diff --git a/docs/observation.md b/docs/observation.md
index e2dd4fb..3796f3c 100644
--- a/docs/observation.md
+++ b/docs/observation.md
@@ -10,7 +10,7 @@ FOXSI-4 was launched from the Poker Flat Research Range in Alaska on April 17, 2
 
 *Relevant for functions with inputs like: `time_range`.*
 
-The time range is important for calculating the atmospheric transmission, which is a key component of the ARF. Depending on the objectives of the analysis, the user may select the appropriate time range using the information provided below:
+The time range is important for calculating the atmospheric transmission (see the [Time ranges and the atmospheric response](https://foxsi.github.io/response-tools/auto_examples/plot_atmospheric_response.html#sphx-glr-auto-examples-plot-atmospheric-response-py) example), which is a key component of the ARF. Depending on the objectives of the analysis, the user may select the appropriate time range using the information provided below:
 
 **Observation time intervals with stable pointing for more than a few seconds:**
 
diff --git a/docs/response_guide.md b/docs/response_guide.md
index 35a3b70..2c32acc 100644
--- a/docs/response_guide.md
+++ b/docs/response_guide.md
@@ -28,7 +28,7 @@ For a coded runthrough of creating an ARF, see the [Create an ARF from scratch](
 
 ## What is a redistribution matrix function/file (RMF)?
 
-The RMF is a matrix that contains the energy redistribution information of the detector, this is the photon-to-count conversion probability. An incoming photon of energy $\epsilon$ can be detected by the telescope's sensor as a count with an energy $\lesssim\epsilon$ (say, $E$) due to scattering, detection efficiency, and energy resolution. Therefore, the energies we are interested in are the ones defined for the RMF creation. I.e., the defined photon energies (input axis) controls the energies the ARF and photon models should be evaluated and the defined count bin energies (observable bins or output axis) controls the binning of the observed data.
+The RMF is a matrix that contains the energy redistribution information of the detector, this is the photon-to-count conversion probability. An incoming photon of energy $\epsilon$ can be detected by the telescope's sensor as a count with an energy $\lesssim\epsilon$ (say, $E$) due to scattering, detection efficiency, and energy resolution. Therefore, the energies we are interested in are the ones defined for the RMF creation. I.e., the defined photon energies (input axis) control the energies of the ARF and are also the energies at which to evaluate any photon models; whereas, the defined count bin energies (observable bins or output axis) control the binning of the observed data.
 
 Example RMFs for the CdTe and CMOS can be seen below (see the [Example FOXSI-4 RMFs](https://foxsi.github.io/response-tools/auto_examples/plot_rmf_examples.html#sphx-glr-auto-examples-plot-rmf-examples-py) example in the example gallery) which show the conversion probability of a photon being recorded as detector observable (i.e., either a count with a calibrated energy or DN). The units of a response matrix will be observable/photon (e.g., counts/photon or DN/photon).
 
diff --git a/docs/versions_and_releases.md b/docs/versions_and_releases.md
index ff1253d..16555ed 100644
--- a/docs/versions_and_releases.md
+++ b/docs/versions_and_releases.md
@@ -13,6 +13,32 @@ import response_tools
 print(response_tools.__version__)
 ```
 
+## Version `1.0.3`
+
+[5 Nov. 2025] Updates to the thermal blanketing transmission files and the atmospheric attenuation file.
+
+### Attenuation
+
+- att_thermal_blanket:
+  - No longer exists.
+- att_early_cmos_prefilter
+  - `v1`: attenuation-data/F4_Blanket_transmission_v1.dat
+    - Was att_thermal_blanket.
+- att_modeled_thermal_blanket
+  - `v1`: attenuation-data/FOXSI4_theoretical_thermal_blanket_transmission_v1.fits
+    - Modeled attenuation for the thermal blanket.
+- att_measured_thermal_blanket
+  - `v1`: attenuation-data/FOXSI4_measured_thermal_blanket_transmission_v1.fits
+    - Measured attenuation for the thermal blanket.
+- att_foxsi4_atmosphere:
+  - `v2`: attenuation-data/FOXSI4_atmospheric_transmission_v2.fits
+    - Times can now be selected via seconds from launch or in UTC.
+
+### Example gallery
+
+- Time ranges and the atmospheric response
+  - Example added showing how to choose a time range in the code for atmospheric transmissions.
+
 ## Version `1.0.2`
 
 [30 Oct. 2025] CdTe detector response files now share the same filename format across different versions and are now all supported in the codebase.
@@ -147,3 +173,16 @@ print(response_tools.__version__)
 - qe_cmos_telescope-1:
   - `v1`: quantum-efficiency-data/foxsi4_telescope-1_BASIC_sensor_quantum_efficiency_v1.fits
     - CMOS team prepared detector quantum efficiencies for telescope 1.
+
+### Example gallery
+
+- Functions & Outputs
+  - Shows how to use the functions and their outputs in the package.
+- Create an ARF from scratch
+  - Shows how to compile a telescope ARF from individual components.
+- Example FOXSI-4 RMFs
+  - Shows how to obtain and work with a detector's RMF data-class.
+- Generating and plotting ARFs, RMFs, and SRMs
+  - Shows how to obtain and plot the ARF, RMF, and SRM for Telescope 2.
+- Telescope ARFs, RMFs, and SRMs
+  - Shows a test ``asset`` function to produce a response plot for all of FOXSI-4's telescopes.
diff --git a/examples/README.rst b/examples/README.rst
index c9268dd..53d83e4 100644
--- a/examples/README.rst
+++ b/examples/README.rst
@@ -21,6 +21,11 @@ Data handling examples
   * File: ``plot_rmf_examples.py``.
   * Shows how to obtain and work with a detector's RMF data-class.
 
+* **Time ranges and the atmospheric response**
+
+  * File: ``plot_atmospheric_response.py``.
+  * Shows how to choose a time range in the code for atmospheric transmissions.
+
 Plotting examples
 -----------------
 
diff --git a/examples/plot_atmospheric_response.py b/examples/plot_atmospheric_response.py
new file mode 100644
index 0000000..a5fefbb
--- /dev/null
+++ b/examples/plot_atmospheric_response.py
@@ -0,0 +1,324 @@
+"""
+Time ranges and the atmospheric response
+========================================
+
+Script showing how a user might want to consider choosing their analysis
+time range in relation to the atmospheric response.
+
+Check out the `Choosing a time range <https://foxsi.github.io/response-tools/observation.html#choosing-a-time-range>`__ 
+section in the online documentation for more details on other 
+observational time range factors to consider.
+
+Throughout FOXSI-4's flight, there are different amounts of atmosphere 
+in the line of sight that will attenuate the incoming X-rays. This 
+example will show how to get to the atmopspheric response values.
+
+The example originally followed the plot produced from 
+``response_tools.attenuation.asset_atm()``.
+
+*Note:* The atmospheric response is included in the level 3 ARF response
+functions that have "flight" in their name. E.g, 
+``response_tools.responses.foxsi4_telescope0_flight_arf`` which will 
+require a ``time_range`` input to work.
+"""
+
+from astropy.time import Time
+from astropy.visualization import time_support
+import astropy.units as u
+from matplotlib.dates import DateFormatter
+import matplotlib.pyplot as plt
+import numpy as np
+
+from response_tools.attenuation import att_foxsi4_atmosphere
+
+time_support()
+
+# %%
+# The function documentation
+# --------------------------
+#
+# The documentation for any function in ``response-tools`` can be found
+# online `here <https://foxsi.github.io/response-tools/modules.html>`__. 
+#
+# The ``response_tools.attenuation.att_foxsi4_atmosphere`` documentation 
+# can be found `here <https://foxsi.github.io/response-tools/response_tools.html#response_tools.attenuation.att_foxsi4_atmosphere>`__, 
+# but we can also use the ``help`` function.
+
+help(att_foxsi4_atmosphere)
+
+# %%
+# Using the function
+# ------------------
+#
+# Here it is seen the function would like energies and a time range. A 
+# user can also see there are some helpful time markers like the observation 
+# start (shutter door open) and observation end (shutter door closed). 
+# Notice these times are slightly early and late of the first and last 
+# time interval shown in the `Choosing a time range <https://foxsi.github.io/response-tools/observation.html#choosing-a-time-range>`__ 
+# table, respectively.
+#
+# Before getting into time ranges, a user might want to only sample one 
+# energy, or several, and inspect the time profile of the attenuation.
+#
+# To get all times back from the function, specific times don't need to 
+# known. The function will return all times if ``numpy.nan`` is passed 
+# (but it still needs to be unit-aware).
+#
+# So let's get the atmsopheric transmission at 1 keV for the full 
+# flight:
+
+energy0, time0 = [1]<<u.keV, np.nan<<u.second
+atm0 = att_foxsi4_atmosphere(mid_energies=energy0, 
+                             time_range=time0)
+
+#%%
+# Now ``atm0`` stores the transmission time rpofile for 1 keV photons.
+#
+# A user might want to inspect the time profile for several energies. To
+# do this, an energy array can be passed which will return a matrix in 
+# the ``transmission`` attribute where the rows are times and the 
+# columns are the different energy transmissions.
+
+energy1 = [1, 3, 5, 10, 15]<<u.keV
+atm1 = att_foxsi4_atmosphere(mid_energies=energy1, 
+                             time_range=time0)
+
+# %%
+# A user will likely want to select times out of the time profile, so 
+# let's start defining some times in which a user might be interested. 
+# Say, the time range spanning the whole observation time:
+
+# in seconds since launch
+obs_start2end_sec = [100, 461] << u.second
+# in UTC
+obs_start2end_utc = Time(["2024-04-17T22:14:40", 
+                          "2024-04-17T22:20:41"], 
+                         format='isot', 
+                         scale='utc')
+
+#%%
+# Atmospheric transmission time profile
+# -------------------------------------
+#
+# This information can be visualised (although plotting code always 
+# looks messy):
+
+fig = plt.figure(figsize=(8,6))
+gs_ax0 = plt.gca()
+
+# transmissions from only passing one energy
+p0 = gs_ax0.plot(atm0.times_utc, 
+                 atm0.transmissions, 
+                 ls=":", 
+                 label=f"energy:{energy0:latex}\ntime:{time0:latex}", 
+                 lw=3)
+# plot the different energy transmission time profiles
+p1 = []
+for i in range(len(energy1)):
+    p1 += gs_ax0.plot(atm1.times_utc, 
+                      atm1.transmissions[:,i], 
+                      ls="-", 
+                      label=f"energy:{energy1[i]:latex}")
+
+# label bookkeeping
+gs_ax0.set_ylabel(f"Transmission [{atm0.transmissions.unit:latex}]")
+gs_ax0.set_xlabel(f"UTC Time (Obs. start~{obs_start2end_utc[0]})")
+gs_ax0.set_ylim([0,1.05])
+gs_ax0.set_xlim([atm1.times_utc[0], atm1.times_utc[-1]])
+gs_ax0.set_title("Sampled energy band transmission vs. time")
+gs_ax0.xaxis.set_major_formatter(DateFormatter("%H:%M:%S"))
+# would like to display seconds since launch along the top of the plot
+gs_ax0b = gs_ax0.twiny()
+gs_ax0b_color = "grey"
+_ = gs_ax0b.plot(atm0.times, atm0.transmissions, lw=0)
+v0 = gs_ax0b.axvline(obs_start2end_sec[0].value, ls="-.", c="k", label="obs. start")
+v2 = gs_ax0b.axvline(obs_start2end_sec[-1].value, ls="-.", c="k", label="obs. end")
+gs_ax0b.set_xlabel(f"Time (Obs. start~100 s) [{atm0.times.unit:latex}]", color=gs_ax0b_color)
+gs_ax0b.set_xlim([atm1.times[0].value, atm1.times[-1].value])
+gs_ax0b.tick_params(axis="x", labelcolor=gs_ax0b_color, color=gs_ax0b_color, which="both")
+# let's put the timestamps on the plot
+_y_time_loc = 0.3
+_x_offset = 4 << u.second
+gs_ax0.annotate(f"{obs_start2end_utc[0].strftime('%H:%M:%S')} UTC", (obs_start2end_utc[0]+_x_offset, _y_time_loc), rotation=90)
+gs_ax0.annotate(f"{obs_start2end_utc[-1].strftime('%H:%M:%S')} UTC", (obs_start2end_utc[-1], _y_time_loc), rotation=90, ha="right")
+_y_sec_loc = _y_time_loc+0.25
+gs_ax0b.annotate(f"{obs_start2end_sec[0]:.0f}", (obs_start2end_sec[0].value+_x_offset.value, _y_sec_loc), color=gs_ax0b_color, rotation=90)
+gs_ax0b.annotate(f"{obs_start2end_sec[-1]:.0f}", (obs_start2end_sec[-1].value, _y_sec_loc), color=gs_ax0b_color, rotation=90, ha="right")
+# make sure to show the legend
+plt.legend(handles=p0+p1+[v0,v2])
+plt.tight_layout()
+plt.show()
+
+#%%
+# Atmospheric transmission for a time range
+# -----------------------------------------
+#
+# A user can decide they do not care about the energy range for the 
+# transmission. (Note: this should be decided by the 
+# `RMF <https://foxsi.github.io/response-tools/response_guide.html#what-is-a-redistribution-matrix-function-file-rmf>`__.)
+#
+# Similar to what was seen for the ``time_range`` input, ``numpy.nan`` 
+# can be passed (unit-aware). Let's average over the whole time range:
+
+energy2 = np.nan<<u.keV
+atm2 = att_foxsi4_atmosphere(mid_energies=energy2, 
+                             time_range=obs_start2end_sec)
+
+#%%
+# Since the ``time_range`` can be given as seconds since launch and in
+# Astropy UTC, the following code is equivalent to the above for 
+# ``atm2``:
+
+atm2a = att_foxsi4_atmosphere(mid_energies=energy2, 
+                              time_range=obs_start2end_utc)
+
+#%%
+# A user can also just return all transmissions at all energies and 
+# times from the original file containing the data:
+
+energy3, time3 = np.nan<<u.keV, np.nan<<u.second
+atm3 = att_foxsi4_atmosphere(mid_energies=energy3, 
+                             time_range=time3)
+
+#%%
+# This can be plotted of course to get a better idea of what is going 
+# on.
+#
+# Let's plot the averaged transmissions over the whole observation at 
+# the native file energy resolution (``atm2``) and then let's use 
+# ``atm3`` to sample some transmission curves at some "random" 
+# instantaneous times by indexing.
+#
+# Let's mark where the CdTe and CMOS detector telescope energies ranges 
+# lie on the plot for more context.
+
+fig = plt.figure(figsize=(8,6))
+gs_ax1 = plt.gca()
+
+# whole time range average
+p2 = gs_ax1.plot(atm2.mid_energies, 
+                 atm2.transmissions, 
+                 ls="-",
+                 lw=3, 
+                 label=f"time range:{obs_start2end_sec:latex}")
+p2a = gs_ax1.plot(atm2a.mid_energies, 
+                  atm2a.transmissions, 
+                  ls="-.", 
+                  label=f"time range:{obs_start2end_utc}")
+# random instantaneous times
+p3 = gs_ax1.plot(atm3.mid_energies, 
+                 atm3.transmissions[:, 2000], 
+                 ls="-", 
+                 label=f"time:{atm3.times[2000]:latex}")
+p4 = gs_ax1.plot(atm3.mid_energies, 
+                 atm3.transmissions[:, 5600], 
+                 ls="-", 
+                 label=f"time:{atm3.times[5600]:latex}")
+p5 = gs_ax1.plot(atm3.mid_energies, 
+                 atm3.transmissions[:, 9200], 
+                 ls="-", 
+                 label=f"time:{atm3.times[9200]:latex}")
+
+# let's show the CdTe and CMOS energies on this plot for conntext
+cmos_le = 0.8<<u.keV
+v3 = gs_ax1.axvline(cmos_le.value, ls="-.", c="k", label="CMOS energies")
+gs_ax1.arrow(cmos_le.value, 0.85, 1, 0, length_includes_head=True, head_width=0.02, head_length=0.2, color="k")
+cdte_le = 4<<u.keV
+v4 = gs_ax1.axvline(cdte_le.value, ls=":", c="k", label="CdTe energies")
+gs_ax1.arrow(cdte_le.value, 0.85, 5, 0, length_includes_head=True, head_width=0.02, head_length=1, color="k")
+# some label bookkeeping
+gs_ax1.set_ylabel(f"Transmission [{atm3.transmissions.unit:latex}]")
+gs_ax1.set_xlabel(f"Energy [{atm3.mid_energies.unit:latex}]")
+gs_ax1.set_ylim([0,1.05])
+gs_ax1.set_xlim([0.01, 30])
+gs_ax1.set_xscale("log")
+gs_ax1.set_title("Time averaged and time sampled transmission vs. energy")
+plt.legend(handles=p2+p2a+p3+p4+p5+[v3,v4])
+plt.tight_layout()
+plt.show()
+
+#%%
+# Atmospheric transmission with different energy resolutions
+# ----------------------------------------------------------
+#
+# From the above plot, the user can see quite a bit of detail in the 
+# time profiles but a user will likely wish to match the energy 
+# resolution to that of the relevant telescope RMF.
+#
+# So, let's show the full observation time curve with different energy 
+# resolutions:
+
+# some random energies
+energy4 = [0.01, 0.02, 0.05, 0.1, 0.3, 0.5, 1, 3, 5, 10, 15, 30]<<u.keV
+atm4 = att_foxsi4_atmosphere(mid_energies=energy4, 
+                             time_range=obs_start2end_sec)
+
+#%%
+# Let's also consider the CdTe RMF energy resolution:
+
+energy5 = np.arange(3,30.1, 0.1)<<u.keV
+atm5 = att_foxsi4_atmosphere(mid_energies=energy5, 
+                             time_range=obs_start2end_sec)
+
+#%% 
+# Let's plot this to see how it looks:
+
+fig = plt.figure(figsize=(8,6))
+gs_ax2 = plt.gca()
+
+# random energy resolution
+colour4 = "blue"
+p6 = gs_ax2.plot(atm4.mid_energies, 
+                 atm4.transmissions, 
+                 label=f"time range:{atm4.times[0]:.2f}$-${atm4.times[-1]:.2f}\nrandom-ish energy sampling", 
+                 marker="x", 
+                 ms=4, 
+                 c=colour4)
+# CdTe energy resolution
+colour5 = "orange"
+gs_ax2.plot(atm5.mid_energies, 
+            atm5.transmissions, 
+            label=f"time range:{atm5.times[0]:.2f}$-${atm5.times[-1]:.2f}\nCdTe range+response resolution", 
+            marker="x", 
+            ms=2, 
+            c=colour5)
+
+# add the lowest energies of the detectors
+v5 = gs_ax2.axvline(cmos_le.value, ls="-.", c="k", label="CMOS energies")
+gs_ax2.arrow(cmos_le.value, 0.85, 1, 0, length_includes_head=True, head_width=0.02, head_length=0.2, color="k")
+v6 = gs_ax2.axvline(cdte_le.value, ls=":", c="k", label="CdTe energies")
+gs_ax2.arrow(cdte_le.value, 0.85, 5, 0, length_includes_head=True, head_width=0.02, head_length=1, color="k")
+
+# inset Axes for the CdTe plot
+x1, x2, y1, y2 = 2.5, 30, 0.95, 1.04  # subregion of the original image
+axins = gs_ax2.inset_axes([0.4, 0.35, 0.5, 0.4],
+                            xlim=(x1, x2), 
+                            ylim=(y1, y2)) #, xticklabels=[], yticklabels=[])
+axins.plot(atm4.mid_energies, atm4.transmissions, label=f"time range:{atm4.times[0]:.2f}$-${atm4.times[-1]:.2f}\nrandom-ish energy sampling", marker="x", ms=6, c=colour4)
+p7 = axins.plot(atm5.mid_energies, atm5.transmissions, label=f"time range:{atm5.times[0]:.2f}$-${atm5.times[-1]:.2f}\nCdTe range+response resolution", marker="x", ms=4, c=colour5)
+axins.set_xscale("log")
+_rectangle, _connectors = gs_ax2.indicate_inset_zoom(axins, edgecolor="black")
+# edit the connecting lines so they make sense
+_connectors[0].__dict__["_visible"] = False 
+_connectors[1].__dict__["_visible"] = True
+# add the lowest energies of the detectors to the inset plot
+_ = axins.axvline(cmos_le.value, ls="-.", c="k", label="CMOS energies")
+axins.arrow(cmos_le.value, 0.96, 1, 0, length_includes_head=True, head_width=0.01, head_length=0.2, color="k")
+_ = axins.axvline(cdte_le.value, ls=":", c="k", label="CdTe energies")
+axins.arrow(cdte_le.value, 0.96, 5, 0, length_includes_head=True, head_width=0.01, head_length=1, color="k")
+
+# some label bookkeeping
+gs_ax2.set_ylabel(f"Transmission [{atm3.transmissions.unit:latex}]")
+gs_ax2.set_xlabel(f"Energy [{atm3.mid_energies.unit:latex}]")
+gs_ax2.set_ylim([0,1.05])
+gs_ax2.set_xlim([0.01, 30])
+gs_ax2.set_xscale("log")
+gs_ax2.set_title("Time averaged transmission vs. sampled energy")
+plt.legend(handles=p6+p7+[v5,v6])
+plt.tight_layout()
+plt.show()
+
+#%%
+# It can be seen that the resolution smooths over a lot of the behavious 
+# and it can also be seen that the atmosphere has very little effect in 
+# the CdTe energy range.
\ No newline at end of file
diff --git a/response_tools/assets/response-tools-figs/att-figs/atmospheric-transmissions.png b/response_tools/assets/response-tools-figs/att-figs/atmospheric-transmissions.png
index 19b06af..3302585 100644
Binary files a/response_tools/assets/response-tools-figs/att-figs/atmospheric-transmissions.png and b/response_tools/assets/response-tools-figs/att-figs/atmospheric-transmissions.png differ
diff --git a/response_tools/assets/response-tools-figs/att-figs/transmissions.png b/response_tools/assets/response-tools-figs/att-figs/transmissions.png
index 66f8171..f51299e 100644
Binary files a/response_tools/assets/response-tools-figs/att-figs/transmissions.png and b/response_tools/assets/response-tools-figs/att-figs/transmissions.png differ
diff --git a/response_tools/attenuation.py b/response_tools/attenuation.py
index e8b9ec4..94958ef 100644
--- a/response_tools/attenuation.py
+++ b/response_tools/attenuation.py
@@ -8,7 +8,10 @@
 import warnings
 
 from astropy.io import fits
+from astropy.time import core, Time
+from astropy.visualization import time_support
 import astropy.units as u
+from matplotlib.dates import DateFormatter
 import matplotlib.pyplot as plt
 import matplotlib.gridspec as gridspec
 import numpy as np
@@ -36,12 +39,69 @@ class AttOutput(BaseOutput):
     mid_energies: u.Quantity = np.nan<<u.keV
     off_axis_angle: u.Quantity = np.nan<<u.arcmin
     times: u.Quantity = np.nan<<u.second
+    times_utc: core.Time = None
 
 # thermal blanket attenuation
 @u.quantity_input(mid_energies=u.keV)
-def att_thermal_blanket(mid_energies, file=None):
+def att_thermal_blanket(mid_energies, use_model=False, file=None): 
     """Thermal blanket transmittance interpolated to the given energies.
 
+    Parameters
+    ----------
+    mid_energies : `astropy.units.quantity.Quantity`
+        The energies at which the transmission is required. If 
+        `numpy.nan<<astropy.units.keV` is passed then an entry for all 
+        native file energies are returned. 
+        Unit must be convertable to keV.
+
+    use_model : `bool`
+        Defines whether to use the measured values for the thermal
+        blanket (False) or the modelled values (True).
+        Default: False
+
+    file : `str` or `None`
+        Gives the ability to provide custom files for the information. 
+        Default: None
+
+    Returns
+    -------
+    : `AttOutput`
+        An object containing the energies for each transmission, the 
+        transmissions, and more. See accessible information using 
+        `.contents` on the output.
+    """
+    if not use_model:
+        _f = os.path.join(FILE_PATH, RESPONSE_INFO_TYPE["att_measured_thermal_blanket"]) if file is None else file
+        with fits.open(_f) as hdul:
+            att_es = hdul[1].data["ENERGY"][0] << u.keV
+            att_values = hdul[1].data["MEASURED_TRANS"][0] << u.dimensionless_unscaled
+    else:
+        _f = os.path.join(FILE_PATH, RESPONSE_INFO_TYPE["att_modeled_thermal_blanket"]) if file is None else file
+        with fits.open(_f) as hdul:
+            att_es = hdul[1].data["ENERGY"][0] << u.keV
+            att_values = hdul[1].data["THEO_TRANS"][0] << u.dimensionless_unscaled
+    
+    mid_energies = native_resolution(native_x=att_es, input_x=mid_energies)
+
+    return AttOutput(filename=_f,
+                     function_path=f"{sys._getframe().f_code.co_name}",
+                     mid_energies=mid_energies,
+                     transmissions=np.interp(mid_energies.value, 
+                                             att_es.value, 
+                                             att_values.value, 
+                                             left=0, 
+                                             right=1) << u.dimensionless_unscaled,
+                     attenuation_type="Thermal-Blanket",
+                     model=use_model,
+                     )
+
+# early version of the thermal blanket attenuation
+@u.quantity_input(mid_energies=u.keV)
+def att_early_thermal_blanket(mid_energies, file=None): 
+    """Early version of the thermal blanket transmission.
+
+    You're likely looking for `att_thermal_blanket`.
+
     Parameters
     ----------
     mid_energies : `astropy.units.quantity.Quantity`
@@ -61,7 +121,8 @@ def att_thermal_blanket(mid_energies, file=None):
         transmissions, and more. See accessible information using 
         `.contents` on the output.
     """
-    _f = os.path.join(FILE_PATH, RESPONSE_INFO_TYPE["att_thermal_blanket"]) if file is None else file
+    logging.warning(f"Caution: This might not be the function ({sys._getframe().f_code.co_name}) you are looking for, please see `att_thermal_blanket`.")
+    _f = os.path.join(FILE_PATH, RESPONSE_INFO_TYPE["att_early_thermal_blanket"]) if file is None else file
     att = scipy.io.readsav(_f)
     att_es, att_values = att["energy_kev"] << u.keV, att["f4_transmission"] << u.dimensionless_unscaled
     mid_energies = native_resolution(native_x=att_es, input_x=mid_energies)
@@ -74,7 +135,7 @@ def att_thermal_blanket(mid_energies, file=None):
                                              att_values.value, 
                                              left=0, 
                                              right=1) << u.dimensionless_unscaled,
-                     attenuation_type="Thermal-Blanket",
+                     attenuation_type="Early-Thermal-Blanket",
                      model=True,
                      )
 
@@ -420,27 +481,9 @@ def att_cmos_collimator_ratio(off_axis_angle, telescope=None, file=None):
                      model=True,
                      )
 
-@u.quantity_input(mid_energies=u.keV, time_range=u.second)
+@u.quantity_input(mid_energies=u.keV)
 def att_foxsi4_atmosphere(mid_energies, time_range=None, file=None):
-    """ 
-    Atmsopheric attenuation from and for FOXSI-4 flight data.
-
-    energy = array containing energy in keV for energies 0.01 - 30 keV. Array has 506 elements
-		 
-    atmospheric_trans = array containing transmission for all energy values in energy array.
-                        Transmission is calculated for 10284 times covering the FOXSI-4 flight. 
-                        Array shape is: [10284,506] which corresponds to transmission for [time,energy]
-                        
-                        Launch time t = 0 corresponds to  index [0,*] 
-                        Observation starts at t = 100s corresponds to index [2000,*]
-                        Approximate middle of observation at t = 280s corresponds to index [5600,*]
-                        End of observation at t = 461s corresponds to index [9200,*] 
-
-
-    Units in the FITS header needs to change from keV->eV
-    Need an array of times included
-    -> 10,284 entries and t=0 is index `0` while t=100 is index `2000`
-    -> final time is 100/2000 * 10284 = 514.2
+    """ Atmsopheric attenuation from and for FOXSI-4 flight data.
 
     Parameters
     ----------
@@ -456,7 +499,14 @@ def att_foxsi4_atmosphere(mid_energies, time_range=None, file=None):
         `[numpy.nan, numpy.nan]<<astropy.units.second` then the full 
         time will be considered and the output will not be averaged but 
         a grid of the transmissions at all times and at any provided
-        energies.
+        energies. Should be of length 2. Can be given as seconds since 
+        launch or as UTC time as well either as a string or and Astropy 
+        time.
+        - Observation start: 100<<astropy.units.second
+        - Observation end: 461<<astropy.units.second
+        - String format: YYYY-mm-ddTHH:MM:SS
+        - Observation start: 2024-04-17T22:14:40
+        - Observation end: 2024-04-17T22:20:41
     
     file : `str` or `None`
         Path/name of a custom file wanting to be loaded in as the 
@@ -468,20 +518,49 @@ def att_foxsi4_atmosphere(mid_energies, time_range=None, file=None):
         An object containing the energies for each transmission, the 
         transmissions, and more. See accessible information using 
         `.contents` on the output.
+
+    Example
+    -------
+    >>> from astropy.time import Time
+    >>> import astropy.units as u
+    # two equivalent calls to the function
+    # using Astorpy times
+    >>> a0 = att_foxsi4_atmosphere([np.nan]<<u.keV,
+                                   time_range=Time(["2024-04-17T22:14:40",
+                                                    "2024-04-17T22:20:41"],
+                                                   format='isot',
+                                                   scale='utc'))
+    # using seconds from launch values (unit-aware)
+    >>> a1 = att_foxsi4_atmosphere([np.nan]<<u.keV,
+                                   time_range=[100,
+                                               461]<<u.second)
     """
-    if (time_range is None) or np.all(np.isnan(time_range)):
-        time_range = [np.nan, np.nan] << u.second
-    
-    if (len(time_range)!=2):
-        warnings.warn(f"{sys._getframe().f_code.co_name} `time_range` (convertable to astropy.units.seconds) should be of length 2.")
-        return
 
     _f = os.path.join(FILE_PATH, RESPONSE_INFO_TYPE["att_foxsi4_atmosphere"]) if file is None else file
     with fits.open(_f) as hdul:
-        native_times, native_energies, transmission = hdul[1].data["TIME"][0]<<u.second, (hdul[1].data["ENERGY"][0]<<u.eV)<<u.keV, hdul[1].data["ATMOSPHERIC_TRANS"][0]<<u.dimensionless_unscaled
+        native_times, _native_utc, native_energies, transmission = hdul[1].data["TIME"][0]<<u.second, hdul[1].data["TIME_UTC"][0], (hdul[1].data["ENERGY"][0]<<u.eV)<<u.keV, hdul[1].data["ATMOSPHERIC_TRANS"][0]<<u.dimensionless_unscaled
 
         # assume some sort of uniform uniform binning
         en_res = np.mean(np.diff(native_energies))
+        _native_utc_with_dec = [i if "." in i else f"{i}." for i in _native_utc] 
+        _native_utc_isot = [f"{i:0<34}" for i in _native_utc_with_dec]
+        native_utc = Time(_native_utc_isot, format='isot', scale='utc')
+
+    # handle time
+    if type(time_range)==core.Time:
+        # get UTC time in Astropy
+        user_utc = Time(time_range, format='isot', scale='utc')
+
+        # use first time in `native_times` as reference
+        time_range = (user_utc - native_utc[0]).sec << u.second
+    elif (time_range is None) or np.all(np.isnan(time_range)):
+        time_range = [np.nan, np.nan] << u.second
+    else:
+        assert (type(time_range)==u.Quantity) and ((time_range<<u.second).unit==u.second), f"{sys._getframe().f_code.co_name}: `time_range` should be an array of length 2 and either an Astropy Times list or a `astropy.units.second` (or convertable to) unit list"
+    
+    if (len(time_range)!=2):
+        warnings.warn(f"{sys._getframe().f_code.co_name}: `time_range` should be of length 2.")
+        return
 
     # if the time range is nothing them just want all the times, deal with energies separately
     if np.all(np.isnan(time_range)):
@@ -491,6 +570,7 @@ def att_foxsi4_atmosphere(mid_energies, time_range=None, file=None):
                              function_path=f"{sys._getframe().f_code.co_name}",
                              mid_energies=native_energies<<u.keV,
                              times=native_times,
+                             times_utc=native_utc,
                              transmissions=transmission,
                              attenuation_type="File-Atmospheric-Transmissions",
                              model=True,
@@ -514,6 +594,7 @@ def att_foxsi4_atmosphere(mid_energies, time_range=None, file=None):
                          function_path=f"{sys._getframe().f_code.co_name}",
                          mid_energies=mid_energies,
                          times=all_times,
+                         times_utc=native_utc,
                          transmissions=i(X, Y)<<u.dimensionless_unscaled,
                          attenuation_type="Energy-Interpolated-Atmospheric-Transmissions",
                          model=True,
@@ -530,6 +611,7 @@ def att_foxsi4_atmosphere(mid_energies, time_range=None, file=None):
     # energy_inds = np.insert(energy_inds, 1, energy_inds[-1]+1) if energy_inds[-1]<(len(energy_inds)-1) else energy_inds
 
     times = native_times[time_inds]
+    times_utc = native_utc[time_inds]
     transmissions = transmission[:,time_inds]
 
     tave_transmissions = np.mean(transmissions, axis=1)
@@ -538,6 +620,7 @@ def att_foxsi4_atmosphere(mid_energies, time_range=None, file=None):
                      function_path=f"{sys._getframe().f_code.co_name}",
                      mid_energies=mid_energies<<u.keV,
                      times=times,
+                     times_utc=times_utc,
                      transmissions=np.interp(mid_energies.value, 
                                              native_energies.value, 
                                              tave_transmissions.value, 
@@ -552,14 +635,19 @@ def asset_att(save_location=None):
 
     tb_col, obf0_col, obf1_col, cdte_fixed2_col, cdte_fixed4_col = plt.cm.viridis([0, 0.2, 0.4, 0.6, 0.8])
     pix_att_meas_col, pix_att_mod_col, al_mylar_mod_col, cmost0_col, cmost1_col = plt.cm.plasma([0, 0.2, 0.4, 0.6, 0.8])
-    cmost2_col, cmost3_col = plt.cm.cividis([0.1, 0.9])
+    cmost2_col, tb_cola, tb_colb, cmost3_col = plt.cm.cividis([0.1, 0.35, 0.7, 0.9])
 
     # all attenuators
     plt.figure(figsize=(10,8))
-    att_therm_bl = zeroes2nans(att_thermal_blanket(mid_energies).transmissions)
-    plt.ylabel(f"Transmission [{att_therm_bl.unit:latex}]")
+    att_therm_bl_early = zeroes2nans(att_early_thermal_blanket(mid_energies).transmissions)
+    plt.ylabel(f"Transmission [{att_therm_bl_early.unit:latex}]")
     plt.xlabel(f"Energy [{mid_energies.unit:latex}]")
-    p1 = plt.plot(mid_energies, att_therm_bl, color=tb_col, ls="-", label="Thermal blanket")
+    p1 = plt.plot(mid_energies, att_therm_bl_early, color=tb_col, ls="--", lw=4, label="Early Thermal blanket")
+
+    att_therm_bl_model = zeroes2nans(att_thermal_blanket(mid_energies, use_model=True).transmissions)
+    att_therm_bl_measure = zeroes2nans(att_thermal_blanket(mid_energies, use_model=False).transmissions)
+    p1a = plt.plot(mid_energies, att_therm_bl_model, color=tb_cola, ls="-", label="Model Thermal blanket")
+    p1b = plt.plot(mid_energies, att_therm_bl_measure, color=tb_colb, ls="-", label="Meas. Thermal blanket")
 
     old_prefilter0 = zeroes2nans(_att_old_prefilter(mid_energies, position=0).transmissions)
     p2 = plt.plot(mid_energies, old_prefilter0, color=obf0_col, ls="--", label="Old pre-filter 0", lw=3)
@@ -600,7 +688,7 @@ def asset_att(save_location=None):
 
     plt.title("Attenuators")
     
-    plt.legend(handles=p1+p2+p3+p4+p5+p6+p7+p8+p9+p10+p11+p12)
+    plt.legend(handles=p1+p1a+p1b+p2+p3+p4+p5+p6+p7+p8+p9+p10+p11+p12)
     plt.tight_layout()
     if save_location is not None:
         pathlib.Path(save_location).mkdir(parents=True, exist_ok=True)
@@ -649,9 +737,11 @@ def asset_sigmoid(save_location=None):
 def asset_atm(save_location=None):
     fig = plt.figure(figsize=(16,6))
 
-    obs_start = 100
-    obs_mid = 280
-    obs_end = 461
+    time_support()
+
+    obs_start = 100 << u.second
+    obs_mid = 280 << u.second
+    obs_end = 461 << u.second
 
     gs = gridspec.GridSpec(1, 3)
 
@@ -659,28 +749,56 @@ def asset_atm(save_location=None):
 
     energy0, time0 = [1]<<u.keV, np.nan<<u.second
     atm0 = att_foxsi4_atmosphere(mid_energies=energy0, time_range=time0)
-    p0 = gs_ax0.plot(atm0.times, atm0.transmissions, ls=":", label=f"energy:{energy0:latex}\ntime:{time0:latex}", lw=3)
+    p0 = gs_ax0.plot(atm0.times_utc, atm0.transmissions, ls=":", label=f"energy:{energy0:latex}\ntime:{time0:latex}", lw=3)
 
     energy1, time1 = [1, 3, 5, 10, 15]<<u.keV, np.nan<<u.second
     atm1 = att_foxsi4_atmosphere(mid_energies=energy1, time_range=time1)
     p1 = []
     for i in range(len(energy1)):
-        p1 += gs_ax0.plot(atm1.times, atm1.transmissions[:,i], ls="-", label=f"energy:{energy1[i]:latex}")
+        p1 += gs_ax0.plot(atm1.times_utc, atm1.transmissions[:,i], ls="-", label=f"energy:{energy1[i]:latex}")
+
+    diff_from_first_to_start = obs_start - atm1.times[0]
+    obs_start_utc = atm1.times_utc[0] + diff_from_first_to_start
+    diff_from_first_to_mid = obs_mid - atm1.times[0]
+    obs_mid_utc = atm1.times_utc[0] + diff_from_first_to_mid
+    diff_from_first_to_end = obs_end - atm1.times[0]
+    obs_end_utc = atm1.times_utc[0] + diff_from_first_to_end
 
     gs_ax0.set_ylabel(f"Transmission [{atm0.transmissions.unit:latex}]")
-    gs_ax0.set_xlabel(f"Time (Obs. start=100 s) [{atm0.times.unit:latex}]")
+    gs_ax0.set_xlabel(f"UTC Time (Obs. start~{obs_start_utc})")
     gs_ax0.set_ylim([0,1.05])
-    v0 = gs_ax0.axvline(obs_start, ls="-.", c="k", label="obs. start")
-    v1 = gs_ax0.axvline(obs_mid, ls="-.", c="k", label="obs. middle")
-    v2 = gs_ax0.axvline(obs_end, ls="-.", c="k", label="obs. end")
-    gs_ax0.set_xlim([0, 600])
+    gs_ax0.set_xlim([atm1.times_utc[0], atm1.times_utc[-1]])
     gs_ax0.set_title("Sampled energy band transmission vs. time")
+
+    date_format = DateFormatter("%H:%M:%S")
+    gs_ax0.xaxis.set_major_formatter(date_format)
+
+    gs_ax0b = gs_ax0.twiny()
+    gs_ax0b_color = "grey"
+    _ = gs_ax0b.plot(atm0.times, atm0.transmissions, lw=0)
+    v0 = gs_ax0b.axvline(obs_start.value, ls="-.", c="k", label="obs. start")
+    v1 = gs_ax0b.axvline(obs_mid.value, ls="-.", c="k", label="obs. middle")
+    v2 = gs_ax0b.axvline(obs_end.value, ls="-.", c="k", label="obs. end")
+    gs_ax0b.set_xlabel(f"Time (Obs. start~100 s) [{atm0.times.unit:latex}]", color=gs_ax0b_color)
+    gs_ax0b.set_xlim([atm1.times[0].value, atm1.times[-1].value])
+    gs_ax0b.tick_params(axis="x", labelcolor=gs_ax0b_color, color=gs_ax0b_color, which="both")
+
+    _y_time_loc = 0.3
+    _x_offset = 4 << u.second
+    _y_offset = 0.3
+    gs_ax0.annotate(f"{obs_start_utc.strftime('%H:%M:%S')} UTC", (obs_start_utc+_x_offset, _y_time_loc), rotation=90)
+    gs_ax0.annotate(f"{obs_mid_utc.strftime('%H:%M:%S')} UTC", (obs_mid_utc+_x_offset, _y_time_loc+_y_offset), rotation=90)
+    gs_ax0.annotate(f"{obs_end_utc.strftime('%H:%M:%S')} UTC", (obs_end_utc, _y_time_loc), rotation=90, ha="right")
+    _y_sec_loc = _y_time_loc+0.25
+    gs_ax0b.annotate(f"{obs_start:.0f}", (obs_start.value+_x_offset.value, _y_sec_loc), color=gs_ax0b_color, rotation=90)
+    gs_ax0b.annotate(f"{obs_mid:.0f}", (obs_mid.value+_x_offset.value, _y_sec_loc+_y_offset), color=gs_ax0b_color, rotation=90)
+    gs_ax0b.annotate(f"{obs_end:.0f}", (obs_end.value, _y_sec_loc), color=gs_ax0b_color, rotation=90, ha="right")
+
     plt.legend(handles=p0+p1+[v0,v1,v2])
-    
 
     gs_ax1 = fig.add_subplot(gs[0, 1])
 
-    energy2, time2 = np.nan<<u.keV, [obs_start, obs_end]<<u.second
+    energy2, time2 = np.nan<<u.keV, [obs_start.value, obs_end.value]<<u.second
     atm2 = att_foxsi4_atmosphere(mid_energies=energy2, time_range=time2)
     p2 = gs_ax1.plot(atm2.mid_energies, atm2.transmissions, ls="-", label=f"time range:{time2:latex}")
 
@@ -692,13 +810,20 @@ def asset_atm(save_location=None):
     p4 = gs_ax1.plot(atm3.mid_energies, atm3.transmissions[:, 5600], ls="-", label=f"time:{atm3.times[5600]:latex}")
     p5 = gs_ax1.plot(atm3.mid_energies, atm3.transmissions[:, 9200], ls="-", label=f"time:{atm3.times[9200]:latex}")
 
+    cmos_le = 0.8<<u.keV
+    v3 = gs_ax1.axvline(cmos_le.value, ls="-.", c="k", label="CMOS energies")
+    gs_ax1.arrow(cmos_le.value, 0.85, 1, 0, length_includes_head=True, head_width=0.02, head_length=0.2, color="k")
+    cdte_le = 4<<u.keV
+    v4 = gs_ax1.axvline(cdte_le.value, ls=":", c="k", label="CdTe energies")
+    gs_ax1.arrow(cdte_le.value, 0.85, 5, 0, length_includes_head=True, head_width=0.02, head_length=1, color="k")
+
     gs_ax1.set_ylabel(f"Transmission [{atm3.transmissions.unit:latex}]")
     gs_ax1.set_xlabel(f"Energy [{atm3.mid_energies.unit:latex}]")
     gs_ax1.set_ylim([0,1.05])
     gs_ax1.set_xlim([0.01, 30])
     gs_ax1.set_xscale("log")
     gs_ax1.set_title("Time averaged and time sampled transmission vs. energy")
-    plt.legend(handles=p2+p3+p4+p5)
+    plt.legend(handles=p2+p3+p4+p5+[v3,v4])
 
     
     gs_ax2 = fig.add_subplot(gs[0, 2])
@@ -712,6 +837,13 @@ def asset_atm(save_location=None):
     atm5 = att_foxsi4_atmosphere(mid_energies=energy5, time_range=time5)
     colour5 = "orange"
     gs_ax2.plot(atm5.mid_energies, atm5.transmissions, label=f"time range:{atm5.times[0]:.2f}$-${atm5.times[-1]:.2f}\nCdTe range+response resolution", marker="x", ms=2, c=colour5)
+
+    # add the lowest energies of the detectors
+    v5 = gs_ax2.axvline(cmos_le.value, ls="-.", c="k", label="CMOS energies")
+    gs_ax2.arrow(cmos_le.value, 0.85, 1, 0, length_includes_head=True, head_width=0.02, head_length=0.2, color="k")
+    v6 = gs_ax2.axvline(cdte_le.value, ls=":", c="k", label="CdTe energies")
+    gs_ax2.arrow(cdte_le.value, 0.85, 5, 0, length_includes_head=True, head_width=0.02, head_length=1, color="k")
+
     # inset Axes for the CdTe plot
     x1, x2, y1, y2 = 2.5, 30, 0.95, 1.04  # subregion of the original image
     axins = gs_ax2.inset_axes([0.4, 0.35, 0.5, 0.4],
@@ -725,13 +857,19 @@ def asset_atm(save_location=None):
     _connectors[0].__dict__["_visible"] = False 
     _connectors[1].__dict__["_visible"] = True
 
+    # add the lowest energies of the detectors
+    _ = axins.axvline(cmos_le.value, ls="-.", c="k", label="CMOS energies")
+    axins.arrow(cmos_le.value, 0.96, 1, 0, length_includes_head=True, head_width=0.01, head_length=0.2, color="k")
+    _ = axins.axvline(cdte_le.value, ls=":", c="k", label="CdTe energies")
+    axins.arrow(cdte_le.value, 0.96, 5, 0, length_includes_head=True, head_width=0.01, head_length=1, color="k")
+
     gs_ax2.set_ylabel(f"Transmission [{atm3.transmissions.unit:latex}]")
     gs_ax2.set_xlabel(f"Energy [{atm3.mid_energies.unit:latex}]")
     gs_ax2.set_ylim([0,1.05])
     gs_ax2.set_xlim([0.01, 30])
     gs_ax2.set_xscale("log")
     gs_ax2.set_title("Time averaged transmission vs. sampled energy")
-    plt.legend(handles=p6+p7)
+    plt.legend(handles=p6+p7+[v5,v6])
 
     plt.suptitle("FOXSI-4 Flight Atmospheric Transmission")
 
@@ -742,7 +880,7 @@ def asset_atm(save_location=None):
     plt.show()
 
 if __name__=="__main__":
-    save_location = None # ASSETS_PATH
+    save_location = None # ASSETS_PATH # 
     
     asset_att(save_location=save_location)
     
diff --git a/response_tools/response-information/info.yaml b/response_tools/response-information/info.yaml
index 2329a37..8575a2c 100644
--- a/response_tools/response-information/info.yaml
+++ b/response_tools/response-information/info.yaml
@@ -23,7 +23,9 @@ files:
     cdte_det_pt_resp: "detector-response-data/cdte/pt_v2/"
 
   attenuation:
-    att_thermal_blanket: "attenuation-data/F4_Blanket_transmission_v1.dat"
+    att_early_thermal_blanket: "attenuation-data/F4_Blanket_transmission_v1.dat"
+    att_modeled_thermal_blanket: "attenuation-data/FOXSI4_theoretical_thermal_blanket_transmission_v1.fits"
+    att_measured_thermal_blanket: "attenuation-data/FOXSI4_measured_thermal_blanket_transmission_v1.fits"
     att_pixelated: "attenuation-data/20240607_fosxi4_transmission_v1.csv"
     att_al_mylar: "attenuation-data/thin_mylar_p3_p5_theoretical_v1.csv"
     att_telescope-2_uniform_al_cdte: "attenuation-data/unif_att_p2_theoretical_v1.csv"
@@ -34,7 +36,7 @@ files:
     att_telescope-1_cmos_obfilter: "attenuation-data/foxsi4_telescope-1_BASIC_optical_blocking_filter_transmittance_v1.fits"
     att_telescope-0_cmos_prefilter: "attenuation-data/foxsi4_telescope-0_BASIC_attenuation_filter_transmittance_v1.fits"
     att_telescope-1_cmos_prefilter: "attenuation-data/foxsi4_telescope-1_BASIC_attenuation_filter_transmittance_v1.fits"
-    att_foxsi4_atmosphere: "attenuation-data/FOXSI4_atmospheric_transmission_v1.fits"
+    att_foxsi4_atmosphere: "attenuation-data/FOXSI4_atmospheric_transmission_v2.fits"
     att_early_cmos_prefilter: "attenuation-data/CMOST_Prefilter_transmission_v1.dat"
 
   quantum_efficiency:
diff --git a/response_tools/responses.py b/response_tools/responses.py
index 5873073..275c326 100644
--- a/response_tools/responses.py
+++ b/response_tools/responses.py
@@ -177,7 +177,15 @@ def foxsi4_telescope0_flight_arf(mid_energies, off_axis_angle, time_range):
         `[numpy.nan, numpy.nan]<<astropy.units.second` then the full 
         time will be considered and the output will not be averaged but 
         a grid of the transmissions at all times and at any provided
-        energies.
+        energies. Should be of length 2.
+        Can be given as seconds since launch:
+        - Observation start: 100<<astropy.units.second
+        - Observation end: 461<<astropy.units.second
+        Can be given as UTC time as well either as a string or and 
+        Astropy time.
+        - String format: YYYY-mm-ddTHH:MM:SS
+        - Observation start: 2024-04-17T22:14:40
+        - Observation end: 2024-04-17T22:20:41
 
     Returns
     -------
@@ -307,7 +315,15 @@ def foxsi4_telescope1_flight_arf(mid_energies, off_axis_angle, time_range):
         `[numpy.nan, numpy.nan]<<astropy.units.second` then the full 
         time will be considered and the output will not be averaged but 
         a grid of the transmissions at all times and at any provided
-        energies.
+        energies. Should be of length 2.
+        Can be given as seconds since launch:
+        - Observation start: 100<<astropy.units.second
+        - Observation end: 461<<astropy.units.second
+        Can be given as UTC time as well either as a string or and 
+        Astropy time.
+        - String format: YYYY-mm-ddTHH:MM:SS
+        - Observation start: 2024-04-17T22:14:40
+        - Observation end: 2024-04-17T22:20:41
 
     Returns
     -------
@@ -434,7 +450,15 @@ def foxsi4_telescope2_flight_arf(mid_energies, off_axis_angle, time_range):
         `[numpy.nan, numpy.nan]<<astropy.units.second` then the full 
         time will be considered and the output will not be averaged but 
         a grid of the transmissions at all times and at any provided
-        energies.
+        energies. Should be of length 2.
+        Can be given as seconds since launch:
+        - Observation start: 100<<astropy.units.second
+        - Observation end: 461<<astropy.units.second
+        Can be given as UTC time as well either as a string or and 
+        Astropy time.
+        - String format: YYYY-mm-ddTHH:MM:SS
+        - Observation start: 2024-04-17T22:14:40
+        - Observation end: 2024-04-17T22:20:41
 
     Returns
     -------
@@ -604,7 +628,15 @@ def foxsi4_telescope3_flight_arf(mid_energies, time_range, off_axis_angle=None):
         `[numpy.nan, numpy.nan]<<astropy.units.second` then the full 
         time will be considered and the output will not be averaged but 
         a grid of the transmissions at all times and at any provided
-        energies.
+        energies. Should be of length 2.
+        Can be given as seconds since launch:
+        - Observation start: 100<<astropy.units.second
+        - Observation end: 461<<astropy.units.second
+        Can be given as UTC time as well either as a string or and 
+        Astropy time.
+        - String format: YYYY-mm-ddTHH:MM:SS
+        - Observation start: 2024-04-17T22:14:40
+        - Observation end: 2024-04-17T22:20:41
 
     Returns
     -------
@@ -773,7 +805,15 @@ def foxsi4_telescope4_flight_arf(mid_energies, time_range, off_axis_angle=None):
         `[numpy.nan, numpy.nan]<<astropy.units.second` then the full 
         time will be considered and the output will not be averaged but 
         a grid of the transmissions at all times and at any provided
-        energies.
+        energies. Should be of length 2.
+        Can be given as seconds since launch:
+        - Observation start: 100<<astropy.units.second
+        - Observation end: 461<<astropy.units.second
+        Can be given as UTC time as well either as a string or and 
+        Astropy time.
+        - String format: YYYY-mm-ddTHH:MM:SS
+        - Observation start: 2024-04-17T22:14:40
+        - Observation end: 2024-04-17T22:20:41
 
     Returns
     -------
@@ -942,7 +982,15 @@ def foxsi4_telescope5_flight_arf(mid_energies, off_axis_angle, time_range):
         `[numpy.nan, numpy.nan]<<astropy.units.second` then the full 
         time will be considered and the output will not be averaged but 
         a grid of the transmissions at all times and at any provided
-        energies.
+        energies. Should be of length 2.
+        Can be given as seconds since launch:
+        - Observation start: 100<<astropy.units.second
+        - Observation end: 461<<astropy.units.second
+        Can be given as UTC time as well either as a string or and 
+        Astropy time.
+        - String format: YYYY-mm-ddTHH:MM:SS
+        - Observation start: 2024-04-17T22:14:40
+        - Observation end: 2024-04-17T22:20:41
 
     Returns
     -------
@@ -1106,7 +1154,15 @@ def foxsi4_telescope6_flight_arf(mid_energies, off_axis_angle, time_range):
         `[numpy.nan, numpy.nan]<<astropy.units.second` then the full 
         time will be considered and the output will not be averaged but 
         a grid of the transmissions at all times and at any provided
-        energies.
+        energies. Should be of length 2.
+        Can be given as seconds since launch:
+        - Observation start: 100<<astropy.units.second
+        - Observation end: 461<<astropy.units.second
+        Can be given as UTC time as well either as a string or and 
+        Astropy time.
+        - String format: YYYY-mm-ddTHH:MM:SS
+        - Observation start: 2024-04-17T22:14:40
+        - Observation end: 2024-04-17T22:20:41
 
     Returns
     -------
diff --git a/response_tools/telescope_parts.py b/response_tools/telescope_parts.py
index 9382d69..caba158 100644
--- a/response_tools/telescope_parts.py
+++ b/response_tools/telescope_parts.py
@@ -283,7 +283,7 @@ def foxsi4_position2_thermal_blanket(mid_energies):
         the thermal blanket. See accessible information using 
         `.contents` on the output.
     """
-    r = att_thermal_blanket(mid_energies)
+    r = att_thermal_blanket(mid_energies, use_model=True)
     r.update_function_path(sys._getframe().f_code.co_name)
     return r
 
@@ -415,7 +415,7 @@ def foxsi4_position3_thermal_blanket(mid_energies):
         the thermal blanket. See accessible information using 
         `.contents` on the output.
     """
-    r = att_thermal_blanket(mid_energies)
+    r = att_thermal_blanket(mid_energies, use_model=True)
     r.update_function_path(sys._getframe().f_code.co_name)
     return r
 
@@ -574,7 +574,7 @@ def foxsi4_position4_thermal_blanket(mid_energies):
         the thermal blanket. See accessible information using 
         `.contents` on the output.
     """
-    r = att_thermal_blanket(mid_energies)
+    r = att_thermal_blanket(mid_energies, use_model=True)
     r.update_function_path(sys._getframe().f_code.co_name)
     return r
 
@@ -709,7 +709,7 @@ def foxsi4_position5_thermal_blanket(mid_energies):
         the thermal blanket. See accessible information using 
         `.contents` on the output.
     """
-    r = att_thermal_blanket(mid_energies)
+    r = att_thermal_blanket(mid_energies, use_model=True)
     r.update_function_path(sys._getframe().f_code.co_name)
     return r
 
@@ -864,7 +864,7 @@ def foxsi4_position6_thermal_blanket(mid_energies):
         the thermal blanket. See accessible information using 
         `.contents` on the output.
     """
-    r = att_thermal_blanket(mid_energies)
+    r = att_thermal_blanket(mid_energies, use_model=True)
     r.update_function_path(sys._getframe().f_code.co_name)
     return r
 
diff --git a/setup.py b/setup.py
index bf32b06..005d107 100644
--- a/setup.py
+++ b/setup.py
@@ -2,7 +2,7 @@
 
 setuptools.setup(
     name="response_tools",
-    version="1.0.2",
+    version="1.0.3",
     description="Software used for the FOXSI response files.",
     url="https://github.com/foxsi/response-tools",
     install_requires=[