How To Update PueoSim For GENETIS:

First, whoever updates pueoSim needs access to pueoBuilder, pueoSim, and niceMC on GitHub (ask Will for permissions).

Once you do, go into the peuoSim directory at /fs/ess/PAS1960/buildingPueoSim/

and source set_env.sh

`source set_env.sh`

Then, we want to make copies of the files we are currently modifying for the GENETIS loop.

For PueoSim v1.1.0 these are:

1. nicemc/src/EventGenerator.cc  # Modified to create custom output root files to calculate errors

2. pueosim/test/simulatePueo.cc # Modified to stop the simulation of the LF antenna which is not needed for GENETIS

3. pueosim/src/Seavey.cc # Modified to read in custom gain files

4. pueosim/src/pueo.cc # Modified to read in custom gain files

5. pueosim/include/Seavey.hh # Modifies to read in custom gain files

Currently, I store copies of these in the `/fs/ess/PAS1960/buildingPueoSim/backups` directory in case somebody accidentally overwrites the files in pueoBuilder. 

Once you’ve made the copies, you can run `./pueoBuilder.sh` from the `/fs/ess/PAS1960/buildingPueoSim/pueoBuiler` directory. This will rebuild pueoSim and niceMC, pulling the latest updates from GitHub. 

You may need to delete the pueoSim and niceMC directories in order for the builder to pull the latest version from GitHub. Or, if it’s being really stubborn, move the whole pueoBuilder directory to a temporary location and run the builder from scratch with 

`git clone git@github.com:PUEOCollaboration/pueoBuilder` and then `./pueoBuilder.sh`

Then, you will need to reference the copies of the changed files to make changes to the new version of pueoSim. Hope this doesn’t cause too much of a headache, and when you’re done, return to the /fs/ess/PAS1960/buildingPueoSim/pueoBuiler directory. 

Then you simply type

`make install`

then

`make`

And now, pueoSim should be ready to run!

EventGeneratior.cc Changes and Rationale:

PueoSim’s default output ROOT files are very large and therefore time-consuming to parse through to get the information we need to calculate effective volume and errors. So, we want to create a custom ROOT file with only the variables we want, greatly increasing the speed of the analysis.

To do this, we want to create a new TFile with corresponding TTree and TBranches that will store the loop.positionWeight, loop.directionWeight, and neutrino.path.weight. Then, we want to fill the Tree when a detector is triggered and write the results to the file at the end of the loop.

Sample code for initializing the TFile:

simulatePueo.cc Changes and Rationale:

By default, pueoSim v1.1.0 runs a simulation for the normal antenna and a low-frequency (LF) antenna. As GENETIS is evolving for the main antenna, we are not interested in using computation time to simulate the LF antenna. So, we comment out the lines that initialize the LF detector in this file.

Seavey.cc, Seavey.hh, and pueo.cc Changes and Rationale:

As of pueoSim v1.1.0, the simulation software only has the ability to read in one antenna. This is a problem, as we want it to be able to read in files for any antenna we make. So, we need to change these scripts to be able to read in whatever gain files we want.

The convention the loop uses is to input gain files in the format of 

vv_0_Toyon{runNum}

hh_0_Toyon{runNum}

…

In the ./pueoBuilder/components/pueosim/data/antennas/simulated directory

So, the main change is getting the runNum variable into Seavey.cc file where the gains are read. Currently, we define a global variable at the stary of pueo.cc and pass it into where Seavey is called. This means we have to add runNum as a parameter in Seavey.cc as well as Seavey.hh. Finally, we change the name of the read-in files to be in the above format AFTER dividing runNum by 1000 (this is because pueoSim uses the run number as a random number generating seed, so we divide runNum by 1000 to be able to read in the same gain patterns for multiple seeds of the same individual).

Note: We used to change pueoSim to output a veff.csv file. We now handle this calculation by analyzing ROOT files, so this change is no longer necessary. 

This is a multigenerational tracing of our second-best individual's parents and children:

The second-best individual in this evolution was Bicone 22 from generation 40. This individual is, in fact, a fascinating case as we shall see. But to start the story of this individual we will go back to generation 38 in order to demonstrate some of the peculiarities. 

In generation 38 there were two bicones of no renown,  Bicone 11 and Bicone 17. Bicone 11 was a fairly average individual that was ranked 23rd with a fitness score of 4.72016. Its DNA was {*6.16084, ***79.663, 0.0015434, -0.107765} for side one , and {0.308809, 39.6742, -0.0084247, 0.40629} for side two. One day, by chance it met Bicone 17, another average bicone ranked 24th with a score of 4.71877 and DNA {**2.32499, 79.663, -0.00224948, 0.192602} for side one, and {0.308809, 39.6742, -0.0084247, 0.40629} for side two. These two individuals eventually became the parents of two antennas: Bicone 16 and Bicone 17 of generation 39.

Bicone 16 eventually grew to have been ranked 3rd in fitness score of 4.95323. It’s DNA ended up being a complete balance of its parents sharing side one with Bicone 38.11{2.32499, 79.663, -0.00224948, 0.192602} and side two with bicone 38.17 {0.308809, 39.6742, -0.0084247, 0.40629}. Bicone 16 was an individual with high aspirations and hoped to be reproduced. But alas, it was not meant to be. But bicone 16 came upon some amazing luck, it was selected with itself for crossover. This meant that bicone 16 was able to provide two identical surrogates to survive into the next generation. This is where this Bicone fulfilled its full potential. 

The twin bicones were named Bicone 22 and Bicone 23 in generation 40. Being clones, they shared all their DNA with Bicone 39.16. However, due to some circumstances, they had slightly different fitness scores. Bicone 23 managed a very respectable 5.0117 fitness score and was ranked 2nd in the generation. But not to be outdone Bicone 22 managed to score a 5.17014 and ended up being the second-best performing individual of all time. Being so successful, the two bicones ended up producing 8 children between the two groups.

Bicone 23 was the first to crossover and had 2 children with its partner. These were Bicones 4 and 5. Bicone 4 was ranked 39th with a fitness score of 4.60251 and still shared the DNA of its second side with its grandparent Bicone 38.17 as well as most of its first side with Bicone 38.11 {2.32499, 79.663, -0.00213879, 0.192602} {0.308809, 39.9608, -0.0084247, 0.40629}. Bicone 5 on the other hand, was ranked 14th with a fitness score of 4.80535 and it still shared a lot of DNA with its grandparents {6.16084,53.0851,-0.00224948,0.0534469} {0.966617,39.6742,-0.0084247,0.40629}.

Bicone 22 had 6 children of its own with various other Bicones. These were; Bicone 8, ranked 11th with a fitness Score of 4.81784 and DNA {2.32499, 75.9855, -0.00224948, 0.192602} {0.966617, 39.6742, -0.00320023, 0.213833}; Bicone 9, ranked 7th with a fitness score of 4.88966 and DNA {6.10508, 79.663, -0.000594616, 0.0351901} {0.308809, 42.4246, -0.0084247, 0.40629}; Bicone 12, ranked 12th with a fitness score of 4.81705 and DNA {6.42695, 75.9855, 0.0015434, -0.107765} {0.308809, 39.6742, -0.00320023, 0.213833}; Bicone 13, ranked 2nd with a fitness score of 5.0344 and DNA {2.32499, 79.663, -0.00224948, 0.192602} {0.966617, 42.4246, -0.0084247, 0.40629}; Bicone 34 ranked 3rd with a fitness score of 4.99864 and DNA {0.66148, 73.5522, -0.000594616, 0.00582814} {0.966617, 39.6742, -0.0084247, 0.40629}; and finally Bicone 35, ranked 22nd with fitness score 4.74955 and DNA {2.32499, 79.663, -0.00224948, 0.192602} {0.308809, 42.4246, -0.0084247, 0.40629}

Bellow, I have attached the rainbow plot with the parameters occupied by individual 4 in gen 41 which was again ranked 39th in that generation. From this, we can see that while in its own generation it was a poor performer, overall it was upper middle of the pack. However, because of the density of other better performing antennas in this region, it is hard to distinguish which genes in this antenna are contributing the most to the drop in fitness score compared to its siblings and parents. 


 

*Gene originating from Bicone 38.11

**Gene originating from Bicone 38.17

***Gene originating from Bicone 38.11 and 38.17 that is shared between the two.

Today's Summer 2020 daily update:

As a note, today OSC was down so productivity was more limited