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PHYS40001: Computing Project
Measuring Hubble’s Constant Introduction
In the field of cosmology, which studies the origin and evolution of the universe, one of the most fundamental observations is that of Hubble’s Law. The universe is expanding, which means the distance between objects is increasing with time. Hubble’s Law states that the velocity at which objects, such as galaxies, are moving away from the Earth is proportional to their distance from Earth
𝑣 = 𝐻􏰉𝐷
where 𝑣 is the velocity, 𝐷 is the distance and 𝐻􏰉 is the value of Hubble’s constant.
The distance to a galaxy is inferred from the brightness of the galaxy. The velocity is inferred from the Doppler-shift of light emitted by the galaxy. For a galaxy that is moving away from the observer, the light will appear to the observer to be “redshifted” to longer wavelengths. The relationship between the velocity of the galaxy and the shift in wavelengths is given by the redshift formula
𝑣
𝑐 𝑣
𝑐
Where 𝜆􏰊 and 𝜆􏰋 are the observed and emitted wavelengths of light, 𝑐 = 2.9979 × 10􏰍 𝑚 𝑠􏰎􏰏 is the speed of light and 𝑣 is the velocity of the emitter. The Hydrogen-alpha spectral line (Hα) is a deep-red visible line with wavelength 656.28 𝑛𝑚. This line is commonly observed in the emission spectra from galaxies.
Figure 1 illustrates how 𝐻􏰉 can be inferred from measurements of distance and the “redshifted” velocity.
𝜆􏰊 = 􏰌1 + 𝜆􏰋 1−
Fig. 1: A plot of velocity inferred from redshift versus distance for a range of galaxies. The slope of the line of best fit gives an estimate for 𝐻􏰉.

Project Objective
Given 30 observations of the redshifted Hα line from different galaxies and the distance to each galaxy, write a code in Python to calculate a value for Hubble’s constant. The output from your code should be a plot of velocity inferred from redshift vs. distance for each galaxy, the value of Hubble’s constant inferred from fitting this plot and its uncertainty.
Data
Here is a description of the data that you are given.
The file called “Halpha_spectral_data” contains data for the observed shift of the Hα spectral shift. This file is in .csv format. It consists of 2 header rows, below which are 31 columns of data. The first column is wavelength in m. Columns 2-31 contain spectral intensity in arbitrary units, with each column corresponding to a different observation. An example of an observation is plotted in fig. 2. The first row of the header contains the Date at which the observation occurred and the name of the person who made the observation. The second row contains the observation number for each column of spectral intensity data.
The file called “distances_Mpc” is a .csv file in which the first row is a header. It has three columns. The first column is the observation number, the second column is the measured distance to that galaxy in Mpc and the third column is the instrument response. Note that the order of the observation numbers in the ”distances_Mpc” and ”Halpha_spectral_data” files are different.
𝜆 (𝑛𝑚)
Project Notes
The data for spectral intensity versus wavelength is noisy, as can be seen in fig. 2. In order to use it to calculate the velocity, the data should first be fitted with a combination of a straight line and a Gaussian function. A similar fitting procedure was covered in the final exercise of Core Worksheet 2. Once the data is fitted, the mean value of the fitted Gaussian is a good estimate for the observed wavelength, 𝜆􏰊, of the shifted Hα line.
The instrument response for each observation is either good (corresponding to a value of 1) or bad (corresponding to a value of 0). A bad instrument response can occur if, for example, we have low signal intensity, there is atmospheric interference of the signal or there is a drift in wavelength
Fig. 2: A plot of spectral intensity vs. wavelength. The blue line shows the observed data. The orange line is obtained by fitting a straight line + Gaussian function to the data.
𝐼𝑛𝑡𝑒𝑛𝑠𝑖𝑡𝑦 (𝑎𝑟𝑏. 𝑢𝑛𝑖𝑡𝑠

calibration due to thermal expansion of the instrument. These observations should not be used in the calculation of 𝐻􏰉.
Finding the distance for each observation involves matching the observation number in the header of the spectral data file with those listed in the first column of the distance data. An elegant solution to both this problem is to write code which can read the header in the spectral data file. There are some hints for doing this in the Supplementary Python Knowledge section at the end of this document.
Project Submission
You are required to submit your code (.py file), a plot of redshift velocity vs. distance and the fitted value of Hubble’s constant and its uncertainty. The deadline for submission is 5pm on Friday 27th November. Full details on how to submit will be given out shortly.

Supplementary Python Knowledge
This section describes some Python commands that were not covered in the Computing sessions but which could be useful for the project. You are not required to use these commands but they could make your code neater and more elegant.
Context Manager and Opening Data:
When we loaded in data during the Computing sessions we used the sp.loadtxt command. This is good when we want to load in all the data at once. However, there are instances where we want to open a file and then only read a few lines, and maybe not the whole file (e.g. to read the header but not the data in the file). We can do this with the “readline()” function, which is a file variable associated function.
The following code block is known as a context manager, and it allows us to have more control over how we manipulate our files. We open our data called filename (and use the ‘r’ keyword to tell Python we are going to read this data) and then assign it to the ‘file’ variable. We use a colon and indent our code to show it is part of the context manager (just like for, if and while loops). This can be followed by the readline() function, as shown below. This will read a single row from a file. We can chain these together to read multiple lines from a file. Note that once we leave the context manager the file is automatically closed.
String Manipulation:
When we read the first two lines of the file we opened with the content manager, we end up with variables that look like:
The whole of each line has been read into a single string variable, with no separation for the different cells in the csv file. However, there is important information in each line that you may want to extract as its own separate variable, such as the observation number. To accomplish we can use the following functions on strings to manipulate them:
The split command takes an input argument (in this case a comma) which is the value to split the string on, and then returns a list of the split string.
Another useful command is the .strip() command, which removes whitespace and any special characters from your string. For example, in the following code the str2 variable will be a string with the whitespace removed from the beginning and end of str1.
str1 = ‘ This string has white space. ’
str2 = str1.strip()