Tag Archives: science

KCL Cosmology Hackathon 2026

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On the 5th June 2026 we will host a hackathon at KCL for UG students interested in Cosmology.

Here is the link to the folder containing the data for the three exercises.

For the Least Squares fit exercise, you have a three column file called “lsqdat.csv” that contains x, y and the error on y, sigma_y and you need to find the best fit straight line which goes through this data. Once you have that, you create a file called “teamname.csv” where “teamname” is the name of your team. For this exercise, given the equation of a straight line is y=mx+c there should be the gradient m on the first line, and the intercept c on the second line (one column, two rows).


Here is the link to the google form where results can be submitted.

All results should be submitted in .csv format, that is comma separated variable format so:-

1.0e0,23.4e0
2.0e0,24.7e0
2.6e0,32.43e0
……..

or

1.35e2
2.43e2
………

If everything is working well (and I am sure things will break down at some point during the day), then your results should be published on the big screen, so you can see how well you are doing relative to everybody else. The lower the value of Chi Squared that you produce the better.

For the second exercise, you are trying to predict the value of ΩΛ\Omega_\Lambda which will fit the supernova data. Because we are assuming that the Universe is flat (as confirmed by observations of the Cosmic Microwave Background) that means ΩΛ+ΩM=1\Omega_\Lambda+\Omega_M=1 so that by giving your best bet for ΩΛ\Omega_\Lambda you have provided all the information we require. So we only need a single number.

Here are some cosmology notes. Much more here than you will need today!

Here are the slides from the presentation.

The data for this is in the same google directory as for the straight line. The three columns are redshift (z), the difference between the absolute magnitude (m) and the apparent magnitude (M) which we call μ=m−M.\mu=m-M . We don’t know the precise value of the absolute magnitude, or the precise value of the Hubble constant, so we have to treat this as a nuisance parameter, see notes, listen to presentation and ask the helpers or me for more guidance on this.

The third exercise is obtaining the redshift of galaxies from their photometric observations. The Sloan Digital Sky Survey obtains measurements of many millions of galaxies, but it cannot obtain detailed spectra, and hence redshifts, of all of them, or even the vast majority of them. Rather it measures their brightness in five different filter bands. I have provided you with a training set of 8000 galaxies with these five photometric (brightness) measurements, as well as their redshift, as well as a validation set for you to test your fitting algorithms on. Once you have an algorithm you are confident with, you can predict the redshifts of the galaxies in the test data set, and upload them via the form, this will be a one column list of 1000 redshifts (since there are 1000 sets of five brightnesses in the test file, but no redshifts.)

Please feel free to ask any questions you have, and ask me or the other helpers questions during the day.

Spectrum Response of the Atik 383L+ CCD through Astronomik Blue and Green Filters

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Written by Niharika Rath

Plank’s law is used to calculate the energy per frequency of the light to get the number of photons per frequency.

This is divided by the energy of a single photon to get the energy per frequency.This spectrum is then multiplied by the response curve of the camera (Quantum efficiency) and the response curve of the filter (RGB data from Astronomik). These curves represent the photon detection efficiency of the camera and filters at different frequencies.

We integrate over the response curve spectrum at each frequency to determine the total number of photons that get through the filter to reach the camera.The results are tabulated in a three column table giving the temperature of the light source and the number of photons that get through the blue and green filters to reach the camera sensor. This allows us to estimate the expected flux of the photons reaching the camera sensor and comparing it with our flux values to gauge the spectrum response through the filters. 

import numpy as np
from scipy.integrate import quad
import pandas as pd

# Planck's Law
def plancks_law(wavelength, temperature):
    h = 6.62607015e-34  # Planck's constant
    c = 3.0e8  # Speed of light
    k = 1.380649e-23  # Boltzmann constant

    wavelength_m = wavelength * 1e-9  # Convert wavelength from nm to meters
    frequency = c / wavelength_m
    energy = h * frequency

    exponent = h * c / (wavelength_m * k * temperature)
    intensity = (2 * h * c ** 2 / wavelength_m ** 5) / (np.exp(exponent) - 1)

    return intensity, energy

# Load transmission data for blue and green filters
blue_filter_data = np.loadtxt('Astronomik_Blue.csv', delimiter=',')
green_filter_data = np.loadtxt('Astronomik_Green.csv', delimiter=',')

# Load quantum efficiency data for camera
quantum_efficiency_data = np.loadtxt('QuantumEfficiency.csv', delimiter=',')

# Interpolate transmission and quantum efficiency data
def interpolate_data(wavelengths, data):
    return np.interp(wavelengths, data[:, 0], data[:, 1])

# Calculate total transmission through filters and camera response
def total_transmission(wavelengths):
    blue_transmission = interpolate_data(wavelengths, blue_filter_data)
    green_transmission = interpolate_data(wavelengths, green_filter_data)
    quantum_efficiency = interpolate_data(wavelengths, quantum_efficiency_data)
    return blue_transmission * quantum_efficiency, green_transmission * quantum_efficiency

# Function to compute the number of photons
def photons_per_frequency(wavelength, temperature, filter_type):
    intensity, energy = plancks_law(wavelength, temperature)
    total_trans_blue, total_trans_green = total_transmission(wavelength)
    if filter_type == 'blue':
        #filter_transmission = interpolate_data(wavelength, blue_filter_data)
        ppf =  intensity * total_trans_blue / energy
    elif filter_type == 'green':
        #filter_transmission = interpolate_data(wavelength, green_filter_data)
        ppf = intensity * total_trans_green / energy
    else:
        raise ValueError("Invalid filter type. Use 'blue' or 'green'.")
    return ppf

# Integrate the photons per frequency function
def integrate_photons(temperature, filter_type):
    return quad(photons_per_frequency, 300, 1000, args=(temperature, filter_type))[0]

# Generate table of temperatures and number of photons
temperatures = np.linspace(300, 2898, 50)
blue_photons = [np.log10(integrate_photons(temp, 'blue')) for temp in temperatures]
green_photons = [np.log10(integrate_photons(temp, 'green')) for temp in temperatures]

# Create a pandas DataFrame
results_df = pd.DataFrame({'Temperature (K)': temperatures,
                           'Blue Photons': blue_photons,
                           'Green Photons': green_photons})

print(results_df)

results_df.to_excel('photons_results.xlsx', index=False)
    Temperature (K)  Blue Photons  Green Photons
0        300.000000     -2.318112       1.661602
1        353.020408      3.861581       7.237948
2        406.040816      8.436797      11.367775
3        459.061224     11.962380      14.551051
4        512.081633     14.763506      17.080613
5        565.102041     17.043649      19.139870
6        618.122449     18.936303      20.849181
7        671.142857     20.533090      22.291338
8        724.163265     21.898716      23.524593
9        777.183673     23.080279      24.591446
10       830.204082     24.110034      25.523573
11       883.224490     25.020600      26.344953
12       936.244898     25.829487      27.074464
13       989.265306     26.552946      27.726677
14      1042.285714     27.203924      28.313305
15      1095.306122     27.792870      28.843782
16      1148.326531     28.328306      29.325821
17      1201.346939     28.817258      29.765833
18      1254.367347     29.265564      30.169010
19      1307.387755     29.678124      30.539835
20      1360.408163     30.059073      30.882066
21      1413.428571     30.411932      31.198891
22      1466.448980     30.739715      31.493040
23      1519.469388     31.045017      31.766922
24      1572.489796     31.330085      32.022465
25      1625.510204     31.596877      32.261500
26      1678.530612     31.847103      32.485577
27      1731.551020     32.082266      32.696061
28      1784.571429     32.303693      32.894152
29      1837.591837     32.512557      33.080915
30      1890.612245     32.711132      33.257261
31      1943.632653     32.897873      33.424104
32      1996.653061     33.074859      33.582161
33      2049.673469     33.242839      33.732109
34      2102.693878     33.402484      33.874557
35      2155.714286     33.554401      34.010054
36      2208.734694     33.699141      34.139098
37      2261.755102     33.837201      34.262215
38      2314.775510     33.969034      34.379655
39      2367.795918     34.095054      34.491880
40      2420.816327     34.215637      34.599227
41      2473.836735     34.331203      34.702005
42      2526.857143     34.441918      34.800501
43      2579.877551     34.548149      34.894978
44      2632.897959     34.650164      34.985676
45      2685.918367     34.748209      35.072818
46      2738.938776     34.842511      35.156581
47      2791.959184     34.933282      35.237210
48      2844.979592     35.020716      35.314822
49      2898.000000     35.104928      35.389646

KCL Telescope Alignment

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Written by Emily Davison and Anika Cobby

As the dome atop KCL was recently rebuilt, the telescope needed to be re-aligned.

The shiny new dome, with a new, wider hatch.

This telescope is mounted on an equatorial mount, which compensates for the Earth’s rotation by polar aligning with North’s celestial pole to track astronomical objects and keeps the telescope steady; this mount must face North to function correctly.
This was our first task, before the telescope could be mounted. We located North by
identifying a local church spire, which happened to be directly North of the telescope. The longitude for the telescope and spire were the same to 3 decimal places, -0.115. By loosening the 6 bolts around the base of the mount, holding it to the pedestal, the mount can be rotated carefully without needing to dismantle the telescope. The mount was then turned to face the spire, aligning it with North as accurately as we could. Only then could the telescope be mounted.

The spire directly north of the KCL observatory.

Next, we used the built-in features of our telescope’s program. Coordinates and the
hemisphere were selected before we decided on an appropriate program to polar align the
telescope. There are many programs to select from, but the All-Star Polar Alignment function
is the simplest and quickest function to use. It allowed us to identify approximately where a known astronomical object will be in our location’s sky, direct our telescope to that object and
then manually align & track the object with the telescope.
After manually centering the selected alignment object in our finderscope and eyepiece, this function will then model the sky based on the input and centering such that we could enter the latitude and longitude of star clusters to locate them in the night sky.

For more information on aligning the telescope, look here:
https://www.celestronmexico.com/pdf/500003_manual_y_software/Manual_de_Instrucciones.pdf