SKYSURF VIII -- Modeling SKYSURF Completeness Data for Comparison to the Hubble Space Telescope Exposure Time Calculator

Introduction to the show: ident: Astrophysics. The day in astrophysics: observations, models and what the data forces.

Vera: We're looking at the paper: "SKYSURF VIII -- Modeling SKYSURF Completeness Data for Comparison to the Hubble Space Telescope Exposure Time Calculator".

Jocelyn: Here's the gist of the paper. Accurately assessing image source completeness is critical for interpreting measurements on telescope data <ref:2508.08484#pg2> Using the Wide Field Camera 3 (WFC3) and Advanced Camera for Surveys (ACS) data from the Hubble Space Telescope (HST) archival project “SKYSURF”, we model galaxy completeness as a function of the exposure time and background of an image <ref:2508.08484#pg2> Ultimately, we find that, for extended galaxies, the ETC predicts a 1-2 magnitudes fainter completeness limit than our data <ref:2508.08484#pg7>.

Vera: Modeling Completeness Simulation The authors model galaxy completeness by adding simulated objects of varying magnitudes and sizes into the images to determine the detection rate for each set of parameters <ref:2508.08484#pg2> In order to simulate data analogous to the ETC data, they use the GalSim Python package from Rowe et al.

Jocelyn: (2015) <ref:2508.08484#pg2> The inserted objects have a Sersic index of 1 and an inclination of 0° <ref:2508.08484#pg7>.

Vera: This process is done 200 pixels from the image edges to avoid drizzling artifacts, and repeated 100 times for each image, to decrease the effect of potential overlap with image sources <ref:2508.08484#pg7>.

Title and authors: Vera: So we're looking at "SKYSURF VIII -- Modeling SKYSURF Completeness Data for Comparison to the Hubble Space Telescope Exposure Time Calculator." The title itself is pretty direct about what they're doing here, trying to model completeness based on exposure time and background.

Jocelyn: Yeah, it’s about taking those real-world images from SKYSURF and seeing how their completeness measurements compare against the existing Exposure Time Calculator. It’s essentially testing a prediction against actual data from the Hubble Space Telescope project.

Subrahmanyan: So, what's the main goal here? Are they trying to find a flaw in the ETC, or are they just building a new way to calculate these detection limits?

Vera: They are specifically testing if their model can explain why the ETC gives fainter completeness limits than what they actually observe for extended galaxies. They want to see if their method captures that difference.

Jocelyn: To do this, the authors model galaxy completeness by adding simulated objects of different magnitudes and sizes into these actual images to find out how often each set of parameters gets detected.

Subrahmanyan: So, the methodology involves creating a synthetic population within the real data set for testing purposes, which is a standard approach in observational astronomy.

Vera: They use the GalSim Python package from Rowe et al. two thousand fifteen to simulate this data, inserting objects with a Sersic index of one and an inclination of zero degrees into those images <ref:2508.08484#pg7>.

Jocelyn: And they have to be careful about where they insert these objects; they do it two hundred pixels from the image edges to avoid drizzling artifacts, which is important for consistency <ref:2508.08484#pg7>.

Subrahmanyan: That careful placement is necessary because if you insert them too close to the edge, you risk artifacts from the image processing steps like drizzling.

Vera: They repeat this insertion process one hundred times for each image, and they do it to decrease the effect of potential overlap with sources already in the images <ref:2508.08484#pg7>.

Jocelyn: That repetition is a way to try and minimize double-counting or confusion when checking if an object is detected by Source Extractor <ref:2508.08484#pg7>.

Subrahmanyan: It sounds like they are building a robust simulation pipeline, trying to get as close to reality as possible given the constraints of their archival data.

The paper's summary: Jocelyn: So putting all that simulation work together, the summary says they model galaxy completeness as a function of exposure time and background for images using WFC3 and ACS data from SKYSURF <ref:2508.08484#pg2>.

Vera: The key result is that they find the fifty percent completeness results can be modeled for different object sizes and magnitudes in different filters <ref:2508.08484#pg3>.

Jocelyn: They then model the fifty percent completeness limit specifically in terms of object size, exposure time, and background conditions for each filter <ref:2508.08484#pg3>.

Subrahmanyan: It's like they are creating a comprehensive predictive function that takes those three variables as inputs to tell you what the detection probability is.

Vera: And they fit an exponential function y equals v plus w log10(x) minus xzero minus one one plus e minus z (log10(x) minus xzero). The parameters v, w, xzero and z are averaged from all images for a given filter <ref:2508.08484#pg3>.

Jocelyn: This function gives them the fit lines for the fifty percent ninety percent and ninety five percent completeness values shown in Figure three <ref:2508.08484#pg3>.

Subrahmanyan: So they use that fitted curve to show how completeness percentage decreases when the magnitude gets fainter, which you can see in Figure two <ref:2508.08484#pg3>.

Vera: And they specifically look at the effect of confusion limits for brighter inserted objects on these plots, where the number of matches decreases slightly due to occasional overlap <ref:2508.08484#pg3>.

Jocelyn: The main takeaway is that for larger sizes in these figures, they see this effect of confusion limit where brighter inserted objects cause a slight decrease in matches due to occasional overlap <ref:2508.08484#pg3>.

Subrahmanyan: That’s a direct result of the confusion limit kicking in when you have bigger sources, which connects the simulation back to real detection limitations <ref:2508.08484#pg7>.

Vera: The paper essentially shows how image depth and sky conditions interact to determine whether an object gets detected or not <ref:2508.08484#pg2>.

The paper's improvements: Jocelyn: Now let's look at what the authors suggest is improved in this work compared to previous versions of the Exposure Time Calculator.

Vera: One big suggestion is that instead of relying on purely flat surface brightness profiles like the ETC does, they should use a model that accounts for realistic source profiles <ref:2508.08484#pg3>.

Jocelyn: They want an AI system to generate more accurate completeness predictions by incorporating galaxy structure instead of using idealized assumptions about the sources <ref:2508.08484#pg3>.

Subrahmanyan: That makes sense because real galaxies aren't just simple flat surfaces; they have varying structures, and a model that acknowledges that would be much better for cosmological modeling.

Vera: They also suggest training the AI to recognize the impact of image background by isolating it through equation three <ref:2508.08484#pg3>.

Jocelyn: This allows the system to adjust its predictions based on specific filter backgrounds, using equation three which shows "the impact of the image background on the completeness is isolated" <ref:2508.08484#pg3>.

Subrahmanyan: So they are explicitly incorporating sky conditions into their model, not just treating them as a variable to be plugged in later.

Vera: And finally, they propose integrating exposure time and background models into a single predictive framework using equation four <ref:2508.08484#pg4>.

Jocelyn: This creates their own completeness model where exposure time and background are combined into one prediction for the fifty percent completeness magnitude p based on both image depth and sky conditions simultaneously <ref:2508.08484#pg3>.

Subrahmanyan: That's a much more complete framework for predicting detection limits, moving from separate models to a unified one that incorporates both depth and sky conditions <ref:2508.08484#pg3>.

Conclusion: Vera: So, wrapping up the paper "SKYSURF VIII -- Modeling SKYSURF Completeness Data for Comparison to the Hubble Space Telescope Exposure Time Calculator." They found that the ETC predicts fainter completeness magnitudes for all objects, especially the larger ones <ref:2508.08484#pg7>.

Jocelyn: The slope of completeness magnitude as a function of exposure time remains largely identical across their different analyses <ref:2508.08484#pg7>. This leads them to believe that the ETC’s flat surface brightness approximation of the sources is likely causing this discrepancy.

Subrahmanyan: So if you look at that vertical y-intercept shift, it's a major indicator that their model needs to account for how extended these sources actually are when we talk about those completeness limits <ref:2508.08484#pg3>.

Vera: That means the ETC’s flat surface brightness assumption is what makes the difference between their simulation and reality <ref:2508.08484#pg3>.

Jocelyn: Overall, they think this work provides a solid empirical framework for how to handle these kinds of completeness calculations in future observations <ref:2508.08484#pg2>.

Subrahmanyan: They plan on using these methods on the James Webb Space Telescope to model its exposure time calculator and support other future works using the telescope data <ref:2508.08484#pg7>.