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PHYS40001: Computing Project
创建日期：2024-05-16 17:42:17
所属分类：物理Physical
标签： PHYS40001
Computing Project

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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
= ଴
where is the velocity, is the distance and ଴ 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
௢
௘
= ඩ
1 +
1 −

Where ௢ and ௘ are the observed and emitted wavelengths of light, = 2.9979 × 10଼ ିଵ is
the speed of light and is the velocity of the emitter. The Hydrogen-alpha spectral line (Hα) is a
deep-red visible line with wavelength 656.28 . This line is commonly observed in the emission
spectra from galaxies.
Figure 1 illustrates how ଴ can be inferred from measurements of distance and the “redshifted”
velocity.








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 ଴.
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 3 header rows, below which are 60 columns of data. These
columns are in pairs, with each pair corresponding to a different observation. The first column of a
pair is frequency in Hz. The second column of a pair is the spectral intensity in arbitrary units. The
observation number is at the top of each data column. An example of an observation is plotted in fig.
2.
The file called “Distance_Mpc” is a .txt file in which the first two rows are 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.



Project Notes
The data for spectral intensity versus frequency 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
frequency, from which an observed wavelength, ௢, of the shifted Hα line can be calculated.
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. frequency. The blue line shows the observed data. The orange line is
obtained by fitting a straight line + Gaussian function to the data.
f (THz)





(

.


calibration due to thermal expansion of the instrument. These observations should not be used in
the calculation of ଴.
Finding the distance for each observation involves matching the observation number in the spectral
data file with the observation number in the distance data file since the order of observation
numbers is different in both files. You should write Python code to do this matching.
Commenting your code & plagiarism
The project must be your own work and a full plagiarism check will be carried out on all submissions.
It is acceptable for you to use pieces of code from online sources, your colleagues, etc. This is good
practice for computer programmers. However, when you do so, you need to clearly state the source
of the piece of code using a comment (#). More generally, we expect you to effectively comment
your code such that another programmer could easily understand what the code does, e.g. brief
descriptions of functions, definitions of variables, etc. Submissions that contain little or no
comments will be closely scrutinised for plagiarism.
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 2pm on Friday 25th
November. Full details on how to submit will be given out shortly.
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