WARP – Week 2

Captain’s log, stardate: -303894.

The Theoreticians

Doubt began to cloud Commander Troi’s finely-tuned mind. As a Betazoid, she’d always been strongly aware of such feelings. She felt that, while the data reduction performed by the Theoreticians had been executed as planned, it could further be improved.

We firstly decided to move the cut in our data slightly to the left of its previous position to include more galaxies, meaning there is now less contamination of galaxies in the stars section which is the portion that is important for later use. The new cut along with its effects are shown on the contamination graph below.


The new contamination plot with a cut that further reduces the amount of galaxies in the stars portion.

Star data was given by the Coding Wizards in the form of a table and a new column was added for the temperature of each star. This was approximated using the B-V colour index in the formula below which assumes the stars are perfect black bodies. A subset was also created within TOPCAT which separated the stars into their various spectral types via their temperature.

The equation that was used to calculate the temperature of each star.

We also wanted to calculate the volume of space we are surveying per spectral type so we can later use them to find the metal-poor star density per spectral type. The volumes have to be calculated separately as stars are observed as different distances, for example M type stars are dimmer than O type stars so we can’t see M type stars at as large distances. As the data from the Coding Wizards included the stars’ distances, we could find the limiting distances (the furthest star of each spectral type) to calculate the volume.

As the pilot of the ship, La Forge deemed it his duty to know the space he would be navigating – the lives of her crew were in his hands after all.

The area we are surveying (approximately 1.9 square degrees) was used to create a ratio with the total square degrees of the Earth (approximately 41,000 square degrees). The surveyed area could then be worked out for each spectral type by multiplying this fraction by the volume of a sphere where the radius of the sphere was equal to the limiting distance of that type.

As leader of the Theoretician away team, Commander Riker felt it necessary to evaluate every possibility and eventuality – his Captain had taught him well. If any errors were to befall his loyal crew, he would always ensure that he were at the forefront of the fight against them.

Towards the end of the lab session, we started to look into the errors of the stars’ magnitudes. Using errors of the measurements of NB392, U, g and i, we can get an overall error in ‘(NB392 – U) – (g – i)’ and ‘(g – i)’ for each of our stars. The average of these will be taken over all the stars and this will then be given to the coding team to work with.

Whilst the three-person away team tackled the data head-on, Guinan immersed herself in the endless fields of knowledge housed within the Starfleet archives, tirelessly researching our elusive subjects as they continued to hide in plain sight.

She worked hard on forming the start of the report, researching topics used to create an introduction for our report that is to be produced towards the end of the 10 weeks.

The Coding Wizards

After the act of espionage uncovered in our last episode, the fear of a traitor in the midst still gripped Picard – nothing at the academy had prepared him for this – but of course he could not show weakness in this time of great strife. Via secure comms, Picard and Data were advised by Starfleet Command (Dr. Sobral) to be more self-sufficient, using their years of expertise to formulate their own refined method of magnitude calculation.

The session started promisingly with the quick refinement of the code that generated the magnitudes in different filters from the stellar spectra. By creating a spectrum for a sun-like star with a set luminosity, we could find a more accurate ratio for the calculated luminosity and the correct one. 

From the increased accuracy, a new set of colour-colour theory graphs were created.


(NB392 – U) – (g – i) against (g – i). The reason for the more compicated y-axis was to make the lines flatteras they are normalised to the colour (g – i).

At the same time, the limiting distances to spectral types of stars were calculated. The minimum apparent magnitude that can be detected by most telescopes is around 25.5. Since different spectral types have different brightness, O types being the brightest and M the dimmest, the maximum detectable distance for each type of star is different. By taking the absolute magnitude for a set of different stars and a minimum apparent magnitude of 25.5, the distances were calculated and handed to the other half of the team. The goal is to calculate the volume of the sky we are looking at for each type of star.  


Magnitudes at a distance in parsecs for different spectral types. Looks a bit like a printer error.

As Riker boldly faced the onslaught of errors with their eyes trained on the away team, a secret contingent had snuck their way past our cohort of brave explorers and right into the data!  

The next part of the lab was plotting the theory data against the observational to see what cuts could be made. Unfortunately, the observational data was exceptionally spread out and as such we couldn’t make any real analysis. One of the major reasons for this is the fact that the observational data has errors. The magnitudes detected are not exact and as such the data points should really be more like a ‘circle of uncertainty’ (which Picard noted sounded very much like himself).

This gave the Coding Wizards a new-found sense of purpose as they needed to create even more code.  

The first thing we need to do is take each data point in our theory data and generate a random spread of points according to a Gaussian distribution. (The sigma for this distribution will be found from the sigma in the observational data). The concept is to make a cloud of data points for each possible magnitude. This will then give us a theory plot with a larger spread of values to consider the errors in the observational data. Hopefully, then, we will be able to compare the theory and observational points and find cuts due to metallicities. From there we should be able to identify some metal-poor stars.  

Next time we will continue to make this distribution code, figure out an efficient way to plot it using python and make more pretty graphs.  

Using the first-hand information on the errors gathered by the away team, Data hoped to synthesise his own errors. His soulless, calculating mind knew that they could not hope to defeat the errors – his aim was instead to harness them for use in the holodeck. Captain Picard and Data readied themselves. The holodeck, filled with hordes of cloned errors, eagerly awaited them…

Leave a Reply

Fill in your details below or click an icon to log in:

WordPress.com Logo

You are commenting using your WordPress.com account. Log Out /  Change )

Google photo

You are commenting using your Google account. Log Out /  Change )

Twitter picture

You are commenting using your Twitter account. Log Out /  Change )

Facebook photo

You are commenting using your Facebook account. Log Out /  Change )

Connecting to %s