Convert to pixel location via the WCS
+# Convert to pixel location via the WCS
 pix_pos_obj = pos_obj.to_pixel(img_wcs)
 ```
 
 Now display the image and point to the object.
 
 ```{code-cell} ipython3
-#Initialize figure
+# Initialize figure
 fig = plt.figure(figsize=(8,8))
 ax = fig.add_subplot(projection=img_wcs)
 
-#Use the ZScale interval (similar to DS9) to normalize the intensity
+# Use the ZScale interval (similar to DS9) to normalize the intensity
 norm = ImageNormalize(image, interval=ZScaleInterval())
 
-#Display the figure
+# Display the figure
 ax.imshow(image,origin='lower',norm=norm,cmap='Greys_r')
 
-#Add orange arrow pointing to object
+# Add orange arrow pointing to object
 ax.annotate('',pix_pos_obj-0.01*np.array([header['NAXIS1'],header['NAXIS2']]),
             pix_pos_obj-0.1*np.array([header['NAXIS1'],header['NAXIS2']]),
             arrowprops={'width':2, 'facecolor':'orange', 'edgecolor':'none'})
 
-#Label axes and overlay coordinate grid
+# Label axes and overlay coordinate grid
 ax.set_xlabel('RA')
 ax.set_ylabel('Dec')
 ax.coords.grid(color='lime', linestyle='solid', alpha=0.3)
@@ -531,7 +531,7 @@ There does appear to be a bright object indicated by the arrow, but really we ne
 ### 3.2 Making cutouts and comparing to the AllWISE Atlas
 
 We could display the full image for every exposure listed in the results table, however, this would involve downloading a lot of data when really we're only interested in a small region immediately around where we expect to find the target object.
-Below we will use the astropy `Cutout2D` function to extract cutouts from the remote images without downloading the full FITS file. 
+Below we will use the astropy `Cutout2D` function to extract cutouts from the remote images without downloading the full FITS file.
 This will allow us to see the portions of the images that are most relevant, while saving a lot of bandwidth (and speeding up the execution).
 
 :::{note}
@@ -540,24 +540,24 @@ MOST queries search for all WISE images by default, but they can return images f
 If you alter this notebook to query another image catalog, then the following cells will likely fail to run as intended.
 :::
 
-To start we will order the `'metadata'` table by observation date (MJD) and then by band, so that images appear in the order they were observed and with shorter wavelength images appearing first. 
-This adds some complications to the following code, but will aid the interpretation of the cutouts. 
+To start we will order the `'metadata'` table by observation date (MJD) and then by band, so that images appear in the order they were observed and with shorter wavelength images appearing first.
+This adds some complications to the following code, but will aid the interpretation of the cutouts.
 In particular, as we are re-ordering one of the tables, we must now use the `'image_ID'` column in the `'results'` table to relate the rows in the `'metadata'` table to those in the `'results'` table.
 
 ```{code-cell} ipython3
-#Duplicate metadata table
+# Duplicate metadata table
 ordered_metadata = most_output['metadata']
 
-#Sort by date then band
+# Sort by date then band
 ordered_metadata.sort(['mjd_obs','band'])
 
-#Construct the image IDs to relate rows to those in the results table
+# Construct the image IDs to relate rows to those in the results table
 imgIDs = []
 for i in range(len(ordered_metadata)):
     imgIDs.append(f"{ordered_metadata[i]['scan_id']}_{ordered_metadata[i]['frame_num']}_{ordered_metadata[i]['band']}")
 ordered_metadata['Image_ID'] = imgIDs
 
-#Duplicate results table and index by image ID
+# Duplicate results table and index by image ID
 indexed_results = most_output['results']
 indexed_results.add_index('Image_ID')
 ```
@@ -575,7 +575,7 @@ jupyter:
 def exposure_cutout(image_url,cutout_center,cutout_size):
     '''
     Function to make FITS image cutout.
-    
+
     Parameters
     ----------
     image_url: string
@@ -591,13 +591,13 @@ def exposure_cutout(image_url,cutout_center,cutout_size):
     cutout: Cutout2D object
         Image cutout, including data, WCS, and shape.
     norm: ImageNormalize object
-        Normalization of cutout data for plotting 
+        Normalization of cutout data for plotting
         with matplotlib.
     header: FITS Header object
         FITS header of the parent image.
     '''
 
-    #Open remote FITS file and extract cutout
+    # Open remote FITS file and extract cutout
     with fits.open(image_url, use_fsspec=True) as hdul:
         header = hdul[0].header
         img_wcs = WCS(header)
@@ -605,7 +605,7 @@ def exposure_cutout(image_url,cutout_center,cutout_size):
                           size=cutout_size.to(u.arcsec).value/abs(header['PXSCAL2']),
                           wcs=img_wcs,mode='partial', fill_value=np.nan)
 
-    #Normalize cutout intensity
+    # Normalize cutout intensity
     norm = ImageNormalize(cutout.data, interval=ZScaleInterval())
 
     return cutout, norm, header
@@ -616,7 +616,7 @@ def reference_cutout(cutout_center,cutout,cutout_size,centwave):
     Function to make FITS image cutout from AllWISE
     at specified location, and match projection of
     an existing cutout.
-    
+
     Parameters
     ----------
     cutout_center: SkyCoord object
@@ -634,40 +634,40 @@ def reference_cutout(cutout_center,cutout,cutout_size,centwave):
     ref_cutout: Cutout2D object
         Image cutout, including data, WCS, and shape.
     norm: ImageNormalize object
-        Normalization of cutout data for plotting 
+        Normalization of cutout data for plotting
         with matplotlib.
     header: FITS Header object
         FITS header of the parent image.
     '''
 
-    #Query the IRSA SIAv2 service for the AllWISE image at this position 
-    allwise_img_tab = Irsa.query_sia(pos=(cutout_center, 1 * u.arcmin), 
+    # Query the IRSA SIAv2 service for the AllWISE image at this position
+    allwise_img_tab = Irsa.query_sia(pos=(cutout_center, 1 * u.arcmin),
                                      collection='wise_allwise', band=centwave)
 
-    #Get access URL from the query results
+    # Get access URL from the query results
     allwise_img_url = allwise_img_tab[allwise_img_tab['dataproduct_subtype'] == 'science'][0]['access_url']
 
-    #Open the image and extract a cutout
-    #Extract a larger cutout than above as it will need to be reprojected
+    # Open the image and extract a cutout
+    # Extract a larger cutout than above as it will need to be reprojected
     with fits.open(allwise_img_url, use_fsspec=True) as hdul:
         header = hdul[0].header
         img_wcs = WCS(header)
         ref_cutout = Cutout2D(hdul[0].section, position=cutout_center,size=2*cutout_size,wcs=img_wcs,
                               mode='partial', fill_value=np.nan)
 
-    #Use the reproject function to project the reference cutout
-    #to the same WCS as the target cutout
+    # Use the reproject function to project the reference cutout
+    # to the same WCS as the target cutout
     ref_cutout, footprint = reproject_interp(input_data=(ref_cutout.data, ref_cutout.wcs),
                                              output_projection=cutout.wcs,
                                              shape_out=cutout.shape)
 
-    #Normalize the intensity and display
+    # Normalize the intensity and display
     norm = ImageNormalize(ref_cutout.data, interval=ZScaleInterval())
 
     return ref_cutout, norm, header
 ```
 
-Now we are ready to make and display the cutouts. 
+Now we are ready to make and display the cutouts.
 
 The following cell sets up a grid of subplots based on the number of exposures that were found in the query and the number of unique bands.
 A loop cycles through all of the blank subplots in the grid and a cutout is generated from each image listed in the `'metadata'` table at the expected location of the moving object.
@@ -679,46 +679,46 @@ This cell will likely take a minute or two to run, as it must access and create
 :::
 
 ```{code-cell} ipython3
-#Set number of bands (max of 4 for WISE)
+# Set number of bands (max of 4 for WISE)
 n_band = min(4,len(set(ordered_metadata['band'])))
 band_names = list(set(ordered_metadata['band']))
 band_names.sort()
 
-#Set the number of images (max set at 10)
+# Set the number of images (max set at 10)
 n_img = min(10,len(set(ordered_metadata['scan_id'])))
 
-#Set the WISE band center wavelengths (m)
-#Used in SIAv2 query to return the correct band
+# Set the WISE band center wavelengths (m)
+# Used in SIAv2 query to return the correct band
 wise_bands_centwave = [3.4E-6,4.6E-6,1.2E-5,2.2E-5]
 
-#Initialize the subplot array
+# Initialize the subplot array
 fig, ax_arr = plt.subplots(nrows=n_img, ncols=2*n_band,
                            figsize=2*np.array([2*n_band,n_img]))
 
-#Hide all axes
+# Hide all axes
 for i in range(n_img):
     for j in range(2*n_band):
         ax = ax_arr[i][j]
         ax.set_axis_off()
 
-#Set the cutout size
+# Set the cutout size
 cutout_size = 5*u.arcmin
 
 
-#Initialize a counter to keep track of
-#the number of cutouts displayed
+# Initialize a counter to keep track of
+# the number of cutouts displayed
 counter = 0
 
-#Save the ID of the previous scan in the loop
-#initialize with the first ID
+# Save the ID of the previous scan in the loop
+# initialize with the first ID
 prev_scanID = ordered_metadata['scan_id'][0]
 
-#Loop over the grid
+# Loop over the grid
 for i in range(n_img):
     for j in range(n_band):
 
-        #Check if the current scan ID is the same as the previous
-        #If not, then go to the next row of the plot grid
+        # Check if the current scan ID is the same as the previous
+        # If not, then go to the next row of the plot grid
         scanID = ordered_metadata['scan_id'][counter]
         if (scanID not in prev_scanID) and (j != 0):
             prev_scanID = copy.deepcopy(scanID)
@@ -726,53 +726,53 @@ for i in range(n_img):
         else:
             prev_scanID = copy.deepcopy(scanID)
 
-        #Extract position of target object
+        # Extract position of target object
         ra_obj = ordered_metadata['ra_obj'][counter]*u.deg
         dec_obj = ordered_metadata['dec_obj'][counter]*u.deg
         pos_obj = SkyCoord(ra=ra_obj, dec=dec_obj)
 
-        #Set axis for displaying cutout
+        # Set axis for displaying cutout
         ax = ax_arr[i][2*j]
 
-        #Create single exposure cutout
+        # Create single exposure cutout
         cutout, norm, header = exposure_cutout(indexed_results.loc[ordered_metadata['Image_ID'][counter]]['image_url'],
                                        pos_obj,cutout_size)
 
-        #Display on figure
+        # Display on figure
         ax.imshow(cutout.data,origin='lower',norm=norm,cmap='Greys_r')
 
-        #Add title indicating the band and date
+        # Add title indicating the band and date
         ax.set_title(f"Band: W{band_names[j]}\nMJD: {np.round(header['MJD_OBS'], 2)}")
 
 
-        #Now make reference cutout
+        # Now make reference cutout
 
-        #Set axis for displaying cutout
+        # Set axis for displaying cutout
         ax = ax_arr[i][2*j+1]
 
-        #Create matching AllWISE reference cutout
+        # Create matching AllWISE reference cutout
         ref_cutout, norm, header = reference_cutout(pos_obj,cutout,cutout_size,wise_bands_centwave[j])
 
-        #Display on figure
+        # Display on figure
         ax.imshow(ref_cutout.data,origin='lower',norm=norm,cmap='Greys_r')
 
-        #Add title indicating the band and date
+        # Add title indicating the band and date
         ax.set_title(f'Band: W{band_names[j]}\nAllWISE')
 
-        #Increment counter
+        # Increment counter
         counter += 1
 
 plt.tight_layout()
 ```
 
 The asteroid is clearly visible as a bright point source at the center of each of the single exposure cutouts, but is absent from the AllWISE reference cutouts.
-Furthermore, the source moves from frame to frame, relative to the background sources, confirming that it is a moving object and the cutouts are correctly centered. 
-That it always falls in the center of the cutout indicates that its orbital parameters are well-known and its position can be predicted accurately. 
+Furthermore, the source moves from frame to frame, relative to the background sources, confirming that it is a moving object and the cutouts are correctly centered.
+That it always falls in the center of the cutout indicates that its orbital parameters are well-known and its position can be predicted accurately.
 In less clear cut cases it may be useful to also explore the AllWISE source catalog along with the cutouts.
 This can be done interactively by submitting a solar system object query in the [IRSA WISE interface](https://irsa.ipac.caltech.edu/applications/wise).
 
 :::{note}
-The axes of the cutouts are aligned with whatever the orientation of the spacecraft was when the exposures were taken, not the cardinal directions. 
+The axes of the cutouts are aligned with whatever the orientation of the spacecraft was when the exposures were taken, not the cardinal directions.
 We have chosen to reproject the AllWISE cutouts to the match this orientation, rather than the reverse, because the single exposures contain many hot pixels and other artifacts that will grow in severity if not corrected before reprojection.
 ::::
 
@@ -788,7 +788,7 @@ def plot_cutouts(most_output, return_stack=False):
     Function to plot (NEO)WISE cutouts identifed in a
     MOST query_object query. Up to a maximum of 10 cutouts
     in each band. Reference AllWISE cutouts are also plotted.
-    
+
     Parameters
     ----------
     most_output: dictionary
@@ -802,118 +802,118 @@ def plot_cutouts(most_output, return_stack=False):
     stack: list
         A list of numpy.array objects containing the pixel
         values of the cutouts (in nanomaggies). Only returned
-        if return_stack parameter is True. 
-        The list contains lists of arrays 
+        if return_stack parameter is True.
+        The list contains lists of arrays
     '''
 
-    #Duplicate metadata table
+    # Duplicate metadata table
     ordered_metadata = most_output['metadata']
-    
-    #Sort by date then band
+
+    # Sort by date then band
     ordered_metadata.sort(['mjd_obs','band'])
-    
-    #Construct the image IDs to relate rows to those in the results table
+
+    # Construct the image IDs to relate rows to those in the results table
     imgIDs = []
     for i in range(len(ordered_metadata)):
         imgIDs.append(f"{ordered_metadata[i]['scan_id']}_{ordered_metadata[i]['frame_num']}_{ordered_metadata[i]['band']}")
     ordered_metadata['Image_ID'] = imgIDs
-    
-    #Duplicate results table and index by image ID
+
+    # Duplicate results table and index by image ID
     indexed_results = most_output['results']
     indexed_results.add_index('Image_ID')
 
 
-    #Set number of bands (max of 4 for WISE)
+    # Set number of bands (max of 4 for WISE)
     n_band = min(4,len(set(ordered_metadata['band'])))
     band_names = list(set(ordered_metadata['band']))
     band_names.sort()
-    
-    #Set the number of images (max set at 10)
+
+    # Set the number of images (max set at 10)
     n_img = min(10,len(set(ordered_metadata['scan_id'])))
-    
-    #Set the WISE band center wavelengths (m)
-    #Used in SIAv2 query to return the correct band
+
+    # Set the WISE band center wavelengths (m)
+    # Used in SIAv2 query to return the correct band
     wise_bands_centwave = [3.4E-6,4.6E-6,1.2E-5,2.2E-5]
-    
-    #Initialize the subplot array
+
+    # Initialize the subplot array
     fig, ax_arr = plt.subplots(nrows=n_img, ncols=2*n_band,
                                figsize=2*np.array([2*n_band,n_img]))
-    
-    #Hide all axes
+
+    # Hide all axes
     for i in range(n_img):
         for j in range(2*n_band):
             ax = ax_arr[i][j]
             ax.set_axis_off()
-    
-    #Set the cutout size
+
+    # Set the cutout size
     cutout_size = 5*u.arcmin
-    
-    
-    #Initialize a counter to keep track of
-    #the number of cutouts displayed
+
+
+    # Initialize a counter to keep track of
+    # the number of cutouts displayed
     counter = 0
-    
-    #Save the ID of the previous scan in the loop
-    #initialize with the first ID
+
+    # Save the ID of the previous scan in the loop
+    # initialize with the first ID
     prev_scanID = ordered_metadata['scan_id'][0]
-    
-    #Loop over the grid
+
+    # Loop over the grid
     for i in range(n_img):
         for j in range(n_band):
-    
-            #Check if the current scan ID is the same as the previous
-            #If not, then go to the next row of the plot grid
+
+            # Check if the current scan ID is the same as the previous
+            # If not, then go to the next row of the plot grid
             scanID = ordered_metadata['scan_id'][counter]
             if (scanID not in prev_scanID) and (j != 0):
                 prev_scanID = copy.deepcopy(scanID)
                 break
             else:
                 prev_scanID = copy.deepcopy(scanID)
-    
-            #Extract position of target object
+
+            # Extract position of target object
             ra_obj = ordered_metadata['ra_obj'][counter]*u.deg
             dec_obj = ordered_metadata['dec_obj'][counter]*u.deg
             pos_obj = SkyCoord(ra=ra_obj, dec=dec_obj)
-    
-            #Set axis for displaying cutout
+
+            # Set axis for displaying cutout
             ax = ax_arr[i][2*j]
-    
-            #Create single exposure cutout
+
+            # Create single exposure cutout
             cutout, norm, header = exposure_cutout(indexed_results.loc[ordered_metadata['Image_ID'][counter]]['image_url'],
                                                    pos_obj,cutout_size)
 
-            #Display on figure
+            # Display on figure
             ax.imshow(cutout.data,origin='lower',norm=norm,cmap='Greys_r')
-    
-            #Add title indicating the band and date
+
+            # Add title indicating the band and date
             ax.set_title(f"Band: W{band_names[j]}\nMJD: {np.round(header['MJD_OBS'], 2)}")
 
-            #Check if stack already exists
+            # Check if stack already exists
             if 'stack' not in locals():
                 stack = np.zeros(shape=(2*n_band,n_img,cutout.shape[0],cutout.shape[1])) + np.nan
-            
-            #Place cutout in stack
+
+            # Place cutout in stack
             stack[2*j][i] = np.array(cutout.data)
 
-    
-            #Now make reference cutout
-    
-            #Set axis for displaying cutout
+
+            # Now make reference cutout
+
+            # Set axis for displaying cutout
             ax = ax_arr[i][2*j+1]
-    
-            #Create matching AllWISE reference cutout
+
+            # Create matching AllWISE reference cutout
             ref_cutout, norm, header = reference_cutout(pos_obj,cutout,cutout_size,wise_bands_centwave[j])
-    
-            #Display on figure
+
+            # Display on figure
             ax.imshow(ref_cutout.data,origin='lower',norm=norm,cmap='Greys_r')
-    
-            #Add title indicating the band and date
+
+            # Add title indicating the band and date
             ax.set_title(f'Band: W{band_names[j]}\nAllWISE')
 
-            #Place reference cutout in stack
+            # Place reference cutout in stack
             stack[2*j+1][i] = np.array(ref_cutout.data)
-    
-            #Increment counter
+
+            # Increment counter
             counter += 1
 
     plt.tight_layout()
@@ -928,7 +928,7 @@ In many cases the moving object searched for will not be visible in a single exp
 In these cases it can be useful to stack the exposures to gain sensitivity.
 
 :::{note}
-The cells below are intended purely to aid detection and should not be used for photometry, or other similar analysis, as this approach lacks proper background subtraction and outlier rejection. 
+The cells below are intended purely to aid detection and should not be used for photometry, or other similar analysis, as this approach lacks proper background subtraction and outlier rejection.
 In the case of WISE, these can be done with the [WISE Coadder](https://irsa.ipac.caltech.edu/applications/ICORE/).
 ::::
 
@@ -953,7 +953,7 @@ plot_path(most_output)
 
 Now we can use the other function defined above to plot the first 10 cutouts in each band.
 
-An additional feature that was added in the function definition, but not shown in the example above, is the option to return a stack containing all of the cutouts. 
+An additional feature that was added in the function definition, but not shown in the example above, is the option to return a stack containing all of the cutouts.
 We will do this here as they will be needed below.
 
 ```{code-cell} ipython3
@@ -965,28 +965,28 @@ Although the eagle-eyed among us may be able to detect 2I/Borisov in some of the
 The cell below median combines the single exposure cutouts to boost sensitivity.
 
 ```{code-cell} ipython3
-#Set number of unique bands
+# Set number of unique bands
 n_bands = min(2,len(set(most_output['metadata']['band'])))
 
-#initialize the figure and axes
+# initialize the figure and axes
 fig, ax_arr = plt.subplots(nrows=1, ncols=n_bands,
                            figsize=2*np.array([n_bands,1]))
 
-#Hide all axes
+# Hide all axes
 for j in range(n_bands):
     ax = ax_arr[j]
     ax.set_axis_off()
 
-#Loop over all bands
+# Loop over all bands
 for j in range(n_bands):
 
-    #Set current axis
+    # Set current axis
     ax = ax_arr[j]
 
-    #Median combine the single exposure cutouts
+    # Median combine the single exposure cutouts
     band_stack = np.nanmedian(stack[2*j],axis=0)
 
-    #Normalize the intensity and display
+    # Normalize the intensity and display
     norm = ImageNormalize(band_stack, interval=ZScaleInterval())
     ax.imshow(band_stack,origin='lower',norm=norm,cmap='Greys_r')
     ax.set_title(f'W{j+1} target stack')
@@ -997,11 +997,11 @@ plt.tight_layout()
 2I/Borisov is now clearly visible in the W2 cutout and marginally detected in W1 as well.
 
 :::{caution}
-Remember that the `plot_cutouts` function that we defined above only returns the first 10 cutouts in each band. 
-However, in this case there are more images returned in the initial query. 
+Remember that the `plot_cutouts` function that we defined above only returns the first 10 cutouts in each band.
+However, in this case there are more images returned in the initial query.
 These additional images are *not* included in the stacks.
 
-In addition, we recommend that these stacks only be used for a cursory inspection of the data. 
+In addition, we recommend that these stacks only be used for a cursory inspection of the data.
 The `Cutout2D` function simply extracts a pixel array from the full FITS image, and does not perform rebinning or reprojection.
 Therefore, cutout alignments are only accurate to the nearest pixel.
 Similarly, the rejection of artifacts relies purely on a crude median combination.
@@ -1040,43 +1040,43 @@ plot_path(most_output)
 A cutout can be produced in much the same way as before, although minor changes are needed as the headers of the SPHEREx images differ from those of WISE.
 
 ```{code-cell} ipython3
-#Get the URL for the first image in the MOST output
+# Get the URL for the first image in the MOST output
 img_access_url = most_output['results']['image_url'][0]
 print(img_access_url)
 
-#Get the position of target corresponding to the first image
+# Get the position of target corresponding to the first image
 ra_obj = most_output['results']['ra_obj'][0]*u.deg
 dec_obj = most_output['results']['dec_obj'][0]*u.deg
 
-#Create SkyCoord object of object position
+# Create SkyCoord object of object position
 pos_obj = SkyCoord(ra=ra_obj, dec=dec_obj)
 
-#Set cutout size
+# Set cutout size
 cutout_size = 5*u.arcmin
 
-#Open remote FITS file and extract cutout
+# Open remote FITS file and extract cutout
 with fits.open(img_access_url, use_fsspec=True) as hdul:
     header = hdul[1].header
     pixel_scale = np.sqrt(header['PC1_1']**2 + header['PC1_2']**2)
     img_wcs = WCS(header)
-    
+
     cutout = Cutout2D(hdul[1].data, position=pos_obj,
                       size=cutout_size.to(u.deg).value/pixel_scale,
                       wcs=img_wcs,mode='partial', fill_value=np.nan)
 
-    #Normalize cutout intensity
+    # Normalize cutout intensity
     norm = ImageNormalize(cutout.data, interval=ZScaleInterval())
 ```
 
 ```{code-cell} ipython3
-#Initialize figure
+# Initialize figure
 fig = plt.figure(figsize=(6,6))
 ax = fig.add_subplot(projection=cutout.wcs)
 
-#Display the figure
+# Display the figure
 ax.imshow(cutout.data,origin='lower',norm=norm,cmap='Greys_r')
 
-#Overlay coordinate grid
+# Overlay coordinate grid
 ax.set_xlabel('RA')
 ax.set_ylabel('Dec')
 ax.coords.grid(color='lime', linestyle='solid', alpha=0.3)
@@ -1084,7 +1084,7 @@ ax.coords.grid(color='lime', linestyle='solid', alpha=0.3)
 
 ## 6. New discoveries: Queries using orbital elements and the MPC one-line format
 
-Although in general it is strongly recommended to use object names to perform searches, in the rare case of a new discovery where the object is not yet in JPL Horizons, MOST allows the input of orbital elements or the one-line MPC format. 
+Although in general it is strongly recommended to use object names to perform searches, in the rare case of a new discovery where the object is not yet in JPL Horizons, MOST allows the input of orbital elements or the one-line MPC format.
 These are most effective for times close to the epoch of the elements.
 
 +++
@@ -1125,8 +1125,8 @@ Several epochs of orbit parameters are available for 2I/Borisov and its orbit ch
 If we look for the one line MPCORB database entry for 2I/Borisov, it will list only the paramters for the latest epoch.
 However, these will not be appropriate for locating it in January 2020, which is when we wish to search for intersecting images.
 
-To find the orbital parameters for prior epochs, search for "2I" on the [MPC Database Search](https://minorplanetcenter.net/db_search) page. 
-In this case the epoch including Jan. 2020 is 2019-12-23.0, and we will use the parameters from this epoch in our query below. 
+To find the orbital parameters for prior epochs, search for "2I" on the [MPC Database Search](https://minorplanetcenter.net/db_search) page.
+In this case the epoch including Jan. 2020 is 2019-12-23.0, and we will use the parameters from this epoch in our query below.
 The orbital parameters listed can then be entered into the `query_object` function with the `input_type` set as `"manual_input"` and `obj_type` as `"Comet"`, as follows:
 
 ```{code-cell} ipython3

From 8a8a949179c37fb03121d23d1fd42d3f7d559e8f Mon Sep 17 00:00:00 2001
From: Troy Raen <raen@ipac.caltech.edu>
Date: Thu, 25 Jun 2026 11:36:25 -0700
Subject: [PATCH 2/5] Add MOST_queries.md to deployment

---
 deploy_to_fornax_manifest.in                 |  1 +
 notebook_metadata.yml                        |  4 ++++
 toc.yml                                      | 10 +++++-----
 tutorials/techniques-and-tools/techniques.md |  3 ++-
 4 files changed, 12 insertions(+), 6 deletions(-)

diff --git a/deploy_to_fornax_manifest.in b/deploy_to_fornax_manifest.in
index 1023a1b4..cfddb957 100644
--- a/deploy_to_fornax_manifest.in
+++ b/deploy_to_fornax_manifest.in
@@ -38,6 +38,7 @@ tutorials/spherex/spherex_mosaic.md
 tutorials/techniques-and-tools/cloud-access-intro.md
 tutorials/techniques-and-tools/Parallelize_Convolution.md
 tutorials/techniques-and-tools/irsa-hats-with-lsdb.md
+tutorials/techniques-and-tools/MOST_queries.md
 tutorials/techniques-and-tools/SEDs_in_Firefly.md
 tutorials/wise/NEOWISE_light_curve_demo.md
 tutorials/wise/neowise-source-table-lightcurves.md
diff --git a/notebook_metadata.yml b/notebook_metadata.yml
index c61867f8..bdb635cb 100644
--- a/notebook_metadata.yml
+++ b/notebook_metadata.yml
@@ -114,6 +114,10 @@
   file: tutorials/techniques-and-tools/irsa-hats-with-lsdb.md
   section: Techniques and Tools
   description: Use LSDB for user-friendly cross matching and querying of HATS Collections.
+- title: Moving Object Searches
+  file: tutorials/techniques-and-tools/MOST_queries.md
+  section: Techniques and Tools
+  description: Run MOST queries to find images of moving objects, retrieve cutouts, and stack exposures to boost detection.
 - title: NEOWISE Solar System Objects
   file: tutorials/wise/NEOWISE_light_curve_demo.md
   section: WISE
diff --git a/toc.yml b/toc.yml
index 9029d666..e916a875 100644
--- a/toc.yml
+++ b/toc.yml
@@ -65,7 +65,7 @@ project:
       children:
         - title: Roman HLSS Number Density
           file: tutorials/simulated-data/roman_hlss_number_density.md
-        - title: OpenUniverse2024 Roman coadds
+        - title: OpenUniverse2024 Roman Coadds
           file: tutorials/simulated-data/OpenUniverse2024/openuniverse2024_roman_simulated_wideareasurvey.md
         - title: OpenUniverse2024 Visualization
           file: tutorials/simulated-data/OpenUniverse2024/OpenUniverse2024Preview_Firefly.md
@@ -86,9 +86,9 @@ project:
           file: tutorials/techniques-and-tools/cloud-access-intro.md
         - title: LSDB HATS
           file: tutorials/techniques-and-tools/irsa-hats-with-lsdb.md
-        - title: SED visualization
-          file: tutorials/techniques-and-tools/SEDs_in_Firefly.md
+        - title: Moving Object Searches
+          file: tutorials/techniques-and-tools/MOST_queries.md
         - title: Parallelization
           file: tutorials/techniques-and-tools/Parallelize_Convolution.md
-        - title: Moving object searches
-          file: tutorials/techniques-and-tools/MOST_queries.md
+        - title: SED Visualization
+          file: tutorials/techniques-and-tools/SEDs_in_Firefly.md
diff --git a/tutorials/techniques-and-tools/techniques.md b/tutorials/techniques-and-tools/techniques.md
index 9c2b43fd..ed78096d 100644
--- a/tutorials/techniques-and-tools/techniques.md
+++ b/tutorials/techniques-and-tools/techniques.md
@@ -6,6 +6,7 @@ This collection includes guidance on cloud-based data access, use of powerful bi
 ```{notebook-gallery} notebook_metadata.yml
 tutorials/techniques-and-tools/cloud-access-intro.md
 tutorials/techniques-and-tools/irsa-hats-with-lsdb.md
-tutorials/techniques-and-tools/SEDs_in_Firefly.md
+tutorials/techniques-and-tools/MOST_queries.md
 tutorials/techniques-and-tools/Parallelize_Convolution.md
+tutorials/techniques-and-tools/SEDs_in_Firefly.md
 ```

From 807d85adbe364b136d7393c91ba0ba0a09c03241 Mon Sep 17 00:00:00 2001
From: Troy Raen <raen@ipac.caltech.edu>
Date: Thu, 25 Jun 2026 17:35:16 -0700
Subject: [PATCH 3/5] Install all required dependencies

---
 tutorials/techniques-and-tools/MOST_queries.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tutorials/techniques-and-tools/MOST_queries.md b/tutorials/techniques-and-tools/MOST_queries.md
index 113219cb..294efceb 100644
--- a/tutorials/techniques-and-tools/MOST_queries.md
+++ b/tutorials/techniques-and-tools/MOST_queries.md
@@ -38,7 +38,7 @@ Metadata tables containing the location of the target as a function of time and
 
 ```{code-cell} ipython3
 # Uncomment the next line to install dependencies if needed.
-# !pip install astropy 'astroquery>=0.4.10' regions reproject
+# !pip install aiohttp 'astropy>=7.0.2' 'astroquery>=0.4.10' matplotlib numpy regions reproject requests
 ```
 
 ```{code-cell} ipython3

From 149b721bbe3a73c1add62e24aee1c7984bfad5bf Mon Sep 17 00:00:00 2001
From: Troy Raen <raen@ipac.caltech.edu>
Date: Thu, 25 Jun 2026 18:32:33 -0700
Subject: [PATCH 4/5] Ignore Most_queries.md in oldestdeps test

---
 tox.ini | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/tox.ini b/tox.ini
index ce996336..a2739789 100644
--- a/tox.ini
+++ b/tox.ini
@@ -52,7 +52,8 @@ install_command =
     # lsdb has tighter minimum dependencies, deal with it here for now, long term handle it from the notebook metadata
     # We need to do this here before the dependencies are installed to work around deps conflicts
     # SED fitting notebook uses numpy 2.0+ functionality, ignore it from the oldest job
-    oldestdeps: bash -c "echo tutorials/techniques-and-tools/irsa-hats-with-lsdb >> ignore_testing; echo tutorials/simulated-data/OpenUniverse2024/openuniverse2024_SED_fit.md >> ignore_testing; sed -i -e 's|lsdb|\#lsdb|g' tutorial_requirements.txt && python -I -m pip install $@"
+    # MOST notebook requires astropy>=7.0.2 (for https://github.com/astropy/astropy/pull/17611), so ignore it in oldestdeps job
+    oldestdeps: bash -c "echo tutorials/techniques-and-tools/irsa-hats-with-lsdb >> ignore_testing; echo tutorials/simulated-data/OpenUniverse2024/openuniverse2024_SED_fit.md >> ignore_testing; echo tutorials/techniques-and-tools/MOST_queries.md >> ignore_testing; sed -i -e 's|lsdb|\#lsdb|g' tutorial_requirements.txt && python -I -m pip install $@"
 
     # Adding back the default install command; commented out version for clear cases, more complex one if we need to add more conditional skips
     # !oldestdeps: python -I -m pip install {opts} {packages}

From 697940f541700c9830bddd61eb115819c132b8a4 Mon Sep 17 00:00:00 2001
From: Troy Raen <raen@ipac.caltech.edu>
Date: Thu, 25 Jun 2026 18:32:59 -0700
Subject: [PATCH 5/5] Bump to astropy>=7.2.0 to avoid #338

---
 tutorials/techniques-and-tools/MOST_queries.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tutorials/techniques-and-tools/MOST_queries.md b/tutorials/techniques-and-tools/MOST_queries.md
index 294efceb..70423741 100644
--- a/tutorials/techniques-and-tools/MOST_queries.md
+++ b/tutorials/techniques-and-tools/MOST_queries.md
@@ -38,7 +38,7 @@ Metadata tables containing the location of the target as a function of time and
 
 ```{code-cell} ipython3
 # Uncomment the next line to install dependencies if needed.
-# !pip install aiohttp 'astropy>=7.0.2' 'astroquery>=0.4.10' matplotlib numpy regions reproject requests
+# !pip install aiohttp 'astropy>=7.2.0' 'astroquery>=0.4.10' matplotlib numpy regions reproject requests
 ```
 
 ```{code-cell} ipython3