BAHAMAS: Black Active Holes Are Massive And Super

Hello, we are BAHAMAS, we are a research group of 5 undergraduates at Lancaster University currently working on the PHYS369 Astrophysics Group Project Module in which we have decided to research Distant Active Super massive black holes: X-ray and Radio studies of AGN in SC4K, more specifically the jets that form out of the AGN, and the properties of them. In this 10 week project we will be updating this blog weekly with our progress and outlining what we find during our research labs and our meetings outside of these labs.

Weekly Research Updates:

Week 1

This project began its initial stages of planning in week 1, the 13^{th –}17^th of January where the group roles are assigned as follows:

James Warner – Coordinator/PI
Daniel Head – Theory Lead
Stephen Griffin – Administrator
Georgia Stevens – Communications lead
Alaister Foster – Data lead

We do acknowledge that these roles are set but that every group member should still contribute to every aspect of the project and that some new roles may become apparent or roles may change, all of which can be discussed at a later date between the group and our advisor.

We have decided that we are BAHAMAS (Black Active Holes Are Massive And Super), and our research goal for this astrophysics group project is to use the SC4K survey in the COSMOS field, from deep Chandra Legacy X-Ray data and the VLA sets of 1.4GHz and 3GHz Radio data, to identify jets of AGN. Using both the X-Ray and Radio properties of the jets to analyse their corresponding data we hope to:

Identify the relation between the X-Ray and Radio wavelengths to the presence of jets, and if there is some type of condition/specification for a jet to occur due to the radiation emitted from the AGN
Identify the relation between the flux of the AGN and the presence of jets
Investigate relationship between the jet size and the accretion rate
Identify is there is a relation between the accretion rate of an AGN and the presence of jet

Our first collaborative task of this project was to prepare a presentation to be presented the following week to our peers and advisors, in which we will outline our project aims, the background scientific theory around this, and a Gantt Chart made to better help our overall time management and planning throughout this 10 week journey. Our Gantt Chart is shown here:

With each task having a specific time interval in which we hope for it to be completed and a group member has been assigned to lead each task, we hope that this schedule can be kept fairly well such that our project remains stable and on track.

As a group we all went away from our first meeting with the task to each individually complete our designated part of the presentation, involving the relevant research, making power-point slides and having a small script to keep our presentation within the expected 10 minutes.

We met again on friday to do a small run through of our presentation in which we were all happy with how things have progressed, and will see how the presentation goes next week.

In terms of milestones, in week 1 we planned to have our initial project planning, role assignments, presentation preparation, and literature search beginning, to be completed which it has.

Week 2

Upon presenting to our peers and advisor, we were given the feedback of

The X-ray and radio data do not really provide you with spectra. You do have access to data in two different bands in the X-rays and two different bands in the radio so you can “estimate” whether the spectrum is going up or down, but not much more.
In general, you could have more higher level background/science background.
Use diagrams/pictures even more, and you could have the picture of the AGN earlier on. Never forget to add a citation/reference when you take an image from somewhere, preferably a paper.
Use some references to substantiate statements that you do throughout the presentation.

Have you used DS9 to open the radio and X-ray data? You should start looking at it.
↳We have now carried this out and have outlines our findings below.
Cite reference for the diagram
↳This should have been done before the presentation, but it has now been fixed for future reference.
What equations will you use? It would have been useful to see, and it should be useful for you to identify and write down relevant equations form Calhau et al. (2019), the “reference paper”.
↳ We have compiled a list of relevant equations from the reference paper and other sources that will help our future data analysis in the research labs.
What Astro tools will you be using?
↳The main programs we hope to use will be Topcat and DS9 to evaluate the images and carry out calculations.
Why can’t you see the galaxy and what wavelength is the AGN/Jet image?
↳In the AGN/Jet Image the galaxy itself is not visible because the photo is taken from the radio wavelength emissions and this specific galaxy is dead, and has no star formation processes taking place and is therefore not emitting any radiation that can be detected.
What wavelengths and energy will you be studying? Make sure you can convert well.
↳ We will be using Radio 1.4GHz and 3GHz alongside X-Ray wavelengths.

These comments were taken into consideration and our responses have been summarised below them.

After our first meeting with our advisor it was suggested that we look at the data before the start of the research labs, and learn how to use Topcat and DS9 such that the labs will be easier. We set that before next meeting we will all go away and read the given reference paper – and follow the provided tutorials on Topcat and DS9 to be ready.

We met later in the week with our new gained knowledge. We looked through the visible image data of the ~3,000 AGN galaxies and found an identifiable ~40 jets.

The given data set is represented:

Where from this, ~40 AGN with jets have been identified and shown:

With this glance into our data we are able to know how big our sample size is, and work towards our first steps that we shall carry out in the first research labs, such as what information we would like to calculate from these identified jets, and we shall discuss with our advisor on how to approach this.

In terms of milestones of this week, the literature search is going as expected with Daniel as the Lead, developing a document outlining published papers that are similar and/or relevant to our research goals, and summarised notes surrounding each paper. The overall plan of this project has been mostly developed and for the week ahead we hope to prepare and carry out our first research lab.

Week 3

In our first research lab we set out to evaluate all jets in the provided data sets of the X-Ray from Chandra and Radio 1.4GHz and 3GHz from VLA. Last week we already determined a list of suspected jets by visually looking at the Radio 3GHz data, but now we shall compare these suspected jet Radio 3GHz images to the X-Ray, Radio 1.4GHz and Hubble optical data. Infrared data has been provided but is very low resolution from which we cannot draw any helpful conclusions by considering it in image comparisons.

A confirmed jet we have identified, was identified through visually comparing an image from the X-Ray Chandra whole cosmos data, the top left image shown (cosmos_whole_0570_DS.fits), 1.4Ghz Radio Data bottom left image shown from the VLA (VLA_1.4GHz.fits) , 3GHz Radio Data top right image shown from the VLA (VLA_3GHz.fits), and Hubble’s optical range data bottom right image shown (HST-ACS Mosaic (Mosaic v2.0 [cycle 12 + 13] in full resolution [0.03″/pix]) from Cosmos Cutouts (https://irsa.ipac.caltech.edu/data/COSMOS/index_cutouts.html), if this is not available or we are unable to decide, we also compare with Lyman-Alpha visible data from (SUBARU Tiles (Tiles v2.0, including rms images; B, V, gp, rp, ip, zp, and intermediate Bands), the intermediate bands.

An identifiable jet such as SC4K-IA464-75921 has visible shapes in the radio images and will have no visible jet data in the X-Ray data, instead the X-Ray and visible data will confirm where the source galaxy is in comparison to the jets, making sure the jets are from the selected source, shown below.

The shown jet below is found near the data point of SC4K-IA454-234750, it is a jet but the AGN producing it is not in our data set of surveyed SC4K, as seen the AGN does not have an identifiable green circle around it that identifies all galaxies in the data set, such that we cannot include it in our final data sets. This removes a lot of potential jets from our initial observations of the data, which leaves us with a very small number of jets in this data set.

Going through the process of comparing all images we find from ~3,000 galaxies in the SC4K survey, 5 are jets identified as:
SC4K-IA427-26216 (1)
SC4K-IA624-297973 (1)
SC4K-IA464-75921 (1)
SC4K-IA484-111739. (0.5)
SC4K-IA484-33705 (0.5)
The numbers after the classification, in brackets, show a confidence number towards our observations, where 1 is confident it is a jet and 0 is no confidence.

We acknowledge that 5 data points is not enough to form a sample in which to carry on this group project investigation so we use the full Radio Data Catalogue to observe all galaxies in the SC4K survey, not just specifically the Lyman-Alpha emitters that we have originally been using as the classification of our data set. With this larger data set to examine we set parameters/constraints such that the red-shift must be 2.2 < z < 3.5 and the brightness of the 3GHz Radio data must be above 10μJy such that any more found jets will be easily comparable to the already found data set, the brightness limit makes the jets more easily visible to identify.

The distribution of number of galaxies within the data set against red-shift. The green data points show the new data available within the placed constraints.

With this new data set, we are ready to go into our next research lab next week with more galaxies to observe from this new Radio Catalogue found from the constraints placed. By running a python code analysing and comparing the background brightness of an image we find ~ 50 potential jets that we can use the same method of image comparison in Topcat and DS9 to identify next week.

In terms of milestones we are on track with our search into finding jets of AGN to continue into next week as planned. The literature search is also continued throughout and is going well, and alongside we have published a relevant equation list that will help us greatly with data analysis coming up in future research labs.

Week 4

This week we set out to finish identifying all jets from our data sets of both the SC4K Lyman-Alpha Emitters and the Full Radio Data Catalogue. In the same process of last week, by evaluating in DS9 the images of Radio 1.4GHz and 3GHz, alongside X-Ray Chandra and Hubble visible images, we found overall 12 data points of jets emitted from galaxy sources. These 12 data points are shown in the table below.

We decide that we would like to calculate from this data set:

Total energy passing through jet exit (power)
Luminosity of jets
Magnitude of jets
Fluxes of jets
Radio / X-ray star formation rate of galaxy with jets
Accretion rate of galaxy with jets

These calculations can lead us to our research aims of:

Identifying the relation between the flux of the AGN and the presence of jets
Investigating relationship between the jet size and the accretion rate
Finding the relation between the accretion rate of an AGN and the presence of jets
Investigating the relationship between the BHAR and jet velocity for SC4K galaxies and (all) galaxies within 2.2<z<3.5 range

To begin our research into these areas, we set out to measure the counts of each jet in the Radio 3GHz images by using the software of Gaia, in which we import our table from Topcat and manually select the galaxy jets alongside the background/sky and use the ‘Aperture Photometery’ option to calculate this data. The program usually calculates the sky/background count data by using a circle with a larger radius around the manually placed circle around the measured data point, this however would not work for our data as the background circle would overlap and include brightness from the galaxy source which would alter the overall sky counts so we chose to manually select both measurement circles separately. We took a screenshot of each data set once it is calculated for future reference as to how we took these measurements.

Using Gaia to measure the counts of each jet of each galaxy source in Radio 3GHz images.

The same process was repeated in Gaia for the X-Ray Chandra images, in which we measure the counts of the galaxy source using a set semi-major axis circle of 2.91 for each measurement to make all data sets comparable. The X-Ray data set is much lower resolution and does not include such activity in the measurements meaning it is possible for the program to select its own background/sky count circle without our manual input.

Using Gaia to measure the counts of each galaxy source in X-Ray Chandra images.

We aim to repeat this process again for the Radio 1.4GHz images next week, as we run out of time in this lab.

In terms of milestones we are very much on track by finding all of our jets by Week 4 and starting the data analysis on them also in Week 4, alongside the literature search which is continuing throughout and going well. Next week in the lab we aim to use our count data to start calculations of magnitudes and luminosities and working towards our project aim analysis as discussed.

Week 5

This week we start on our data analysis of our Radio 1.4GHz and 3GHz, alongside our X-Ray data of soft, hard and combined wavelengths.

Our first step of data analysis is to convert all found counts in Gaia as measured last week, into fluxes by the conversion of:

For the Radio 3GHz & 1.4GHz: $1 [Jy/beam] = 10^{-26} [Wm^{-2} Hz^{-1} ]$
For the Infrared 24μm: $1 [Jy/beam] = 10^{-23} [ergs^{-1}cm^{-2}Hz^{-1}]$

The Radio Fluxes are calculated by:
$Flux_{RadiovGHz} = Count data_{vGHz} \times Conversion Factor [Wm^{-2} Hz^{-1}]$
Where v represents the specific frequency of the radio source.

The Luminosity Distance of each source is calculated by a function in Topcat where we assume Hubble’s Constant $H_{0}=70kms^{-1}Mpc^{-1}$ , the density ratio of the universe $\sigma _{m}=0.3$ and the normalised cosmological constant is $\sigma_{\Lambda}=0.7$ , with the variable redshift $z$ for each galaxy.

The Luminosity distance function in Topcat:
$dL = luminosityDistance(z, 70, 0.3, 0.7)$

The Radio Luminosities are calculated by:
$L_{vGHz} = \frac{4* \pi *dL^{2}}{(1+z)^{\alpha +1}}*F_{vGHz}$
Where we assume $\alpha =0.8$ , the characteristic spectral index of synchrotron radiation and a value typically found in AGN, as discussed in the Reference Paper.

The errors of Luminosities are calculated by:
$\sigma_{L_{vGHz}} = \frac{ \sigma_{count data_{vGHz}} * 10^{-26}}{F_{vGHz}} * L_{vGHz}$

The X-Ray Fluxes are calculated by:
$F_{X_{0}} = (counts/s) * CF * 10^{-11} [ergs^{-1}cm^{-2}]$
Where CF is conversion factor, of 0.687, 3.05, 1.64 for our Soft, Hard and Full Band wavelengths of data, as explained in the Reference Paper.
The Fluxes will need to be aperture corrected through:
$log_{10}(F_{X}) = log_{10}(F_{X_{0}}) + A_{C}$
Where $A_{C} = 0.1$ as followed in the Reference Paper.

The X-Ray Luminosity and Infrared Luminosity can be calculated by:
$L_{X/IR}[ergs^{-1}] = 4*\pi*F_{X/IR}[ergs^{-1}cm^{-2}]*dL^{2}[cm^{2}]$

The star formation rates (SFR) of the galaxies with jets has been calculated in the Full Radio Catalogue data set that contains 6 of our 10 data points. We take an average of the SFR of our 6 data points and compare it to the average SFR of all the galaxies in the radio catalogue within the constraints placed in Week 3 of 2.2 < z < 3.5 and luminosity > 10μJy of the 3GHz sources, and we find that:

SFR of galaxies with jets ~ 120 M $\odot yr^{-1}$
SFR of galaxies without jets ~280 M $\odot yr^{-1}$

This shows a significant difference between the two values and we discuss the theory of that by the time jets formed from galaxies have travelled/grown to be at such a distance that they are large enough to be observable, the AGN source of the galaxy has died and therefore the galaxy source has lower SFR’s. This is an interesting development of which we have predicted, it means for our research into the properties of galaxies with jets / requirements for jets to be formed in galaxies, may not fully be able to be investigated considering the galaxy sources are all dead, but to confirm this we will calculate the SFR of the remaining 4 data points that have not had this property calculated in the SC4K COSMOS Data Set we have been using.

We follow the steps taken by https://arxiv.org/pdf/1302.1858.pdf to find the SFR through Infrared 24 $\mu m$ luminosities we have measured:

Star Formation Rate is calculated by:
$SFR [1M_{\odot}yr^{-1}] = 1.27 \times 10^{-38} * (\nu*L_{24\mu m}[ergs^{-1}])^{0.8850}$

We use Gaia to load in COSMOS Cut-Out FarInfrared Data images from Spitzer-MIPS (Mosaics v1.0, v2.0; 24μm, 70μm, 160μm science and ancillary images) of our jet data points and use the Aperture Photometry methods as last week to calculate count data, which we can convert to flux, to luminosity to SFR.

With some discovery of typing errors in our data calculations and the discussion that our initial count data in units of Jy/beam, it means we need to calculate the conversion of a beam in our data set when making these flux conversions, which means that our calculations are not quite accurate. We can tell this anyway by our magnitudes of values being out by ~ factors of $10^{10}$ . We also find that the data points find flux in units of $Hz^{-1}$ which we need to integrate over all possible frequency bands before any further calculations. This will be our next steps next week.

In terms of milestones, we are on track as we have started the write up of our final group report, alongside continued literature search which is now being used in the write up of theory and background content with the introduction too. We have continued our data analysis and error analysis with our group project aims being constantly considered as we plan our next steps of calculations and data investigation next lab.

A Brief Interlude

This brief interlude is to show you what might be our biggest research goal achievement to date; a blog visit from the BAHAMAS itself, with a statistical map of views for proof!

Week 6

Following week 5, we have carried out all of our calculations on our data so we get started on plotting graphs and we start some overall analysis alongside constantly writing our report as we go.

We use python to plot all graphs for our data analysis of this project, which is not our strongest skill as a group so this might have been a very long session.

A figure showing the RA against DEC co-ordinates of all data sets we used, and in relation showing the 10 jet sources we found amongst this.

Figures showing the relation between Radio 3GHz Fluxes of our data set of jets compared to all galaxies without jets.

When calculating the Radio 3GHz fluxes, we used a beam area size of $14 [pixels^{2}]$ by assuming the beam used in our calculations is the minimal measurement size due to the limits of the telescope in an area of square shape.

We find that the Radio Fluxes of jets compared to the Radio Fluxes of galaxies without jets show a significant difference that the jets are on average much brighter than the radio emission from galaxies. The luminosities also show this pattern, by factoring in the distance of each galaxy and jet, we still find the same results.

Figures showing the Radio 3GHz Luminosity of galaxies with jets compared to galaxies without jets.

Figures showing the X-Ray Luminosity and BHAR of galaxies with jets compared to galaxies without jets with respect to redshift.

We find that galaxies with jets seem to be X-Ray quieter compared to galaxies without jets, it suggests that galaxies with jets are older and almost dead galaxies, with no high X-Ray activity to show an active galaxy behaviour.

Figures showing the Infrared calculated SFR of galaxies with jets compared to galaxies without jets.

We find that the SFR of galaxies with jets is on average lower than galaxies without jets. This tells us that possibly the galaxies with jets we are looking at are dead/dying galaxies, this could be due to the theory that by the time the jets have reached a luminosity and size that is large enough for us to observe, so much time has passed such that the galaxy that created the jets is no longer active.

A figure showing the relation between BHAR and SFR of galaxies with and without jets.

Week 7

This week we began by looking at the morphology of each of our jets we have observed.

The Figure shows the morphology of each jet in the Radio 3GHz band, each labelled respectively with their placement in our data table of jets.

Fanaroff and Riley (1974) developed a classification scheme for radio sources, that we can use to classify the morphology of our jets. The two classifications are FR-I and FR-II, where jets from these sources have different morphologies, such that FR-II jets have bright radio lobes and bright hotspots whereas FR-I jets have much darker edges, as demonstrated in the figure below.

This enables us to say that approximately 8 of our sources produce FRII jet morphologies and 2 of our sources produce FRI jet morphologies.

We also thought to compare the radio luminosity of the jets to the accretion rate of the host black hole as we expected some correlation. However, as can be seen here, there is no obvious correlation between the accretion rate of the black holes and the luminosity of the radio bulbs of the jet.

Week 8

This week amongst mostly writing our report, we took a minute to consider the safe viewing distance of a jet, if you were to want to visit one in the spaceship you totally own, and of course wanted to be a safe distance such that the ionising radiation it produces doesn’t harm you too much, so; to calculate the energy flux of the jets we had to make some assumptions:

The opening angle of the jet was the minimum
The only energy output was as radio waves in the bright spots
All the energy was being carried by ionising particles

This allowed us to make some comparisons to more comprehensible energy levels and provide an idea of a safe approach distance if anyone was planning to visit one.

The figure shows the flux produces by our jets in comparison to flux produced by the Sun and some standards that have been medically agreed as a safe amount of fluxes of ionising radiation at different distances from sources.

As you can see, you cannot be within ~ $10^{17}m$ of a jet without being significantly hit with ionising radiation produced from the jets.

Week 9

This week we presented our results, methods and findings of this project so far to our peers, and will receive feedback later this week that we can evaluate and work on. Here is an overview of our slides for our presentation that was approximately 15 minutes long.

Our feedback included to label our jet morphologies on our images, and to discuss the distribution of galaxies with jets found in the SC4K Catalogue compared with the Radio catalogue used to find all data points, whilst also considering if jet morphologies can have any cause to the properties found of the AGN jet producing sources.

Radio 3GHz images of each of our 10 galaxies that produce jets, with their morphology type labelled and the corresponding data table index included so that they are identifiable.

It was also suggested to us that we should include on our plots of jet luminosity and fluxes against redshift compared to galaxies without jets, we should include a data set representing galaxies with jets as a total summed luminosity or flux so that has been added and they are shown here.

Figures showing galaxies with jets, luminosities and fluxes, plotted with jets and galaxies without jets.

Week 10

The final week of our project and research! Well, our research is very much done and our report is getting it’s final touch ups! A small review of our project overall:

Our project aims discussed and decided in Week 2 were to:

Identify any relationship between X-ray and Radio emissions of Active Galactic Nuclei
Compare these to the accretion rate of the host supermassive black holes
Expect to see the emission rates increase with respect to each other and the accretion rate
Compare every measurement of our jet sources against those without to attempt to give insight on what effects jets have, and what their causes may be

We have visually looked at over 4,000 images of galaxies and were able to identify and confirm that we have found 10 galaxies that are producing jets, a total of 16 observed. We did this through the use of DS9 and comparing the X-Ray, Radio 3.0GHz & 1.4GHz and Hubble Optical images, where we find a jet is visible in the radio spectrum only and the AGN producing them should be identifiable in our X-Ray spectrum. This was carried out over Week 2 and Week 3.

Throughout Week 4 we used Gaia and Topcat to manually measure the flux density of counts of each jet we observe, and we were able to convert these measurements to radio fluxes and luminosities. Along with using the Infrared Spectrum of the AGN sources to find the Star Formation Rates of the galaxies producing jets. Using the X-Ray data we were able to calculate Black Hole Accretion Rate and X-Ray Luminosities of the galaxies producing jets. All formulae were outlined in our Week 5 blog update post.

All of our calculated data of the properties of the jets and the galaxies that are producing these jets were compared to the SC4K and Full Radio Catalogue of galaxies without jets, and we were able to plot many graphs as seen above in Week 6,7,8 & 9.

We find and conclude;

There are a total of 10 jet producing galaxies in the COSMOS field with 2.2 < z < 3.5. 4 of which are L $-\alpha$ emitting galaxies with jets in this region representing 0.13% of the population and 8 are Radio galaxies with jets representing 0.71% of the population, with 2 galaxies in both sets
The average luminosity of the jets is $2.272\pm0.010\times10^{25}$ erg s $^{-1}$ in the 3GHz band and $3.104 \pm 0.014 \times 10 ^{25}$ erg s $^{-1}$ in the 1.4GHz band.
The accretion rate of the AGNs producing these jets averages to $0.141^{+0.078}_{-0.016}$ M $\odot$ yr $^{-1}$ and the star formation rate of these galaxies is varies between 352 and 9 M $_\odot$ yr $^{-1}$ with an average of 108 M $_\odot$ yr $^{-1}$ .
The X-ray luminosity and BHAR of the galaxies with jets is decreasing over time faster than the galaxies without jets.
The X-ray hardness of jet producing galaxies with z < 2.9 averages to $0.41\pm0.21$ , which is considerably higher than that of galaxies without jets of $0.14\pm0.47$ , with a sharp drop at $Z\approx3$ where the hardness of the galaxies with jets drops to < 0.0
The star formation rate of galaxies with jets is decreased by $\sim160\rm M_\odot \rm{yr}^{-1}$ from the average of for galaxies without jets of $\sim 280 \rm M_\odot \rm{yr}^{-1}$ to a much lower $\sim 120 \rm M_\odot \rm{yr}^{-1}$
The BHAR of the galaxies with jets on average, of $0.141^{+0.078}_{-0.016}$ M $\odot$ yr $^{-1}$ , is lower than the average of X-ray detected galaxies, which is $0.271^{+0.091}_{-0.019} \rm M\odot \rm{yr}^{-1}$
The presence of jets in a galaxy drastically affects the potential habitability of an planetary system which passes through the jet at any distance < 1 Pc

Overall our Gantt chart was accurate, with maybe a few small changes, our group roles and dynamics slightly changed but we all agree that we are proud of our project and the results we have found and we thank you for reading our blog and following us on this journey of a third year physics astrophysics group project.

Data Set

This is our final data set table showing the information for each measured jet, there are 16 jets across 10 galaxy sources.