So I was asked, yeah, the others to give a short presentation of an actual application

in real life to what can be done with system in a specific case of simulating agent and

fields and even more specifically in the case of Athena W5 survey. Basically because it's

something I've been working, I've spent some time in the last few years and actually I

started looking at this and working with SIGSTAY in a previous version of this workshop. I

think it was taken 2016 or something like that. So now these are the results after a

few years. Okay, so very brief introduction to the Athena W5 science and survey. I guess

you all know more or less what Athena is. W5 is just one of the two instruments. It's a

wide field imager. So it will spend most of its standard lifetime of the mission performing

an extra galactic survey that has several scientific goals. One of the main pillars of this survey

will be studying the air-rushed universe. So air-rushed AGN. This scientific goal is translated

into two very specific aims that is detecting at least 10 AGN at rush from 6 to 7 at this

very low luminosity. Can you see my pointer? Yeah. Okay. And 10 AGN at slightly higher

rush of 7 to 8 at slightly higher luminosities. This may sound strange as a definition of

a science goal, but it's just a useful way of synthesizing what you want to do with the

survey and translate that into two numbers, basically flux limit and an area that you

want to cover. And you can see in this figure, basically the flux area coverage of several

existing survey with existing telescope plus IdoSITA and what is expected from the Athena

and the W5 extra galactic survey. These two aims translate into the two layers of the

survey. One is the deep layer. It's broad enough for big fields of one megasecond or

more exposure time. And the shallow layer that is more or less 100 pointings with a

bit less than 100 kiloseconds of exposure time. The deep layer for the phase mostly

driven by the faintest sources and the shallow layer by the highest ratio of sources. So

all these numbers and these curves were computed back in 2014, I think, when the science case

was defined based on analytic estimates. So you take all your information from about the

PSF shape and dependencies, field of view, background, and so on and so forth. And you

can compute analytically what will be the sensitivity of your survey and then how these

will reach the log n log s of sources that are given in the brush, as you can see in

this course. So a few years ago, we started thinking on backing up and confirming these

analytic estimates with end-to-end simulations. And also there was the idea of having a tool,

another independent tool to study the impact of different telescope characteristics. As

you may imagine, these have changed during the study phase of the mission. And so naturally,

we ended up looking into six, because as you have seen these days, it's the perfect tool

to perform these kinds of studies. So what you need to perform a deep field in the case

of the NWFI or in any case of any telescope that you can imagine is building your input

MOCs. So we have HGN MOCs, two flavors to account for the uncertainties of the I-Rashid

universe. We don't know what's the limiter function of HGN at Rashid greater than four,

five, or six unit space. And so you can see here the two versions, how they compare in

terms of log n log s above Rashid 4 in this case. With the data points, of course they

match the few data points that we have and then they differ very a lot at low fluxes.

So we're taking into account the uncertainties on our extrapolation to the I-Rashid of what

we know now. Both these MOCs are computed in a wide range of Rashid luminosities and

down to very low fluxes. So basically you simulate also the sources that you will not

directly detect and this makes up the cosmic pixel background basically. So you don't have

to worry about what's the fraction of cosmic pixel background that you result because you

will actually resolve it in the images that you simulate. Then we have Galaxy MOCs. When

you go to very low fluxes, this increases the confusion and the number of point sources

in your image. At the beginning was a very simplified MOC with just sources drawn from

log n log s. Now we have developed a more refined Galaxy MOC that is based on spritz,

which is a model, very complex model that is based on optical near-infrared data and