diff --git a/docs/source/user_guide/operation.md b/docs/source/user_guide/operation.md
index 44471da2..ebee8ac4 100644
--- a/docs/source/user_guide/operation.md
+++ b/docs/source/user_guide/operation.md
@@ -66,6 +66,14 @@ Run the [autofocus action](scheduling.md#autofocus-action). Ensure the `filter`
 
 Upon successful completion, the optimized focus position is updated in the configuration. Autofocus images and V-curve plot are saved to the `images/autofocus` directory.
 
+```{admonition} Focusing Tip
+:class: tip
+
+**Coarse Search (e.g., `fft`):** Best for broad ranges where stars appear as blurry "donuts." These non-parametric operators measure overall frame sharpness without needing to identify individual stars, making them nearly impossible to "confuse" with distorted optics.
+
+**Fine Tuning (e.g., `HFR`):** Best for precision near the focus peak. These algorithms fit a "V-curve" to the diameters of detected stars. They provide great accuracy once stars are point-like, but will fail during wide searches if the stars are too bloated to be recognized by the star-finder.
+```
+
 <!-- **3. Optional: Pointing model**
 Once focused, build a pointing model. Each pointing is plate solved and sends SyncToCoordinates commands to the mount. The receipt of these commands can be used to build a pointing model in the mount control software. _Astra_ itself does not build or maintain a pointing model.
 
diff --git a/example_observatories/speculoos/speculoos.py b/example_observatories/speculoos/speculoos.py
index 76b38c66..65bd18d8 100644
--- a/example_observatories/speculoos/speculoos.py
+++ b/example_observatories/speculoos/speculoos.py
@@ -1,6 +1,6 @@
 from typing import List
 
-import astra.utils as utils
+import astra.utils.astelos_errors as astelos_errors
 from astra.observatory import Observatory
 from astra.paired_devices import PairedDevices
 
@@ -168,7 +168,7 @@ def speculoos_check_and_ack_error(self, close=False) -> None:
                 telescope = self.devices["Telescope"][telescope_name]
 
                 # check telescope status
-                valid, all_errors, messages = utils.check_astelos_error(
+                valid, all_errors, messages = astelos_errors.check_error(
                     telescope, close=close
                 )
 
@@ -176,7 +176,7 @@ def speculoos_check_and_ack_error(self, close=False) -> None:
                     self.logger.info(
                         f"Attempting to acknowledge AsTelOS errors for {telescope_name}: {messages}"
                     )
-                    ack, messages = utils.ack_astelos_error(
+                    ack, messages = astelos_errors.ack_error(
                         telescope, valid, all_errors, messages, close=close
                     )
 
diff --git a/pyproject.toml b/pyproject.toml
index 988a43b5..2eb2cb3d 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -39,11 +39,11 @@ classifiers = [
   "Programming Language :: Python :: 3"
 ]
 dependencies = [
-  "astrafocus>=0.1.1",
+  "astrafocus>=0.1.5",
   "astropy",
-  "photutils",
+  "photutils>=3.0.0",
   "scipy",
-  "twirl>=0.4.1",
+  "twirl>=0.5.2",
   "sqlite3worker",
   "alpyca>=3.1.2,<4",
   "psutil",
@@ -54,7 +54,7 @@ dependencies = [
   "fastapi[standard]",
   "pyyaml",
   "ruamel.yaml",
-  "cabaret"
+  "cabaret>=0.5.4"
 ]
 description = "Automated Survey observaTory Robotised with Alpaca"
 license = {text = "GPL-3.0"}
diff --git a/src/astra/action_configs.py b/src/astra/action_configs.py
index 5779f83e..34a4c1ba 100644
--- a/src/astra/action_configs.py
+++ b/src/astra/action_configs.py
@@ -624,7 +624,7 @@ def validate_visibility(
         # Try to resolve coordinates if RA/Dec are not explicitly provided
         if ra is None or dec is None:
             if self.lookup_name is not None:
-                from astra.utils import get_body_coordinates
+                from astra.utils.ephemeris import get_body_coordinates
 
                 # Resolve body position at start time
                 target_coord = get_body_coordinates(
@@ -1041,6 +1041,15 @@ class AutofocusConfig(BaseActionConfig):
         4. Measure star sharpness in each image
         5. Fit a curve to determine optimal focus
         6. Save plots/results and save the best focus position in the observatory configuration
+
+    Note:
+        Coarse searches (for example `fft`, `normalized_variance`) use non-parametric
+        focus measures that characterise overall frame sharpness and are well suited
+        for very broad search ranges where stars appear as large, defocused "donuts".
+        Analytic response-function autofocusers (for example `HFR`/StarSize), which fit
+        a V-curve to measured star sizes, are better for fine-tuning near the focus
+        peak but can give incorrect results if applied over an excessively large range
+        because the assumed response model may not fit across the whole span.
     """
 
     exptime: float | int = field(default=3.0)
@@ -1053,7 +1062,7 @@ class AutofocusConfig(BaseActionConfig):
     n_exposures: List[int] | int = field(default_factory=lambda: [1, 1])
     decrease_search_range: bool = True
     star_find_threshold: float | int = 5.0
-    fwhm: int = 8
+    fwhm: Optional[int] = None
     percent_to_cut: int = 60
     focus_measure_operator: str = "HFR"
     save: bool = True
@@ -1092,9 +1101,16 @@ class AutofocusConfig(BaseActionConfig):
         ),
         "decrease_search_range": "Reduce the search range after each sweep.",
         "star_find_threshold": "DAOStarFinder threshold for star detection.",
-        "fwhm": "DAOStarFinder FWHM of the Gaussian kernel in pixels.",
+        "fwhm": (
+            "DAOStarFinder FWHM of the Gaussian kernel in pixels. If not set, derived "
+            'from the camera/telescope plate scale assuming ~2" seeing.'
+        ),
         "percent_to_cut": "Percentage of worst-performing focus samples to drop when shrinking the range.",
         "focus_measure_operator": "Focus metric to optimize (e.g., hfr, gauss, tenengrad, fft, normalized_variance).",
+        "focus_measure_operator_note": (
+            "Prefer non-parametric metrics (e.g., 'fft', 'normalized_variance') for coarse/broad searches; "
+            "use analytic measures (e.g., 'HFR') for fine tuning near the focus peak."
+        ),
         "reduce_exposure_time": "Automatically shorten exposures to prevent saturation.",
         "save": "Persist the optimal focus position back into observatory configuration.",
         "extremum_estimator": "Curve-fitting method used to determine the minimum (LOWESS, medianfilter, spline, rbf).",
@@ -1113,6 +1129,7 @@ class AutofocusConfig(BaseActionConfig):
         "action_value": {
             "exptime": 1.0,
             "filter": "V",
+            "focus_measure_operator": "HFR",
             "search_range_is_relative": True,
             "search_range": 1000,
             "n_steps": [30, 20],
diff --git a/src/astra/autofocus.py b/src/astra/autofocus.py
index dcbd2f87..6b52d235 100644
--- a/src/astra/autofocus.py
+++ b/src/astra/autofocus.py
@@ -25,7 +25,6 @@
 import matplotlib
 import matplotlib.pyplot as plt
 import numpy as np
-import pandas as pd
 from astrafocus import ExtremumEstimatorRegistry, FocusMeasureOperatorRegistry
 from astrafocus.autofocuser import (
     AnalyticResponseAutofocuser,
@@ -231,6 +230,9 @@ def move_focuser_to_position(
         while self.alpaca_device_focuser.get("IsMoving"):
             if time.time() - start_time > hard_timeout:
                 raise TimeoutError("Slew timeout")
+            if not self.observatory.check_conditions(action=self.action):
+                break
+
             time.sleep(0.1)
 
         time.sleep(self.settle_time)
@@ -570,8 +572,10 @@ def __init__(
         self.paired_devices = paired_devices
         self.action_value = action.action_value
         self.autofocuser = autofocuser
+        self.error_message: str | None = None
         self.success = success
         self.config: AutofocusConfig = action.action_value  # type: ignore
+        self._run_timestamp: str | None = None
 
         if (
             self.config.calibration_field.fov_width == 0
@@ -581,6 +585,9 @@ def __init__(
             self.config.calibration_field.fov_width = fov_width
             self.config.calibration_field.fov_height = fov_height
 
+        if self.config.fwhm is None:
+            self.config.fwhm = self.determine_default_fwhm(paired_devices)
+
         self._initialise_logging()
         self.observatory.logger.info(f"Loaded action values {self.action_value}")
         self.observatory.logger.info(f"Autofocus configuration: {self.config}.")
@@ -696,7 +703,6 @@ def _setup(self) -> None:
             n_exposures=self.config.n_exposures,
             decrease_search_range=self.config.decrease_search_range,
             exposure_time=self.config.exptime,
-            # save_path=self.config.save_path,
             secondary_focus_measure_operators=self.config._secondary_focus_measure_operators,
             focus_measure_operator_kwargs=self.config.focus_measure_operator_kwargs,
             search_range_is_relative=self.config.search_range_is_relative,
@@ -966,6 +972,40 @@ def reduce_exposure_time(
 
         return new_exposure_time
 
+    @property
+    def run_timestamp(self) -> str:
+        """Timestamp shared across all output files for a single run."""
+        if self._run_timestamp is None:
+            self._run_timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
+        return self._run_timestamp
+
+    def _output_path(self, save_dir: Path, suffix: str, ext: str) -> Path:
+        save_dir.mkdir(parents=True, exist_ok=True)
+        return save_dir / f"autofocus_{self.run_timestamp}_{suffix}.{ext}"
+
+    def _determine_save_dir(self) -> Path | None:
+        """Resolve the directory to write autofocus output files to.
+
+        Prefers the configured save_path; falls back to the directory of the
+        last image saved by this camera, or None if neither is available.
+        """
+        if self.config.save_path is not None:
+            return Path(self.config.save_path)
+
+        try:
+            image_handler = self.observatory.get_image_handler(self.action.device_name)
+            last_image_path = getattr(image_handler, "last_image_path", None)
+        except Exception as e:
+            self.observatory.logger.warning(
+                f"Unable to determine last image path from image handler: {str(e)}"
+            )
+            last_image_path = None
+
+        if last_image_path is None:
+            return None
+
+        return last_image_path.parent
+
     def make_summary_plot(self) -> None:
         """Create visualization plot of autofocus results.
 
@@ -977,35 +1017,14 @@ def make_summary_plot(self) -> None:
             if self.success is False:
                 return
 
-            # Determine directory to write the summary to. Prefer configured save_path;
-            # if not provided, fall back to the directory containing the last saved
-            # autofocus image for this camera (if available).
-            save_dir: Path | None = None
-            if self.config.save_path is not None:
-                save_dir = Path(self.config.save_path)
-
+            save_dir = self._determine_save_dir()
             if save_dir is None:
-                # try to find last image saved by this camera
-                try:
-                    image_handler = self.observatory.get_image_handler(
-                        self.action.device_name
-                    )
-                    last_image_path = getattr(image_handler, "last_image_path", None)
-                except Exception:
-                    last_image_path = None
-
-                if last_image_path is None:
-                    self.observatory.logger.warning(
-                        "Skipping creation of summary plot: no save_path configured and no last image available."
-                    )
-                    return
-
-                save_dir = last_image_path.parent
+                self.observatory.logger.warning(
+                    "Skipping creation of autofocus summary plot: "
+                    "unable to determine save directory."
+                )
+                return
 
-            # Obtain focus record dataframe. Prefer in-memory record from the
-            # astrafocus autofocuser instance; if not available, try reading CSVs
-            # from the chosen directory.
-            df: pd.DataFrame | None = None
             if (
                 hasattr(self, "autofocuser")
                 and getattr(self.autofocuser, "focus_record", None) is not None
@@ -1013,20 +1032,17 @@ def make_summary_plot(self) -> None:
                 try:
                     df = self.autofocuser.focus_record
                 except Exception:
-                    df = None
-
-            if df is None:
-                assert save_dir is not None
-                csv_files = sorted(
-                    [p for p in Path(save_dir).iterdir() if p.suffix == ".csv"],
-                    key=lambda p: p.stat().st_mtime,
-                )
-                if not csv_files:
-                    self.observatory.logger.error(
-                        f"No focus record CSV found in {save_dir}. Skipping summary plot."
+                    self.observatory.logger.warning(
+                        "Skipping creation of autofocus summary plot: "
+                        "unable to retrieve focus record from autofocuser instance."
                     )
                     return
-                df = pd.read_csv(csv_files[-1])
+            else:
+                self.observatory.logger.warning(
+                    "Skipping creation of autofocus summary plot: "
+                    "autofocuser instance does not have a focus_record attribute."
+                )
+                return
 
             df = df.sort_values("focus_pos")
 
@@ -1047,18 +1063,11 @@ def make_summary_plot(self) -> None:
             )
             ax.legend()
 
-            # Build output filename: if we read a CSV file use its stem, otherwise timestamp it.
-            if "csv_files" in locals() and csv_files:
-                out_name = f"{csv_files[-1].stem}.png"
-            else:
-                out_name = (
-                    f"autofocus_summary_{datetime.now().strftime('%Y%m%d_%H%M%S')}.png"
-                )
-
-            assert save_dir is not None
-            out_path = Path(save_dir) / out_name
-            plt.savefig(out_path)
+            plt.savefig(self._output_path(save_dir, "summary", "png"))
             plt.close()
+
+            df.to_csv(self._output_path(save_dir, "record", "csv"), index=False)
+
         except Exception as e:
             self.observatory.logger.exception(f"Error creating summary plot: {str(e)}")
 
@@ -1070,41 +1079,15 @@ def create_result_file(self) -> None:
         """
         if self.success is False:
             return
-        save_dir: Path | None = None
-        if self.config.save_path is not None:
-            save_dir = Path(self.config.save_path)
-
+        save_dir = self._determine_save_dir()
         if save_dir is None:
-            try:
-                image_handler = self.observatory.get_image_handler(
-                    self.action.device_name
-                )
-                last_image_path = getattr(image_handler, "last_image_path", None)
-            except Exception:
-                last_image_path = None
-
-            if last_image_path is None:
-                self.observatory.logger.error(
-                    "Skipping creation of log file: no save_path configured and no last image available."
-                )
-                return
-
-            save_dir = last_image_path.parent
-
-        # derive a timestring for filename; prefer an adjacent CSV if present
-        timestr = None
-        assert save_dir is not None
-        csv_files = sorted(
-            [p for p in Path(save_dir).iterdir() if p.suffix == ".csv"],
-            key=lambda p: p.stat().st_mtime,
-        )
-        if csv_files:
-            timestr = csv_files[-1].stem.split("_")[0]
-
-        if not timestr:
-            timestr = datetime.now().strftime("%Y%m%d_%H%M%S")
+            self.observatory.logger.error(
+                "Skipping creation of result file: "
+                "no save_path configured and no last image available."
+            )
+            return
 
-        result_file_path = Path(save_dir) / f"{timestr}_result.txt"
+        result_file_path = self._output_path(save_dir, "result", "txt")
         try:
             with open(result_file_path, "w") as result_file:
                 result_file.write(f"Best focus position: {self.best_focus_position}\n")
@@ -1113,7 +1096,7 @@ def create_result_file(self) -> None:
                 )
                 result_file.write(f"Autofocuser: {self.autofocuser}\n")
         except Exception as e:
-            self.observatory.logger.exception(f"Error creating log file: {str(e)}")
+            self.observatory.logger.exception(f"Error creating result file: {str(e)}")
 
     def _initialise_logging(self) -> None:
         """Set up logging integration with astrafocus library.
@@ -1199,43 +1182,29 @@ def determine_extremum_estimator(
         )
         return extremum_estimator
 
-    def calculate_field_of_view(self, paired_devices):
-        """
-        Calculate the field of view of the camera-telescope system.
-        """
+    def determine_default_field_of_view(self, paired_devices):
         try:
-            camera = paired_devices.camera
-            telescope = paired_devices.telescope
+            field_of_view = paired_devices.calculate_field_of_view()
+            fov_width = float(field_of_view[0])
+            fov_height = float(field_of_view[1])
 
-            # Convert microns to meters
-            pixel_size = 1e-6 * np.array(
-                [camera.get("PixelSizeX"), camera.get("PixelSizeY")]
+            self.observatory.logger.info(
+                f"Determined field of view width={fov_width}, height={fov_height}."
             )
-            number_of_pixels = np.array([camera.get("NumX"), camera.get("NumY")])
-
-            focal_length = telescope.get("FocalLength")  # meters
-            # plate_scale = np.arctan(pixel_size / focal_length)
-
-            # field_of_view = plate_scale * number_of_pixels
-            sensor_size = pixel_size * number_of_pixels  # [sx, sy]
-
-            fov = 2.0 * np.arctan(sensor_size / (2.0 * focal_length)) * (180.0 / np.pi)
-            return fov
-
+            return fov_width, fov_height
         except Exception as e:
-            field_of_view = np.array([np.nan, np.nan])
-            self.observatory.error(
-                f"Error calculating field of view from paired devices. Exception: {e}"
+            raise ValueError(
+                f"Error determining default field of view from paired devices: {str(e)}"
             )
 
-        return field_of_view
-
-    def determine_default_field_of_view(self, paired_devices):
-        field_of_view = self.calculate_field_of_view(paired_devices)
-        fov_width = float(field_of_view[0])
-        fov_height = float(field_of_view[1])
-
-        self.observatory.logger.info(
-            f"Determined field of view width={fov_width}, height={fov_height}."
-        )
-        return fov_width, fov_height
+    def determine_default_fwhm(self, paired_devices) -> int:
+        try:
+            fwhm = paired_devices.calculate_fwhm(seeing_arcsec=3)
+            self.observatory.logger.info(
+                f"Determined default fwhm={fwhm} px from plate scale."
+            )
+            return fwhm
+        except Exception as e:
+            raise ValueError(
+                f"Error determining default fwhm from paired devices: {str(e)}"
+            )
diff --git a/src/astra/calibrate_guiding.py b/src/astra/calibrate_guiding.py
index cd8eb80f..93256dda 100644
--- a/src/astra/calibrate_guiding.py
+++ b/src/astra/calibrate_guiding.py
@@ -25,7 +25,7 @@
 from astra.image_handler import ImageHandler
 from astra.paired_devices import PairedDevices
 from astra.scheduler import Action
-from astra.utils import CustomImageClass
+from astra.utils.image import CustomImageClass
 
 
 class GuidingCalibrator:
diff --git a/src/astra/guiding.py b/src/astra/guiding.py
index a9e0c9fc..155b49e7 100644
--- a/src/astra/guiding.py
+++ b/src/astra/guiding.py
@@ -42,7 +42,7 @@
 from astra.logger import ObservatoryLogger
 from astra.paired_devices import PairedDevices
 from astra.thread_manager import ThreadManager
-from astra.utils import CustomImageClass
+from astra.utils.image import CustomImageClass
 
 # rejection buffer length
 GUIDE_BUFFER_LENGTH = 20
diff --git a/src/astra/header_manager.py b/src/astra/header_manager.py
index c866b7a4..1f88d5ff 100644
--- a/src/astra/header_manager.py
+++ b/src/astra/header_manager.py
@@ -27,12 +27,12 @@
 from astropy.io import fits
 
 import astra
-from astra import utils
 from astra.config import ObservatoryConfig
 from astra.database_manager import DatabaseManager
 from astra.logger import ObservatoryLogger
 from astra.paired_devices import PairedDevices
 from astra.scheduler import Action, BaseActionConfig
+from astra.utils import time as time_utils
 
 __all__ = ["HeaderManager", "ObservatoryHeader"]
 
@@ -173,13 +173,13 @@ def add_times(
         dateend = exposure_start_datetime + datetime.timedelta(
             seconds=float(self["EXPTIME"])  # type : ignore
         )
-        jd = utils.to_jd(exposure_start_datetime)
-        jdend = utils.to_jd(dateend)
+        jd = time_utils.to_jd(exposure_start_datetime)
+        jdend = time_utils.to_jd(dateend)
 
         mjd = jd - 2400000.5
         mjdend = jdend - 2400000.5
 
-        hjd, bjd, lstsec, ha = utils.time_conversion(jd, location, target)
+        hjd, bjd, lstsec, ha = time_utils.time_conversion(jd, location, target)
 
         for row_header, row in fits_config[fits_config["fixed"] == False].iterrows():  # noqa: E712
             if row["device_type"] == "astra":
@@ -485,7 +485,7 @@ def _prepare_image_times(df_images_filt: pd.DataFrame) -> pd.DataFrame:
             drop=True
         )
         df_images_filt["jd_obs"] = (
-            df_images_filt["date_obs"].apply(utils.to_jd).sort_values()
+            df_images_filt["date_obs"].apply(time_utils.to_jd).sort_values()
         )
         while df_images_filt["jd_obs"].duplicated().sum() > 0:
             df_images_filt["jd_obs"] = df_images_filt["jd_obs"].mask(
@@ -506,7 +506,7 @@ def _get_polling_data(
         )
         df_poll["jd"] = pd.to_datetime(
             df_poll["datetime"], format="%Y-%m-%d %H:%M:%S.%f"
-        ).apply(utils.to_jd)
+        ).apply(time_utils.to_jd)
         return df_poll
 
     @staticmethod
@@ -571,7 +571,7 @@ def _interpolate_poll_data(
                 errors="coerce",
             ).fillna(-1)
 
-            df_inp[poll_row["header"]] = utils.interpolate_dfs(
+            df_inp[poll_row["header"]] = time_utils.interpolate_dfs(
                 df_images_filt["jd_obs"], df_poll_filtered["device_value"]
             )["device_value"].fillna(0)
         return df_inp
diff --git a/src/astra/observatory.py b/src/astra/observatory.py
index e553e599..959c1558 100644
--- a/src/astra/observatory.py
+++ b/src/astra/observatory.py
@@ -49,7 +49,6 @@
 from astropy.time import Time
 from astropy.wcs.utils import WCS
 
-import astra.utils
 from astra.alpaca_device_process import AlpacaDevice
 from astra.autofocus import Autofocuser, Defocuser
 from astra.calibrate_guiding import GuidingCalibrator
@@ -70,6 +69,7 @@
 from astra.safety_monitor import SafetyMonitor
 from astra.scheduler import Action, BaseActionConfig, ScheduleManager
 from astra.thread_manager import ThreadManager
+from astra.utils import ephemeris
 
 logging.getLogger("sqlite3worker").setLevel(logging.INFO)
 
@@ -183,6 +183,8 @@ def __init__(
         # log start up
         self.logger.debug("Database and DatabaseLoggingHandler initialized")
         self.logger.info(f"Starting observatory {self.name}")
+        if type(self) is not Observatory:
+            self.logger.info(f"Using observatory subclass: {type(self).__name__}")
 
         # warn if debug mode
         if self.logger.getEffectiveLevel() == logging.DEBUG:
@@ -1512,7 +1514,7 @@ def setup_observatory(
                 now = Time.now()
 
                 # Get body coordinates
-                target_coord = astra.utils.get_body_coordinates(
+                target_coord = ephemeris.get_body_coordinates(
                     body_name=lookup_name,
                     obs_time=now,
                     obs_location=obs_location,
@@ -2860,11 +2862,17 @@ def autofocus_sequence(self, action: Action, paired_devices: PairedDevices) -> b
             if not self.check_conditions(action=action):
                 return False
 
-            autofocuser = Autofocuser(
-                observatory=self,
-                action=action,
-                paired_devices=paired_devices,
-            )
+            try:
+                autofocuser = Autofocuser(
+                    observatory=self,
+                    action=action,
+                    paired_devices=paired_devices,
+                )
+            except Exception as e:
+                self.logger.warning(
+                    f"Autofocuser initialization failed for {action.device_name}: {e}"
+                )
+                return False
             autofocuser.determine_autofocus_calibration_field()
             autofocuser.slew_to_calibration_field()
             autofocuser.setup()
@@ -2951,7 +2959,7 @@ def flats_sequence(self, action: Action, paired_devices: PairedDevices) -> None:
         ).get_observatory_location()
 
         # wait for sun to be in right position
-        sun_rising, take_flats, sun_altaz = astra.utils.is_sun_rising(obs_location)
+        sun_rising, take_flats, sun_altaz = ephemeris.is_sun_rising(obs_location)
         self.logger.info(
             f"Sun at {sun_altaz.alt.degree:.2f} degrees and {'rising' if sun_rising else 'setting'}"
         )
@@ -2981,7 +2989,7 @@ def flats_sequence(self, action: Action, paired_devices: PairedDevices) -> None:
             )
 
         while self.check_conditions(action) and (take_flats is False):
-            sun_rising, take_flats, sun_altaz = astra.utils.is_sun_rising(obs_location)
+            sun_rising, take_flats, sun_altaz = ephemeris.is_sun_rising(obs_location)
 
             if take_flats is False:
                 time.sleep(1)
@@ -2998,7 +3006,7 @@ def flats_sequence(self, action: Action, paired_devices: PairedDevices) -> None:
         # start taking flats
         for i, filter_name in enumerate(action.action_value["filter"]):
             count = 0
-            sun_rising, take_flats, sun_altaz = astra.utils.is_sun_rising(obs_location)
+            sun_rising, take_flats, sun_altaz = ephemeris.is_sun_rising(obs_location)
 
             if self.check_conditions(action) and take_flats:
                 ## initial setup + exposure setting
@@ -3156,7 +3164,7 @@ def flats_position(
             # check if ready to take flats
             take_flats = False
             while self.check_conditions(action) and (take_flats is False):
-                _, take_flats, sun_altaz = astra.utils.is_sun_rising(obs_location)
+                _, take_flats, sun_altaz = ephemeris.is_sun_rising(obs_location)
 
                 if take_flats is False:
                     time.sleep(1)
@@ -3227,7 +3235,7 @@ def flats_exptime(
 
         """
 
-        sun_rising, take_flats, sun_altaz = astra.utils.is_sun_rising(obs_location)
+        sun_rising, take_flats, sun_altaz = ephemeris.is_sun_rising(obs_location)
 
         # initial exposure time guess
         if exptime is None:
@@ -3284,7 +3292,7 @@ def flats_exptime(
                         if fraction <= 0:
                             fraction = 0.01
 
-                    sun_rising, take_flats, sun_altaz = astra.utils.is_sun_rising(
+                    sun_rising, take_flats, sun_altaz = ephemeris.is_sun_rising(
                         obs_location
                     )
 
diff --git a/src/astra/paired_devices.py b/src/astra/paired_devices.py
index cf281b9e..71081886 100644
--- a/src/astra/paired_devices.py
+++ b/src/astra/paired_devices.py
@@ -44,9 +44,12 @@
     ...     telescope = paired_devices.telescope
 """
 
+import numpy as np
+
 import astra
 from astra.alpaca_device_process import AlpacaDevice
 from astra.config import ObservatoryConfig
+from astra.utils import optics
 
 
 class PairedDevices(dict[str, str]):
@@ -406,6 +409,59 @@ def rotator(self) -> AlpacaDevice:
         self._raise_property_not_paired("Rotator")
         return self.devices["Rotator"][self["Rotator"]]
 
+    def calculate_field_of_view(self) -> np.ndarray:
+        """
+        Calculate the field of view of the paired camera-telescope system.
+
+        Returns:
+            np.ndarray: Field of view [width, height] in degrees.
+
+        Raises:
+            KeyError: If either camera or telescope is not paired.
+            TypeError: If a required camera/telescope property is missing (None).
+        """
+        camera = self.camera
+        telescope = self.telescope
+
+        # Convert microns to meters
+        pixel_size = 1e-6 * np.array(
+            [camera.get("PixelSizeX"), camera.get("PixelSizeY")]
+        )
+        number_of_pixels = np.array([camera.get("NumX"), camera.get("NumY")])
+
+        focal_length = telescope.get("FocalLength")  # meters
+        sensor_size = pixel_size * number_of_pixels  # [sx, sy]
+
+        return optics.field_of_view(sensor_size, focal_length)
+
+    def calculate_fwhm(self, seeing_arcsec: float = 1.0) -> float:
+        """
+        Calculate the FWHM (Full Width at Half Maximum) in pixels for the
+        paired camera-telescope system based on seeing.
+
+        Parameters:
+            seeing_arcsec (float): Atmospheric seeing in arcseconds (default: 2.0").
+
+        Returns:
+            float: Expected FWHM in pixels.
+
+        Raises:
+            KeyError: If either camera or telescope is not paired.
+            TypeError: If a required camera/telescope property is missing (None).
+        """
+        camera = self.camera
+        telescope = self.telescope
+
+        # Convert microns to meters (average X and Y pixel sizes)
+        pixel_size = 1e-6 * np.mean(
+            [camera.get("PixelSizeX"), camera.get("PixelSizeY")]
+        )
+        focal_length = telescope.get("FocalLength")  # meters
+
+        plate_scale_deg_per_pixel = optics.plate_scale(pixel_size, focal_length)
+
+        return optics.fwhm_pixels(plate_scale_deg_per_pixel, seeing_arcsec)
+
     def _raise_property_not_paired(self, device_type: str):
         """
         Raise an error if the specified device type is not paired.
diff --git a/src/astra/pointer.py b/src/astra/pointer.py
index a9244d31..ee0f4296 100644
--- a/src/astra/pointer.py
+++ b/src/astra/pointer.py
@@ -66,7 +66,8 @@
 from photutils.detection import DAOStarFinder
 
 from astra.config import Config
-from astra.utils import clean_image
+from astra.utils import optics
+from astra.utils.image import clean_image
 
 logger = logging.getLogger(__name__)
 
@@ -186,7 +187,9 @@ def calculate_pointing_correction_from_image(
     stars_in_image = find_stars(
         image_clean,
         threshold=7,
-        fwhm=(2 / 3600) / plate_scale,
+        fwhm=optics.fwhm_pixels(
+            plate_scale_deg_per_pixel=plate_scale, seeing_arcsec=2.0
+        ),
     )
 
     # Limit number of stars and gaia stars to use for plate solve
@@ -285,7 +288,9 @@ def find_stars(
     dao_sources = dao_sources[dao_sources["peak"] > mean + threshold * std]
 
     # Convert to (x, y) coordinates
-    coordinates = np.column_stack([dao_sources["xcentroid"], dao_sources["ycentroid"]])
+    coordinates = np.column_stack(
+        [dao_sources["x_centroid"], dao_sources["y_centroid"]]
+    )
 
     return coordinates
 
@@ -609,9 +614,9 @@ def _extract_plate_scale_and_dateobs(header):
     else:
         focallen_unit = 1.0  # default to m
 
-    plate_scale = np.arctan(
-        (header["XPIXSZ"] * 1e-6) / (header["FOCALLEN"] * focallen_unit)
-    ) * (180 / np.pi)  # deg/pixel
+    plate_scale = optics.plate_scale(
+        header["XPIXSZ"] * 1e-6, header["FOCALLEN"] * focallen_unit
+    )  # deg/pixel
     return plate_scale, dateobs
 
 
@@ -673,7 +678,7 @@ def _get_gaia_star_coordinates(
         np.ndarray: Array of Gaia star coordinates.
     """
     # Get fov from image shape and plate scale
-    fov = np.array(image_clean.shape) * plate_scale * fov_scale
+    fov = optics.fov_from_plate_scale(image_clean.shape, plate_scale) * fov_scale
     gaia_tmass_filter = _map_filter_band_to_gaia_tmass(filter_band)
 
     # Try online query first (if local DB doesn't exist, this is the only option)
@@ -684,7 +689,7 @@ def _get_gaia_star_coordinates(
             table = cabaret.GaiaQuery.query(
                 center=(ra, dec),
                 radius=np.max(fov) / 2,
-                filter_band=gaia_tmass_filter,
+                filter_bands=gaia_tmass_filter,
                 limit=limit,
                 timeout=60,
             )
@@ -731,7 +736,7 @@ def _verify_offset_within_fov(
     """
     # Check that the offset is not larger than the fov
     # Get fov from image shape and plate scale
-    fov = np.array(image_shape) * plate_scale
+    fov = optics.fov_from_plate_scale(image_shape, plate_scale)
     if pointing_correction.angular_separation > max(fov):
         raise Exception("Pointing error is larger than the field of view")
 
diff --git a/src/astra/utils.py b/src/astra/utils.py
deleted file mode 100644
index 4d5b1e77..00000000
--- a/src/astra/utils.py
+++ /dev/null
@@ -1,501 +0,0 @@
-"""Utility functions for astronomical observations and data processing.
-
-This module provides essential utility functions for the Astra observatory system,
-including time conversions, coordinate transformations, database queries, and
-telescope error handling. Functions support FITS header processing, flat field
-observations, and SPECULOOS telescope operations.
-
-Key capabilities:
-    - Julian Day and astronomical time system conversions
-    - FITS header time calculations with light travel corrections
-    - Solar position analysis for flat field timing
-    - Database queries for astronomical catalogs
-    - SPECULOOS telescope error checking and acknowledgement
-"""
-
-import math
-import time
-from datetime import datetime
-from typing import Any, Tuple
-
-import astropy.units as u
-import numpy as np
-import pandas as pd
-from astropy.coordinates import AltAz, Angle, SkyCoord, get_sun
-from astropy.stats import SigmaClip, sigma_clipped_stats
-from astropy.time import Time
-from donuts.image import Image
-from photutils.background import Background2D, MedianBackground
-from scipy import ndimage
-
-
-class CustomImageClass(Image):
-    """Enhanced image processing class with background subtraction and cleaning."""
-
-    def preconstruct_hook(self) -> None:
-        """
-        Apply image preprocessing before Donuts star detection.
-
-        Performs background subtraction, noise reduction, and systematic
-        correction to improve star detection reliability.
-        """
-        # if greater than 2Kx2K, crop to 2Kx2K for speed
-        shapex, shapey = self.raw_image.shape
-        if shapex > 2048 and shapey > 2048:
-            self.raw_image = self.raw_image[
-                shapex // 2 - 1024 : shapex // 2 + 1024,
-                shapey // 2 - 1024 : shapey // 2 + 1024,
-            ]
-
-        self.raw_image = clean_image(self.raw_image)
-        mean, median, std = sigma_clipped_stats(self.raw_image, sigma=3.0)
-
-        # remove noise floor
-        self.raw_image -= median + 7 * std
-        self.raw_image[self.raw_image < 0] = 0
-
-
-## for final fits header
-def interpolate_dfs(index: np.ndarray, *data: pd.DataFrame) -> pd.DataFrame:
-    """Interpolate multiple pandas DataFrames onto a common index.
-
-    Merges and interpolates multiple DataFrames using a specified index array,
-    commonly used for wavelength-dependent data processing in spectroscopy.
-
-    Args:
-        index (np.ndarray): 1D array to interpolate data onto (e.g., wavelength grid).
-        *data (pd.DataFrame): Variable number of DataFrames to interpolate.
-
-    Returns:
-        pd.DataFrame: Combined DataFrame with all data interpolated onto the common index.
-    """
-    df = pd.DataFrame({"tmp": index}, index=index)
-    for dat in data:
-        dat = dat[~dat.index.duplicated(keep="first")]
-        df = pd.concat([df, dat], axis=1)
-    df = df.sort_index()
-    df = df.interpolate(method="index", axis=0).reindex(index)
-    df = df.drop(labels="tmp", axis=1)
-
-    return df
-
-
-def __to_format(jd: float, fmt: str) -> float:
-    """Convert Julian Day to specified time format.
-
-    Internal function for converting Julian Day values to different astronomical
-    time formats like Modified Julian Day or Reduced Julian Day.
-
-    Args:
-        jd (float): Julian Day value to convert.
-        fmt (str): Target format ('jd', 'mjd', 'rjd').
-
-    Returns:
-        float: Converted time value in specified format.
-
-    Raises:
-        ValueError: If format string is not recognized.
-    """
-    if fmt.lower() == "jd":
-        return jd
-    elif fmt.lower() == "mjd":
-        return jd - 2400000.5
-    elif fmt.lower() == "rjd":
-        return jd - 2400000
-    else:
-        raise ValueError("Invalid Format")
-
-
-def to_jd(dt: datetime, fmt: str = "jd") -> float:
-    """Convert datetime object to Julian Day using standard algorithm.
-
-    Converts Python datetime to Julian Day format using the algorithm from
-    Wikipedia. Supports conversion to various Julian Day formats.
-
-    Args:
-        dt (datetime): Datetime object to convert.
-        fmt (str): Output format ('jd', 'mjd', 'rjd'). Defaults to 'jd'.
-
-    Returns:
-        float: Julian Day value in specified format.
-    """
-    a = math.floor((14 - dt.month) / 12)
-    y = dt.year + 4800 - a
-    m = dt.month + 12 * a - 3
-
-    jdn = (
-        dt.day
-        + math.floor((153 * m + 2) / 5)
-        + 365 * y
-        + math.floor(y / 4)
-        - math.floor(y / 100)
-        + math.floor(y / 400)
-        - 32045
-    )
-
-    jd = (
-        jdn
-        + (dt.hour - 12) / 24
-        + dt.minute / 1440
-        + dt.second / 86400
-        + dt.microsecond / 86400000000
-    )
-
-    return __to_format(jd, fmt)
-
-
-def getLightTravelTimes(target: SkyCoord, time_to_correct: Time) -> Tuple[Time, Time]:
-    """Calculate light travel times to heliocentric and barycentric frames.
-
-    Computes corrections for light travel time from Earth to the solar system
-    barycenter and heliocenter, essential for precise timing in astronomy.
-
-    Args:
-        target (SkyCoord): Target celestial coordinates.
-        time_to_correct (Time): Observation time requiring correction.
-            Must be initialized with an EarthLocation.
-
-    Returns:
-        Tuple[Time, Time]: Light travel times as (barycentric, heliocentric).
-    """
-
-    ltt_bary = time_to_correct.light_travel_time(target)
-    ltt_helio = time_to_correct.light_travel_time(target, "heliocentric")
-    return ltt_bary, ltt_helio
-
-
-def time_conversion(
-    jd: float, location: Any, target: SkyCoord
-) -> Tuple[float, float, float, str]:
-    """Convert time to various astronomical reference frames.
-
-    Transforms Julian Day to heliocentric and barycentric systems, calculates
-    local sidereal time and hour angle for astronomical observations.
-
-    Args:
-        jd (float): Julian Day to convert.
-        location (EarthLocation): Observer's geographic location.
-        target (SkyCoord): Target celestial coordinates.
-
-    Returns:
-        Tuple[float, float, float, str]: Converted times as
-            (hjd, bjd, lst_seconds, hour_angle_string).
-    """
-
-    time_inp = Time(jd, format="jd", scale="utc", location=location)
-
-    ltt_bary, ltt_helio = getLightTravelTimes(target, time_inp)
-
-    hjd = (time_inp + ltt_helio).value
-    bjd = (time_inp.tdb + ltt_bary).value
-    lst = time_inp.sidereal_time("mean")
-    lstsec = lst.hour * 3600
-    ha = Angle(((((lst - target.ra).hour + 12) % 24) - 12) * u.hourangle).to_string(
-        unit=u.hourangle, sep=" ", pad=True
-    )
-
-    return hjd, bjd, lstsec, ha
-
-
-## for flat fielding
-def is_sun_rising(obs_location: Any) -> Tuple[bool, bool, AltAz]:
-    """Determine solar motion and flat field observation readiness.
-
-    Analyzes sun position and movement to determine if conditions are suitable
-    for flat field calibration observations, which require specific twilight conditions.
-
-    Args:
-        obs_location (EarthLocation): Observer's geographic location.
-
-    Returns:
-        Tuple[bool, bool, AltAz]: Solar status as (rising, flat_ready, position):
-            - rising: True if sun is rising, False if setting
-            - flat_ready: True if optimal for flats (sun altitude -12° to -1°)
-            - position: Current sun position in alt-az coordinates
-    """
-    # sun's position now
-    obs_time0 = Time.now()
-    sun_position0 = get_sun(obs_time0)
-    sun_altaz0 = sun_position0.transform_to(
-        AltAz(obstime=obs_time0, location=obs_location)
-    )
-
-    # sun's position in 5 minutes
-    obs_time1 = obs_time0 + 5 * u.minute
-    sun_position1 = get_sun(obs_time1)
-    sun_altaz1 = sun_position1.transform_to(
-        AltAz(obstime=obs_time1, location=obs_location)
-    )
-
-    # determine if sun is moving up or down by looking at gradient
-    sun_altaz_grad = (sun_altaz1.alt.degree - sun_altaz0.alt.degree) / (
-        obs_time1 - obs_time0
-    ).sec
-
-    sun_rising = None
-    if sun_altaz_grad > 0:
-        sun_rising = True
-    else:
-        sun_rising = False
-
-    flat_ready = False
-
-    if sun_altaz0.alt.deg > -12 and sun_altaz0.alt.deg < -1:
-        flat_ready = True
-
-    return sun_rising, flat_ready, sun_altaz0
-
-
-def clean_image(data: np.ndarray) -> np.ndarray:
-    """
-    Clean an image by subtracting the background.
-
-    Parameters:
-        data (np.ndarray): The 2D image data.
-
-    Returns:
-        np.ndarray: The background-subtracted image.
-    """
-
-    sigma_clip = SigmaClip(sigma=3.0)
-    bkg_estimator = MedianBackground()
-
-    # Convert to float32, handling both regular and masked arrays
-    data = data.astype(np.float32)
-    if np.ma.isMaskedArray(data):
-        data = data.filled(fill_value=np.nan)
-
-    bkg = Background2D(
-        data,
-        (32, 32),
-        filter_size=(3, 3),
-        sigma_clip=sigma_clip,
-        bkg_estimator=bkg_estimator,  # type: ignore
-    )
-
-    bkg_clean = data - bkg.background
-
-    med_clean = ndimage.median_filter(
-        bkg_clean, size=5, mode="mirror"
-    )  # slow but needed
-
-    # add minimum back to avoid negative values
-    med_clean += np.abs(np.nanmin(med_clean))
-
-    return med_clean
-
-
-## planet or SIMBAD positions
-def get_body_coordinates(body_name: str, obs_time: Time, obs_location: Any) -> SkyCoord:
-    """Get the position of a celestial body (Solar System or Deep Sky).
-
-    Calculates the apparent celestial coordinates of a specified solar system body
-    or resolves the coordinates of a deep sky object by name.
-
-    Args:
-        body_name (str): Name of the body (e.g., 'mars', 'jupiter', 'M31', 'Vega').
-        obs_time (Time): Observation time (used for solar system bodies).
-        obs_location (EarthLocation): Observer's geographic location (used for solar system bodies).
-
-    Returns:
-        SkyCoord: Position of the body in the sky.
-    """
-    from astropy.coordinates import SkyCoord, get_body, solar_system_ephemeris
-
-    # Check if the body is in the solar system ephemeris (case-insensitive)
-    # solar_system_ephemeris.bodies normally contains lowercase strings
-    if body_name.lower() in solar_system_ephemeris.bodies:
-        return get_body(body_name, obs_time, obs_location)
-
-    # Otherwise, try to resolve as a deep sky object (ICRS)
-    return SkyCoord.from_name(body_name)
-
-
-## SPECULOOS EDIT
-def check_astelos_error(
-    telescope: Any, close: bool = False
-) -> Tuple[bool, pd.DataFrame, str]:
-    """Check SPECULOOS telescope status for known acceptable errors.
-
-    Analyzes telescope status messages to identify errors and determines if they
-    are in the list of known acceptable errors that can be safely acknowledged.
-
-    Args:
-        telescope (Any): Telescope object with get() method for status commands.
-        close (bool): Whether to include slit closure errors in acceptable list.
-
-    Returns:
-        Tuple[bool, pd.DataFrame, str]: Error analysis as (valid, errors, messages):
-            - valid: True if all errors are acceptable, False otherwise
-            - errors: DataFrame with columns ['error', 'detail', 'level', 'component']
-            - messages: Raw telescope status message string
-    """
-
-    allowed_err = [
-        [
-            "ERR_DeviceError",
-            "axis (0) unexpectedly changed to powered on state",
-            "2",
-            "DOME[0]",
-        ],
-        [
-            "ERR_DeviceError",
-            "axis (0) unexpectedly changed to powered on state",
-            "2",
-            "DOME[1]",
-        ],
-        [
-            "ERR_DeviceError",
-            "axis (1) unexpectedly changed to powered on state",
-            "2",
-            "DOME[0]",
-        ],
-        [
-            "ERR_DeviceError",
-            "axis (1) unexpectedly changed to powered on state",
-            "2",
-            "DOME[1]",
-        ],
-        [
-            "ERR_DeviceError",
-            "axis #0\\| amplifier fault #07H\\| safe torque-off circuit fault",
-            "2",
-            "HA",
-        ],
-        [
-            "ERR_DeviceError",
-            "axis #0\\| amplifier fault #07H\\| safe torque-off circuit fault",
-            "2",
-            "DEC",
-        ],
-        [
-            "ERR_DeviceError",
-            "axis #1\\| amplifier fault #07H\\| safe torque-off circuit fault",
-            "2",
-            "HA",
-        ],
-        [
-            "ERR_DeviceError",
-            "axis #1\\| amplifier fault #07H\\| safe torque-off circuit fault",
-            "2",
-            "DEC",
-        ],
-        ["ERR_RunDevError", "Working pressure suddenly lost", "2", "HA"],
-        ["ERR_RunDevError", "Working pressure suddenly lost", "2", "DEC"],
-        ["ERR_DeviceWarn", "Malformed telegram from GPS", "4", "LOCAL"],
-        ["ERR_DeviceError", "axis (1)\\| BOTH LIMITS (code=128)", "2", "DOME[0]"],
-        ["ERR_DeviceError", "axis (1)\\| BOTH LIMITS (code=128)", "2", "DOME[1]"],
-        ["ERR_DeviceError", "axis (1)\\| EXTERN (code=32)", "2", "DOME[0]"],
-        ["ERR_DeviceError", "axis (1)\\| EXTERN (code=32)", "2", "DOME[1]"],
-    ]
-    if close:
-        slit_error = []
-        allowed_err.extend(slit_error)
-
-    df_allowed = pd.DataFrame(
-        allowed_err, columns=["error", "detail", "level", "component"]
-    )
-    df_list = pd.DataFrame(columns=["error", "detail", "level", "component"])
-
-    messages = telescope.get("CommandString", Command="TELESCOPE.STATUS.LIST", Raw=True)
-    # structure = "<group>|<level>[:<component>|<level>[;<component>...]][:<error>|<detail>|<level>|<component>[;<error>...]][,<group>...]"
-
-    for message in messages.split(","):
-        parts = message.split(":")
-
-        # only look parts after "<group>|<level>"
-        for part in parts[1:]:
-            elements = part.split(";")
-
-            for element in elements:
-                error_detail = element.replace("\\|", "[ESCAPED_PIPE]").split("|")
-                error_detail = [
-                    item.replace("[ESCAPED_PIPE]", "\\|") for item in error_detail
-                ]
-
-                if len(error_detail) == 4:
-                    if not error_detail[1].isdigit():
-                        error = error_detail[0]
-                        detail = error_detail[1]
-                        error_level = error_detail[2]
-                        component = error_detail[3]
-
-                        df_list = pd.concat(
-                            [
-                                df_list,
-                                pd.DataFrame(
-                                    {
-                                        "error": [error],
-                                        "detail": [detail],
-                                        "level": [error_level],
-                                        "component": [component],
-                                    }
-                                ),
-                            ],
-                            ignore_index=True,
-                        )
-
-    # check all rows of df_list are in df_allowed
-    compare_df = pd.merge(df_list, df_allowed, how="left", indicator="exists")
-    exists = compare_df["exists"] == "both"
-
-    # if all of exists is True
-    if exists.all():
-        return True, df_list, messages
-    else:
-        return False, df_list, messages
-
-
-def ack_astelos_error(
-    telescope: Any,
-    valid: bool,
-    all_errors: pd.DataFrame,
-    messages: str,
-    close: bool = False,
-) -> Tuple[bool, str]:
-    """Acknowledge acceptable SPECULOOS telescope errors.
-
-    Attempts to clear acceptable telescope errors by sending appropriate
-    acknowledgement commands. Continues until all errors are cleared or
-    unacceptable errors are encountered.
-
-    Args:
-        telescope (Any): Telescope object with get() method for commands.
-        valid (bool): Whether errors are acceptable (from check_astelos_error).
-        all_errors (pd.DataFrame): Error information with 'level' column.
-        messages (str): Original telescope status messages.
-        close (bool): Whether to include slit closure errors as acceptable.
-
-    Returns:
-        Tuple[bool, str]: Acknowledgement result as (success, final_messages):
-            - success: True if all errors cleared, False if unacceptable errors remain
-            - final_messages: Updated telescope status messages
-
-    Raises:
-        TimeoutError: If error clearing takes longer than 2 minutes.
-    """
-
-    start_time = time.time()
-
-    while valid and len(all_errors) > 0:
-        # derive system eror level
-        sys_level = int(np.sum(np.unique(np.array(all_errors.level.astype(int)))))
-
-        # clear errors
-        telescope.get(
-            "CommandBlind",
-            Command=f"TELESCOPE.STATUS.CLEAR_ERROR={sys_level}",
-            Raw=True,
-        )
-        time.sleep(2)
-
-        # check telescope status
-        valid, all_errors, messages = check_astelos_error(telescope, close=close)
-
-        if time.time() - start_time > 120:  # 2 minutes hardcoded limit
-            raise TimeoutError("Astelos error acknowledgement timed out")
-
-    if not valid:
-        return False, messages
-
-    return True, messages
diff --git a/src/astra/utils/__init__.py b/src/astra/utils/__init__.py
new file mode 100644
index 00000000..e69de29b
diff --git a/src/astra/utils/astelos_errors.py b/src/astra/utils/astelos_errors.py
new file mode 100644
index 00000000..8888ac3c
--- /dev/null
+++ b/src/astra/utils/astelos_errors.py
@@ -0,0 +1,194 @@
+"""AsTelOS telescope error checking and acknowledgement."""
+
+import time
+from typing import Any, Tuple
+
+import numpy as np
+import pandas as pd
+
+
+def check_error(telescope: Any, close: bool = False) -> Tuple[bool, pd.DataFrame, str]:
+    """Check AsTelOS telescope status for known acceptable errors.
+
+    Analyzes telescope status messages to identify errors and determines if they
+    are in the list of known acceptable errors that can be safely acknowledged.
+
+    Args:
+        telescope (Any): Telescope object with get() method for status commands.
+        close (bool): Whether to include slit closure errors in acceptable list.
+
+    Returns:
+        Tuple[bool, pd.DataFrame, str]: Error analysis as (valid, errors, messages):
+            - valid: True if all errors are acceptable, False otherwise
+            - errors: DataFrame with columns ['error', 'detail', 'level', 'component']
+            - messages: Raw telescope status message string
+    """
+
+    allowed_err = [
+        [
+            "ERR_DeviceError",
+            "axis (0) unexpectedly changed to powered on state",
+            "2",
+            "DOME[0]",
+        ],
+        [
+            "ERR_DeviceError",
+            "axis (0) unexpectedly changed to powered on state",
+            "2",
+            "DOME[1]",
+        ],
+        [
+            "ERR_DeviceError",
+            "axis (1) unexpectedly changed to powered on state",
+            "2",
+            "DOME[0]",
+        ],
+        [
+            "ERR_DeviceError",
+            "axis (1) unexpectedly changed to powered on state",
+            "2",
+            "DOME[1]",
+        ],
+        [
+            "ERR_DeviceError",
+            "axis #0\\| amplifier fault #07H\\| safe torque-off circuit fault",
+            "2",
+            "HA",
+        ],
+        [
+            "ERR_DeviceError",
+            "axis #0\\| amplifier fault #07H\\| safe torque-off circuit fault",
+            "2",
+            "DEC",
+        ],
+        [
+            "ERR_DeviceError",
+            "axis #1\\| amplifier fault #07H\\| safe torque-off circuit fault",
+            "2",
+            "HA",
+        ],
+        [
+            "ERR_DeviceError",
+            "axis #1\\| amplifier fault #07H\\| safe torque-off circuit fault",
+            "2",
+            "DEC",
+        ],
+        ["ERR_RunDevError", "Working pressure suddenly lost", "2", "HA"],
+        ["ERR_RunDevError", "Working pressure suddenly lost", "2", "DEC"],
+        ["ERR_DeviceWarn", "Malformed telegram from GPS", "4", "LOCAL"],
+        ["ERR_DeviceError", "axis (1)\\| BOTH LIMITS (code=128)", "2", "DOME[0]"],
+        ["ERR_DeviceError", "axis (1)\\| BOTH LIMITS (code=128)", "2", "DOME[1]"],
+        ["ERR_DeviceError", "axis (1)\\| EXTERN (code=32)", "2", "DOME[0]"],
+        ["ERR_DeviceError", "axis (1)\\| EXTERN (code=32)", "2", "DOME[1]"],
+    ]
+    if close:
+        slit_error = []
+        allowed_err.extend(slit_error)
+
+    df_allowed = pd.DataFrame(
+        allowed_err, columns=["error", "detail", "level", "component"]
+    )
+    df_list = pd.DataFrame(columns=["error", "detail", "level", "component"])
+
+    messages = telescope.get("CommandString", Command="TELESCOPE.STATUS.LIST", Raw=True)
+    # structure = "<group>|<level>[:<component>|<level>[;<component>...]][:<error>|<detail>|<level>|<component>[;<error>...]][,<group>...]"
+
+    for message in messages.split(","):
+        parts = message.split(":")
+
+        # only look parts after "<group>|<level>"
+        for part in parts[1:]:
+            elements = part.split(";")
+
+            for element in elements:
+                error_detail = element.replace("\\|", "[ESCAPED_PIPE]").split("|")
+                error_detail = [
+                    item.replace("[ESCAPED_PIPE]", "\\|") for item in error_detail
+                ]
+
+                if len(error_detail) == 4:
+                    if not error_detail[1].isdigit():
+                        error = error_detail[0]
+                        detail = error_detail[1]
+                        error_level = error_detail[2]
+                        component = error_detail[3]
+
+                        df_list = pd.concat(
+                            [
+                                df_list,
+                                pd.DataFrame(
+                                    {
+                                        "error": [error],
+                                        "detail": [detail],
+                                        "level": [error_level],
+                                        "component": [component],
+                                    }
+                                ),
+                            ],
+                            ignore_index=True,
+                        )
+
+    # check all rows of df_list are in df_allowed
+    compare_df = pd.merge(df_list, df_allowed, how="left", indicator="exists")
+    exists = compare_df["exists"] == "both"
+
+    # if all of exists is True
+    if exists.all():
+        return True, df_list, messages
+    else:
+        return False, df_list, messages
+
+
+def ack_error(
+    telescope: Any,
+    valid: bool,
+    all_errors: pd.DataFrame,
+    messages: str,
+    close: bool = False,
+) -> Tuple[bool, str]:
+    """Acknowledge acceptable AsTelOS telescope errors.
+
+    Attempts to clear acceptable telescope errors by sending appropriate
+    acknowledgement commands. Continues until all errors are cleared or
+    unacceptable errors are encountered.
+
+    Args:
+        telescope (Any): Telescope object with get() method for commands.
+        valid (bool): Whether errors are acceptable (from check_error).
+        all_errors (pd.DataFrame): Error information with 'level' column.
+        messages (str): Original telescope status messages.
+        close (bool): Whether to include slit closure errors as acceptable.
+
+    Returns:
+        Tuple[bool, str]: Acknowledgement result as (success, final_messages):
+            - success: True if all errors cleared, False if unacceptable errors remain
+            - final_messages: Updated telescope status messages
+
+    Raises:
+        TimeoutError: If error clearing takes longer than 2 minutes.
+    """
+
+    start_time = time.time()
+
+    while valid and len(all_errors) > 0:
+        # derive system eror level
+        sys_level = int(np.sum(np.unique(np.array(all_errors.level.astype(int)))))
+
+        # clear errors
+        telescope.get(
+            "CommandBlind",
+            Command=f"TELESCOPE.STATUS.CLEAR_ERROR={sys_level}",
+            Raw=True,
+        )
+        time.sleep(2)
+
+        # check telescope status
+        valid, all_errors, messages = check_error(telescope, close=close)
+
+        if time.time() - start_time > 120:  # 2 minutes hardcoded limit
+            raise TimeoutError("Astelos error acknowledgement timed out")
+
+    if not valid:
+        return False, messages
+
+    return True, messages
diff --git a/src/astra/utils/ephemeris.py b/src/astra/utils/ephemeris.py
new file mode 100644
index 00000000..6b1902b5
--- /dev/null
+++ b/src/astra/utils/ephemeris.py
@@ -0,0 +1,82 @@
+"""Solar position analysis and celestial body coordinate lookup."""
+
+from typing import Any, Tuple
+
+import astropy.units as u
+from astropy.coordinates import AltAz, SkyCoord, get_sun
+from astropy.time import Time
+
+
+## for flat fielding
+def is_sun_rising(obs_location: Any) -> Tuple[bool, bool, AltAz]:
+    """Determine solar motion and flat field observation readiness.
+
+    Analyzes sun position and movement to determine if conditions are suitable
+    for flat field calibration observations, which require specific twilight conditions.
+
+    Args:
+        obs_location (EarthLocation): Observer's geographic location.
+
+    Returns:
+        Tuple[bool, bool, AltAz]: Solar status as (rising, flat_ready, position):
+            - rising: True if sun is rising, False if setting
+            - flat_ready: True if optimal for flats (sun altitude -12° to -1°)
+            - position: Current sun position in alt-az coordinates
+    """
+    # sun's position now
+    obs_time0 = Time.now()
+    sun_position0 = get_sun(obs_time0)
+    sun_altaz0 = sun_position0.transform_to(
+        AltAz(obstime=obs_time0, location=obs_location)
+    )
+
+    # sun's position in 5 minutes
+    obs_time1 = obs_time0 + 5 * u.minute
+    sun_position1 = get_sun(obs_time1)
+    sun_altaz1 = sun_position1.transform_to(
+        AltAz(obstime=obs_time1, location=obs_location)
+    )
+
+    # determine if sun is moving up or down by looking at gradient
+    sun_altaz_grad = (sun_altaz1.alt.degree - sun_altaz0.alt.degree) / (
+        obs_time1 - obs_time0
+    ).sec
+
+    sun_rising = None
+    if sun_altaz_grad > 0:
+        sun_rising = True
+    else:
+        sun_rising = False
+
+    flat_ready = False
+
+    if sun_altaz0.alt.deg > -12 and sun_altaz0.alt.deg < -1:
+        flat_ready = True
+
+    return sun_rising, flat_ready, sun_altaz0
+
+
+## planet or SIMBAD positions
+def get_body_coordinates(body_name: str, obs_time: Time, obs_location: Any) -> SkyCoord:
+    """Get the position of a celestial body (Solar System or Deep Sky).
+
+    Calculates the apparent celestial coordinates of a specified solar system body
+    or resolves the coordinates of a deep sky object by name.
+
+    Args:
+        body_name (str): Name of the body (e.g., 'mars', 'jupiter', 'M31', 'Vega').
+        obs_time (Time): Observation time (used for solar system bodies).
+        obs_location (EarthLocation): Observer's geographic location (used for solar system bodies).
+
+    Returns:
+        SkyCoord: Position of the body in the sky.
+    """
+    from astropy.coordinates import SkyCoord, get_body, solar_system_ephemeris
+
+    # Check if the body is in the solar system ephemeris (case-insensitive)
+    # solar_system_ephemeris.bodies normally contains lowercase strings
+    if body_name.lower() in solar_system_ephemeris.bodies:
+        return get_body(body_name, obs_time, obs_location)
+
+    # Otherwise, try to resolve as a deep sky object (ICRS)
+    return SkyCoord.from_name(body_name)
diff --git a/src/astra/utils/image.py b/src/astra/utils/image.py
new file mode 100644
index 00000000..ef3649a9
--- /dev/null
+++ b/src/astra/utils/image.py
@@ -0,0 +1,72 @@
+"""Image cleaning and background subtraction utilities."""
+
+import numpy as np
+from astropy.stats import SigmaClip, sigma_clipped_stats
+from donuts.image import Image
+from photutils.background import Background2D, MedianBackground
+from scipy import ndimage
+
+
+class CustomImageClass(Image):
+    """Enhanced image processing class with background subtraction and cleaning."""
+
+    def preconstruct_hook(self) -> None:
+        """
+        Apply image preprocessing before Donuts star detection.
+
+        Performs background subtraction, noise reduction, and systematic
+        correction to improve star detection reliability.
+        """
+        # if greater than 2Kx2K, crop to 2Kx2K for speed
+        shapex, shapey = self.raw_image.shape
+        if shapex > 2048 and shapey > 2048:
+            self.raw_image = self.raw_image[
+                shapex // 2 - 1024 : shapex // 2 + 1024,
+                shapey // 2 - 1024 : shapey // 2 + 1024,
+            ]
+
+        self.raw_image = clean_image(self.raw_image)
+        mean, median, std = sigma_clipped_stats(self.raw_image, sigma=3.0)
+
+        # remove noise floor
+        self.raw_image -= median + 7 * std
+        self.raw_image[self.raw_image < 0] = 0
+
+
+def clean_image(data: np.ndarray) -> np.ndarray:
+    """
+    Clean an image by subtracting the background.
+
+    Parameters:
+        data (np.ndarray): The 2D image data.
+
+    Returns:
+        np.ndarray: The background-subtracted image.
+    """
+
+    sigma_clip = SigmaClip(sigma=3.0)
+    bkg_estimator = MedianBackground()
+
+    # Convert to float32, handling both regular and masked arrays
+    data = data.astype(np.float32)
+    if np.ma.isMaskedArray(data):
+        data = data.filled(fill_value=np.nan)
+
+    bkg = Background2D(
+        data,
+        (32, 32),
+        filter_size=(3, 3),
+        sigma_clip=sigma_clip,
+        bkg_estimator=bkg_estimator,  # type: ignore
+    )
+
+    bkg_clean = data - bkg.background
+
+    med_clean = ndimage.median_filter(
+        bkg_clean, size=5, mode="mirror"
+    )  # slow but needed
+
+    # add minimum back to avoid negative values
+    med_clean += np.abs(np.nanmin(med_clean))
+
+    return med_clean
diff --git a/src/astra/utils/optics.py b/src/astra/utils/optics.py
new file mode 100644
index 00000000..c3a59033
--- /dev/null
+++ b/src/astra/utils/optics.py
@@ -0,0 +1,77 @@
+"""
+Pure optics math shared across device-based and FITS-header-based code paths.
+
+`paired_devices.py` derives plate scale from live Alpaca `Camera`/`Telescope`
+device properties, while `pointer.py` derives it from FITS header metadata
+(`XPIXSZ`/`FOCALLEN`). Both then need the same downstream math (plate scale,
+field of view, FWHM in pixels), so that math lives here as plain functions
+over floats/arrays with no device or FITS dependencies.
+"""
+
+from typing import Union
+
+import numpy as np
+
+
+def plate_scale(
+    pixel_size: Union[float, np.ndarray], focal_length: float
+) -> np.ndarray:
+    """
+    Calculate the plate scale in degrees per pixel.
+
+    Parameters:
+        pixel_size: Pixel size in meters. Scalar or array (e.g. [x, y]).
+        focal_length (float): Focal length in meters.
+
+    Returns:
+        Plate scale in degrees per pixel, same shape as `pixel_size`.
+    """
+    return np.degrees(np.arctan(np.asarray(pixel_size) / focal_length))
+
+
+def field_of_view(
+    sensor_size: Union[float, np.ndarray], focal_length: float
+) -> np.ndarray:
+    """
+    Calculate the field of view spanned by a sensor, without the small-angle
+    approximation.
+
+    Parameters:
+        sensor_size: Sensor extent in meters. Scalar or array (e.g. [sx, sy]).
+        focal_length (float): Focal length in meters.
+
+    Returns:
+        Field of view in degrees, same shape as `sensor_size`.
+    """
+    return 2.0 * np.degrees(np.arctan(np.asarray(sensor_size) / (2.0 * focal_length)))
+
+
+def fov_from_plate_scale(
+    num_pixels: Union[float, np.ndarray], plate_scale_deg_per_pixel: float
+) -> np.ndarray:
+    """
+    Calculate field of view from a per-pixel plate scale (small-angle approximation).
+
+    Parameters:
+        num_pixels: Number of pixels along one or more axes.
+        plate_scale_deg_per_pixel (float): Plate scale in degrees per pixel.
+
+    Returns:
+        Field of view in degrees, same shape as `num_pixels`.
+    """
+    return np.asarray(num_pixels) * plate_scale_deg_per_pixel
+
+
+def fwhm_pixels(plate_scale_deg_per_pixel: float, seeing_arcsec: float = 1) -> float:
+    """
+    Calculate the expected FWHM in pixels for a given atmospheric seeing.
+
+    Parameters:
+        plate_scale_deg_per_pixel (float): Plate scale in degrees per pixel.
+        seeing_arcsec (float): Atmospheric seeing in arcseconds.
+
+    Returns:
+        float: Expected FWHM in pixels.
+    """
+    plate_scale_arcsec_per_pixel = plate_scale_deg_per_pixel * 3600.0
+    return seeing_arcsec / plate_scale_arcsec_per_pixel
diff --git a/src/astra/utils/time.py b/src/astra/utils/time.py
new file mode 100644
index 00000000..8c592cbd
--- /dev/null
+++ b/src/astra/utils/time.py
@@ -0,0 +1,153 @@
+"""Julian Day conversions and astronomical time-frame calculations."""
+
+import math
+from datetime import datetime
+from typing import Any, Tuple
+
+import astropy.units as u
+import numpy as np
+import pandas as pd
+from astropy.coordinates import Angle, SkyCoord
+from astropy.time import Time
+
+
+## for final fits header
+def interpolate_dfs(index: np.ndarray, *data: pd.DataFrame) -> pd.DataFrame:
+    """Interpolate multiple pandas DataFrames onto a common index.
+
+    Merges and interpolates multiple DataFrames using a specified index array,
+    commonly used for wavelength-dependent data processing in spectroscopy.
+
+    Args:
+        index (np.ndarray): 1D array to interpolate data onto (e.g., wavelength grid).
+        *data (pd.DataFrame): Variable number of DataFrames to interpolate.
+
+    Returns:
+        pd.DataFrame: Combined DataFrame with all data interpolated onto the common index.
+    """
+    df = pd.DataFrame({"tmp": index}, index=index)
+    for dat in data:
+        dat = dat[~dat.index.duplicated(keep="first")]
+        df = pd.concat([df, dat], axis=1)
+    df = df.sort_index()
+    df = df.interpolate(method="index", axis=0).reindex(index)
+    df = df.drop(labels="tmp", axis=1)
+
+    return df
+
+
+def __to_format(jd: float, fmt: str) -> float:
+    """Convert Julian Day to specified time format.
+
+    Internal function for converting Julian Day values to different astronomical
+    time formats like Modified Julian Day or Reduced Julian Day.
+
+    Args:
+        jd (float): Julian Day value to convert.
+        fmt (str): Target format ('jd', 'mjd', 'rjd').
+
+    Returns:
+        float: Converted time value in specified format.
+
+    Raises:
+        ValueError: If format string is not recognized.
+    """
+    if fmt.lower() == "jd":
+        return jd
+    elif fmt.lower() == "mjd":
+        return jd - 2400000.5
+    elif fmt.lower() == "rjd":
+        return jd - 2400000
+    else:
+        raise ValueError("Invalid Format")
+
+
+def to_jd(dt: datetime, fmt: str = "jd") -> float:
+    """Convert datetime object to Julian Day using standard algorithm.
+
+    Converts Python datetime to Julian Day format using the algorithm from
+    Wikipedia. Supports conversion to various Julian Day formats.
+
+    Args:
+        dt (datetime): Datetime object to convert.
+        fmt (str): Output format ('jd', 'mjd', 'rjd'). Defaults to 'jd'.
+
+    Returns:
+        float: Julian Day value in specified format.
+    """
+    a = math.floor((14 - dt.month) / 12)
+    y = dt.year + 4800 - a
+    m = dt.month + 12 * a - 3
+
+    jdn = (
+        dt.day
+        + math.floor((153 * m + 2) / 5)
+        + 365 * y
+        + math.floor(y / 4)
+        - math.floor(y / 100)
+        + math.floor(y / 400)
+        - 32045
+    )
+
+    jd = (
+        jdn
+        + (dt.hour - 12) / 24
+        + dt.minute / 1440
+        + dt.second / 86400
+        + dt.microsecond / 86400000000
+    )
+
+    return __to_format(jd, fmt)
+
+
+def getLightTravelTimes(target: SkyCoord, time_to_correct: Time) -> Tuple[Time, Time]:
+    """Calculate light travel times to heliocentric and barycentric frames.
+
+    Computes corrections for light travel time from Earth to the solar system
+    barycenter and heliocenter, essential for precise timing in astronomy.
+
+    Args:
+        target (SkyCoord): Target celestial coordinates.
+        time_to_correct (Time): Observation time requiring correction.
+            Must be initialized with an EarthLocation.
+
+    Returns:
+        Tuple[Time, Time]: Light travel times as (barycentric, heliocentric).
+    """
+
+    ltt_bary = time_to_correct.light_travel_time(target)
+    ltt_helio = time_to_correct.light_travel_time(target, "heliocentric")
+    return ltt_bary, ltt_helio
+
+
+def time_conversion(
+    jd: float, location: Any, target: SkyCoord
+) -> Tuple[float, float, float, str]:
+    """Convert time to various astronomical reference frames.
+
+    Transforms Julian Day to heliocentric and barycentric systems, calculates
+    local sidereal time and hour angle for astronomical observations.
+
+    Args:
+        jd (float): Julian Day to convert.
+        location (EarthLocation): Observer's geographic location.
+        target (SkyCoord): Target celestial coordinates.
+
+    Returns:
+        Tuple[float, float, float, str]: Converted times as
+            (hjd, bjd, lst_seconds, hour_angle_string).
+    """
+
+    time_inp = Time(jd, format="jd", scale="utc", location=location)
+
+    ltt_bary, ltt_helio = getLightTravelTimes(target, time_inp)
+
+    hjd = (time_inp + ltt_helio).value
+    bjd = (time_inp.tdb + ltt_bary).value
+    lst = time_inp.sidereal_time("mean")
+    lstsec = lst.hour * 3600
+    ha = Angle(((((lst - target.ra).hour + 12) % 24) - 12) * u.hourangle).to_string(
+        unit=u.hourangle, sep=" ", pad=True
+    )
+
+    return hjd, bjd, lstsec, ha
diff --git a/tests/test_utils.py b/tests/test_utils.py
index 54d261bf..d22c332c 100644
--- a/tests/test_utils.py
+++ b/tests/test_utils.py
@@ -8,12 +8,11 @@
 from astropy.coordinates import EarthLocation, SkyCoord
 from astropy.time import Time, TimeDelta
 
-from astra.utils import (
+from astra.utils.ephemeris import get_body_coordinates, is_sun_rising
+from astra.utils.time import (
     __to_format,
-    get_body_coordinates,
     getLightTravelTimes,
     interpolate_dfs,
-    is_sun_rising,
     time_conversion,
     to_jd,
 )
@@ -210,7 +209,7 @@ class DummyAltAz:
     def fake_get_sun(time):
         return type("Dummy", (), {"transform_to": lambda self, frame: DummyAltAz()})()
 
-    monkeypatch.setattr("astra.utils.get_sun", fake_get_sun)
+    monkeypatch.setattr("astra.utils.ephemeris.get_sun", fake_get_sun)
 
     rising, flat_ready, position = is_sun_rising(location)
     assert flat_ready is True
@@ -228,7 +227,7 @@ class DummyAltAz:
     def fake_get_sun(time):
         return type("Dummy", (), {"transform_to": lambda self, frame: DummyAltAz()})()
 
-    monkeypatch.setattr("astra.utils.get_sun", fake_get_sun)
+    monkeypatch.setattr("astra.utils.ephemeris.get_sun", fake_get_sun)
 
     rising, flat_ready, position = is_sun_rising(location)
     assert flat_ready is False
@@ -252,7 +251,7 @@ def fake_get_sun(time):
             "Dummy", (), {"transform_to": lambda self, frame: DummyAltAz(next(values))}
         )()
 
-    monkeypatch.setattr("astra.utils.get_sun", fake_get_sun)
+    monkeypatch.setattr("astra.utils.ephemeris.get_sun", fake_get_sun)
 
     rising, _, _ = is_sun_rising(location)
     assert rising is True
@@ -276,7 +275,7 @@ def fake_get_sun(time):
             "Dummy", (), {"transform_to": lambda self, frame: DummyAltAz(next(values))}
         )()
 
-    monkeypatch.setattr("astra.utils.get_sun", fake_get_sun)
+    monkeypatch.setattr("astra.utils.ephemeris.get_sun", fake_get_sun)
 
     rising, _, _ = is_sun_rising(location)
     assert rising is False
diff --git a/tests/test_visibility_check.py b/tests/test_visibility_check.py
index 642d92da..7b3d58cc 100644
--- a/tests/test_visibility_check.py
+++ b/tests/test_visibility_check.py
@@ -227,9 +227,9 @@ def test_visibility_resolution_from_lookup_name(
     from astropy.coordinates import SkyCoord
 
     # The method-under-test imports get_body_coordinates internally:
-    #     from astra.utils import get_body_coordinates
-    # To intercept this, we must ensure 'astra.utils' is loaded in sys.modules
-    # and patch the function on the module object itself.
+    #     from astra.utils.ephemeris import get_body_coordinates
+    # To intercept this, we must ensure 'astra.utils.ephemeris' is loaded in
+    # sys.modules and patch the function on the module object itself.
 
     # 1. Define coordinates that are guaranteed to be known:
     # Start with a Visible target near Zenith (Alt=85 degrees)
@@ -240,10 +240,10 @@ def test_visibility_resolution_from_lookup_name(
 
     mock_get_body = MagicMock(return_value=zenith_radec)
 
-    import astra.utils
+    import astra.utils.ephemeris
 
-    original_func = astra.utils.get_body_coordinates
-    astra.utils.get_body_coordinates = mock_get_body
+    original_func = astra.utils.ephemeris.get_body_coordinates
+    astra.utils.ephemeris.get_body_coordinates = mock_get_body
 
     try:
         config = ObjectActionConfig(
@@ -286,4 +286,4 @@ def test_visibility_resolution_from_lookup_name(
 
     finally:
         # Restore the original function to avoid side effects on other tests
-        astra.utils.get_body_coordinates = original_func
+        astra.utils.ephemeris.get_body_coordinates = original_func
diff --git a/uv.lock b/uv.lock
index 7ecfbda1..cc5f4950 100644
--- a/uv.lock
+++ b/uv.lock
@@ -4,9 +4,7 @@ requires-python = "==3.11.*"
 resolution-markers = [
     "sys_platform == 'win32'",
     "sys_platform == 'emscripten'",
-    "sys_platform == 'darwin'",
-    "platform_machine == 'aarch64' and sys_platform == 'linux'",
-    "(platform_machine != 'aarch64' and sys_platform == 'linux') or (sys_platform != 'darwin' and sys_platform != 'emscripten' and sys_platform != 'linux' and sys_platform != 'win32')",
+    "sys_platform != 'emscripten' and sys_platform != 'win32'",
 ]
 
 [[package]]
@@ -32,8 +30,8 @@ wheels = [
 
 [[package]]
 name = "alpaca-simulators"
-version = "0.2.5"
-source = { git = "https://github.com/ppp-one/alpaca-simulators#b3de2d811f2dd594171f7a4d140bdf617842fdc8" }
+version = "1.1.0"
+source = { git = "https://github.com/ppp-one/alpaca-simulators#a4d9e7717bd0602af1657a50aa5a54653eb7d8fc" }
 dependencies = [
     { name = "cabaret" },
     { name = "fastapi" },
@@ -79,15 +77,15 @@ wheels = [
 
 [[package]]
 name = "anyio"
-version = "4.12.1"
+version = "4.14.2"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "idna" },
     { name = "typing-extensions" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/96/f0/5eb65b2bb0d09ac6776f2eb54adee6abe8228ea05b20a5ad0e4945de8aac/anyio-4.12.1.tar.gz", hash = "sha256:41cfcc3a4c85d3f05c932da7c26d0201ac36f72abd4435ba90d0464a3ffed703", size = 228685, upload-time = "2026-01-06T11:45:21.246Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/61/cc/a381afa6efea9f496eff839d4a6a1aed3bfafc7b3ab4b0d1b243a12573dd/anyio-4.14.2.tar.gz", hash = "sha256:cfa139f3ed1a23ee8f88a145ddb5ac7605b8bbfd8592baacd7ce3d8bb4313c7f", size = 260176, upload-time = "2026-07-12T20:29:07.082Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/38/0e/27be9fdef66e72d64c0cdc3cc2823101b80585f8119b5c112c2e8f5f7dab/anyio-4.12.1-py3-none-any.whl", hash = "sha256:d405828884fc140aa80a3c667b8beed277f1dfedec42ba031bd6ac3db606ab6c", size = 113592, upload-time = "2026-01-06T11:45:19.497Z" },
+    { url = "https://files.pythonhosted.org/packages/da/35/f2287558c17e29fafc8ef3daf819bb9834061cfa43bff8014f7df7f63bdc/anyio-4.14.2-py3-none-any.whl", hash = "sha256:9f505dda5ac9f0c8309b5e8bd445a8c2bf7246f3ce950121e45ea15bc41d1494", size = 125813, upload-time = "2026-07-12T20:29:05.763Z" },
 ]
 
 [[package]]
@@ -150,21 +148,21 @@ test = [
 [package.metadata]
 requires-dist = [
     { name = "alpyca", specifier = ">=3.1.2,<4" },
-    { name = "astrafocus", specifier = ">=0.1.1" },
+    { name = "astrafocus", specifier = ">=0.1.5" },
     { name = "astropy" },
-    { name = "cabaret" },
+    { name = "cabaret", specifier = ">=0.5.4" },
     { name = "donuts" },
     { name = "fastapi", extras = ["standard"] },
     { name = "jinja2" },
     { name = "matplotlib" },
     { name = "pandas" },
-    { name = "photutils" },
+    { name = "photutils", specifier = ">=3.0.0" },
     { name = "psutil" },
     { name = "pyyaml" },
     { name = "ruamel-yaml" },
     { name = "scipy" },
     { name = "sqlite3worker" },
-    { name = "twirl", specifier = ">=0.4.1" },
+    { name = "twirl", specifier = ">=0.5.2" },
 ]
 
 [package.metadata.requires-dev]
@@ -194,10 +192,11 @@ test = [
 
 [[package]]
 name = "astrafocus"
-version = "0.1.1"
+version = "0.1.5"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "astropy" },
+    { name = "cabaret" },
     { name = "numpy" },
     { name = "opencv-python" },
     { name = "pandas" },
@@ -205,15 +204,16 @@ dependencies = [
     { name = "scipy" },
     { name = "sqlite3worker" },
     { name = "statsmodels" },
+    { name = "tqdm" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/65/56/c40b5b71d88565b63da17284e64396784203e2bf693344c9a25c6845b2c0/astrafocus-0.1.1.tar.gz", hash = "sha256:444496b6ebe43aa53e48d487156195938be6fce1950b8d7a09f213c0c6fd2224", size = 652943, upload-time = "2025-09-21T09:14:17.456Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/c6/32/4fde202b60fe326add46a443e75bfd056672a27e60249e29b393a939ff7c/astrafocus-0.1.5.tar.gz", hash = "sha256:51626117f8fa4b66e65e7997ac542d2a2d58bc4ff595b1271d5994209b7c022b", size = 847119, upload-time = "2026-07-21T15:57:04.37Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/71/97/d0cdae2b98cca8d861b9203cbf3cd1ca6a453b91c172ec569381339afcf1/astrafocus-0.1.1-py3-none-any.whl", hash = "sha256:99fb905febf8e578cf641d4e86a1c2599fc3f2c289ab3feba7617330fcdfc902", size = 70925, upload-time = "2025-09-21T09:14:16.172Z" },
+    { url = "https://files.pythonhosted.org/packages/af/c2/d4cc5134984dd28355edcea46fa23770468a5266b7c89d81a6ce8b3ea019/astrafocus-0.1.5-py3-none-any.whl", hash = "sha256:daf33102f29e453034ef877cf47a9bc141ac1349a04123347313b5d6563f885f", size = 79695, upload-time = "2026-07-21T15:57:02.715Z" },
 ]
 
 [[package]]
 name = "astropy"
-version = "7.2.0"
+version = "7.2.2"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "astropy-iers-data" },
@@ -222,25 +222,25 @@ dependencies = [
     { name = "pyerfa" },
     { name = "pyyaml" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/7b/92/2dce2d48347efc3346d08ca7995b152d242ebd170c571f7c9346468d8427/astropy-7.2.0.tar.gz", hash = "sha256:ae48bc26b1feaeb603cd94bd1fa1aa39137a115fe931b7f13787ab420e8c3070", size = 7057774, upload-time = "2025-11-25T22:36:41.916Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/24/3a/33c6c5c34d366edbb74bc01b9b95d9956686f6d3c54eff9e405703a77e64/astropy-7.2.2.tar.gz", hash = "sha256:d48d6025636fa1330594603b9c01345561b6da404e2115852a9136db13f455cd", size = 7095594, upload-time = "2026-07-05T07:17:32.464Z" }
 wheels = [
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-    { url = "https://files.pythonhosted.org/packages/a6/ba/3418133ba144dfcd1530bca5a6b695f4cdd21a8abaaa2ac4e5450d11b028/astropy-7.2.0-cp311-abi3-macosx_11_0_arm64.whl", hash = "sha256:52e9a7d9c86b21f1af911a2930cd0c4a275fb302d455c89e11eedaffef6f2ad0", size = 6413656, upload-time = "2025-11-25T22:36:26.548Z" },
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+version = "0.2026.7.20.15.31.18"
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 [[package]]
@@ -263,20 +263,20 @@ wheels = [
 
 [[package]]
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