A Tumblr/blog by an astronomy PhD student, documenting her work, recording her life and explaining the wider craft. Because science is tasty!
So this was the most exciting thing to happen all day; the fan blade just shattered on the rotating fan in our office. Oh, the perils of working as a PhD student! Terrified the life out of us all!
On the bright side, I finished my annual review report today, which consisted of two draft papers!
Hi! Just reposting an infodump I made for r/cosmology on reddit in case someone else is interested.
“Although this simulation isn’t new, it’s over a year old. I’ll just info dump about simulations if you don’t mind.
So the simulation itself is worth mentioning. The code used is called AREPO, and it’s ridiculously clever. Broadly, simulation codes work in one of two ways; on a grid, or with particles. Grid simulations split their simulation box up into tiny cubes (cells) that each have associated with them numbers for a temperature, energy, density, amount of hydrogen… etc. At each time-step in the simulation, physics equations (and approximations to them in some cases) are applied to the cubes and their neighbours and the numbers are updated to show e.g. gas moving from one cell into another.
Particle simulations do something different a bit different. Particles are spread out in the box, and particles are assigned a mass. The particles then move in the box under the influence of gravity, so the particles gravitate towards the densest point, giving automatic higher resolution in the denser areas. The resulting particle pattern is smoothed out to calculate the density and other values.
AREPO combines these two methods using a moving Voronoi mesh (here ’s what it looks like, I can’t find a video/gif but it’s mesmerising to watch). In comparison tests it seems to reproduce and keep turbulence better than a particle code called GADGET, which is interesting. AREPO uses the same particle solver as GADGET to do the dark matter portion of the simulation, mesh is hydro only. AREPO and GADGET were both written by Volker Springel for the most part.
This particular simulation isn’t a big one by any means, which is to be expected as I think this is just the proof of concept simulation? Looking at the vid description it’s a 20 Mpc box which means that at the output time of z=2 (3.3 billion years after the big bang) when this snapshot was taken, the box is ~7 Mpc wide or in more easily pictureable terms, 23 million light years, or 700ish Milky Way galaxies end to end.
The simulation itself shows the distribution of gas in the box and highlights some large galaxies and galaxy mergers. They’re drawing attention to the structure of the galaxies and the tidal streams of gas during the mergers to show how well resolved and complicated they are.
Tl’dr, this simulation is a showcase for the AREPO code.”
I wouldn’t mind so much if I didn’t have a deadline to meet. I suppose my supervisors would mind regardless, haha.
It’s been a bit stressful lately; it’s customary for PhD students to give a seminar on what they’ve been up to to the department and I gave mine on Thursday. It wasn’t perfect, but people were positive about it so that’s a good sign. Disseminating your work is an important part of science but presenting is something I really dislike because it’s exposed. I was so relieved after the presentation ended that I signed up to give a talk on my work to a visiting school at the end of June. This means there’s going to be some kids out there seeing pretty pictures at some point. That should be a fun talk to give and good practise!
More pressingly, I have a talk to give in Lyon, France in just over a month. I need some bigger simulations run before then. At least I know what I’m analysing, but it’s just getting the code to work on the super-computer (it’s still not playing ball).
I found a supernova in my simulation though! I might put it up and explain how I know that as that’s not really what I’m working on. After I’ve done my work, of course(!).
The purpose of data analysis is to work out whether the values of parameters are compatible with data. By parameters, I mean some measurement(s) that has been created by theory or past experiment.
During model selection, one may ask what parameters are indicated by the data.
In combining these two analyses, one can create a feedback loop that simultaneously tests how well a theory fits a set of observational data AND see how well observational data obeys a theory. To do this effectively we need to demand certain criteria from both data and models:
Useful models:
- Fit the present data acceptably.
- Have the ability to make predictions for future data (this is computationally expensive).
Useful data:
- Will be predictable by the models we aim to test
- Have modellable intrinsic randomness (e.g. cosmic variance, sample variance) & experimental error (instrumental accuracy, noise)
Bayesian Inference
Bayesian Inference is a system of logical deduction which assigns probabilities to all quantities of interest. The marked difference with this and regular (frequentist) statistics is that you now take into account how reliable /unreliable a quantity is. Frequentist statistics only really allows for an “absolute” truth and is not as suited to making predictions.where A and B can be anything. So lets make P(A) the probability that you are wearing a hat versus P(B), the probability you’re going to a casino. The above equation then reads as: the probability that you are wearing a hat, given the fact you are going to a casino, is equal to the probability you are going to a casino now that you’re wearing a hat multiplied by the probability you are wearing a hat, divided by the probability you are going to a casino. The key thing is here, if you’re unlikely to be wearing a hat, you’ll unlikely be going to a casino, UNLESS it’s more than likely you wear a hat whenever you go to a casino! Random bits aside, Bayes theorem is a clever doodle to test whether something is related to something else. It may even be a good thing to find if something isn’t related to something else either! Back on topic though, for data analysis the concept stays but the variables change thusly:
Prior P(θ) has no data dependence - more on this follows.
Likelihood P(D|θ) involves making a new calculation to evaluate the data against the model.
Posterior P(θ|D) then is the ability of the model to reflect the data.
Marginal LikelihoodP(D) is commonly ignored: it is simply a number that normalizes the probabilities to unity.
Now, priors. They can be a source of disagreement in calculation since how much you trust parameters (from previous results/theory) depends on how you feel about them. But the upshot is, you have to lay it down on paper, illustrating any point you have to make on them will be clear in the calculations you make. This is also where a little bravery comes into play. You have to force your assumptions into play, exposing them. This is where physical intuition comes into play. The trick is, don’t seek a single “right” prior. Rather, test the robustness of your conclusions under a reasonable variation of the priors. Bayesian statistics ensures that, eventually, good data will overturn an incorrect choice of prior. Bravery aside, hardcore bayesianists (is that a word?!) simply comment “if you don’t know enough to set a prior, why did you bother getting the data”.
Bayesian Parameter Estimation
So with the above knowledge in tow, how do you use the thing?
1. Choose a model (a choice of a set of parameters to be varied to fit a dataset):
- Set parameters to be varied
- Set prior ranges for these parameters
2. Choose datasets
- Having the most powerful/complete/accurate dataset is all well and good, but adding more data will only improve your result.
3. Compute a likelihood function… that is to say, the probability something will occur given another situation. The reason it’s a function rather than a number is that we are now playing with a whole bunch of parameters (the model) and not just one.
4. Obtain the posterior parameter distribution… basically plug in and play!
In this way, a huge dataset can then be summarized into a handful of parameters. Taking a cosmological example, From the WMAP satellite we receive a series of time-ordered images covering the whole sky; these are then composited into a map of ~10 million pixels. Make an assumption about this map (such as the likelihood of getting this map given gaussianity of the cosmic microwave background [CMB]) and we can obtain the power spectrum of the CMB, which has reduced those 10 million pixels into ~1000 parameters for each measurement of an angular power. Then take a model and compare it to the likelihood of obtaining the observed power spectrum and we get a handful of parameters describing, say, Ωb, ΩDM and the Hubble parameter.
Source: crashtronomy
Massive black holes halt star birth in distant galaxies
Above is a picture from the new Nature paper led by Dr Mat Page of MSSL, written as part of the HerMES consortium. The yellow blobs are light from star forming regions in galaxies (from the Herschel Space Observatory’s 250µm and 350µm bands) and the blue are X-ray sources showing where black holes are growing (from the Chandra X-ray Observatory from 0.5-8 keV). Page et al found that the galaxies with the fastest growing black holes at their centre have less stars forming in them than one would expect. This implies that radiation from the accretion disk around black holes hinder stars formation.
I’ve tumblred the full press release here.
This is the full press-release (sans contact details) for the new HerMES consortium Nature paper led by Dr Mat Page of MSSL. I’m putting the full one up for those interested, doing it as two separate posts to get the tl;dr one with the picture out. (PS, Can you tell I’m somewhat invested in the HerMES consortium?)
Astronomers, using the European Space Agency’s (ESA) Herschel Space Observatory, have shown that the number of stars that form during the early lives of galaxies may be influenced by the massive black holes at their hearts. This helps explain the link between the size of the central bulges of galaxies and the mass of their central black holes.
All large galaxies have a massive black hole at their centre, each millions of times the mass of a single star. For over a decade scientists have been puzzled as to why the masses of the black holes are linked to the size of the round central bulges at the hearts of galaxies. The suspicion has long been that the answer lies in the early lives of the galaxies, when the stars in the bulge were forming. To study this phase, astronomers need to look at very distant galaxies, so far away that we see them as they were billions of years ago.
Although the black holes themselves cannot be seen, the material closest to them can get incredibly hot, emitting large amounts of light over a very wide range of wavelengths, from radio waves to x-rays. The light from this super-heated material can be trillions of times as bright as the Sun, with brighter emissions indicating a more massive black hole. There are also strong flows of material (winds and jets) expelled from the region around the black hole.
The hot material near the black hole outshines almost all the light from rest of the host galaxy, except for the light with wavelengths just less than a millimetre. This sub-millimetre light is invisible to normal telescopes but is seen by the Herschel Space Observatory and indicates the rate at which stars are being formed in the galaxy.
We have a press release in hand, too! Unfortunately there’s an embargo on the details of the paper until 19:00 UTC tonight (Nature is very weird like that) but rest assured that as soon as I get these last slides completed, I’ll be putting up the press release on the tumblr queue to publish on the deadline. So you guys will be literally the first people to read it. WOO!
Also thank you to whoever spoke to the yt team (and to one of the developers who contacted me) about my plots. I’ll be in touch after my seminar tomorrow to ask the team how to do it (too busy freaking out right now, so nervous). You guys are great. :D
I’m in a love-hate relationship right now with python, trying to make plots for a seminar I’m giving on Thursday look presentable (which I’ve been freaking out about for over a week now). So let’s have a pretty picture instead.
This is a iso-contour volume rendering of the 4th largest dark matter halo in one of my simulations. The coloured surfaces are all of the same density. It’s like those circles/contours you get on maps that show the height of a hill, except in 3D, in density and all shiny-like.
I also can’t tell you exactly what I’m up to for the simulation stuff anymore as I’ve just found out that at least two other groups are doing the same thing so we’re in a publication race. I’m very paranoid as I was hoping this would be my first paper.
On the off-chance, if anyone knows how to get a legend on this thing I’d be extremely grateful! It’s from the yt-project.org’s analysis package and I’ve seen volume rendering with legends on before, just can’t see a legend-like option in the documentation (camera stuff is treated different to plotCollection, unfortunately, otherwise I could do it).
The initial simulation was run with Enzo.
You may not necessarily have seen the paper “Experimental delayed-choice entanglement swapping” by Ma et al published in the journal Nature yesterday (also available on the arXiv here). It describes a quantum entanglement experiment where measurements are influenced by a decision made after they are performed.
Yes, you read that right. After. Sounds like sending a signal back in time to me!
Of course I am being facetious. I understand it, this is merely (I say merely, as though it’s run-of-the-mill occurance) another example of “spooky action at a distance”, a consequence of the nature of Quantum Mechanics. Indeed this very experiment was first proposed by John Archibald Wheeler in 1978.
I will do my best to try to explain what is going on here, though probably over-simplifying/getting it wrong in the process. In the standard double slit experiment, light is shone through two slits, creating a set of interference fringes .
(Explanatory images taken from here.)
As the intensity of light is reduced, to the point where only one photon can be passing through the system at a time, we would expect the fringes to vanish, and see two single stripes, corresponding to a photon going through one or the other of the slits. We don’t, the photon, as the photon interferes with itself (or rather the quantum wavefunction of the photon means that it goes through both slits simultaneously).
Of course, if you were then to try to determine which slit the photon had gone through (for example by covering the slit, or using some other information), the wavefunction collapses), and the interference pattern vanishes. In previous experiments, the choice of trying to measure which slit the photon passes through is made at the time, altering the quantum wavefunction of the system. In the delayed-choice experiment, the decision to make this measurement can be made after the photons have traveled through the system, which will retroactivelychange the quantum wavefunction of the system (they do this using entangled photos). This is why it looks like time travel, as the decision to measure the photons at the slit occurs after the pattern of photons is measured at the plate.
How does this not violate causality then? No actual information is being transmitted back in time, merely the probabilistic distribution of photons at the end of the experiment. But this is an unsatisfactory explanation. The truth is, Quantum Mechanics is strange, and difficult to reconcile with our day to day experience of the macroscopic universe (as exemplified by the Schrödinger’s cat thought experiment). This is just another example of the weird quantum universe we live in.
Source: parkysnewbrain
Astronomers are using infrared observations from NASA’s WISE spacecraft to identify sources seen at energies a million times greater.
(Some galaxies have feeding supermassive black hole that shoot out relativistic jets, shown here in this artistic depiction. Credit: NASA / JPL / CalTech)
“NASA’s infrared eye on the sky is helping astronomers take on a high-energy challenge. The Wide-field Infrared Survey Explorer (WISE) team recently released the spacecraft’s all-sky survey, an impressive catalog of infrared sources that includes stars, asteroids, and galaxies. But while WISE could only see in infrared, astronomers have found a way to expand its usefulness to the shortest, most energetic wavelengths around: gamma rays.”
“But recently Francesco Massaro (SLAC National Laboratory and Kavli Institute for Particle Astrophysics and Cosmology) and his colleagues figured out that galaxies with a jet pointed straight at us — called blazars — have a distinct infrared look compared with other galaxies, both active and quiet. That’s because the infrared emission from blazars is from synchrotron radiation, radiation emitted by charged particles such as electrons as they spiral through a magnetic field at relativistic speeds.”
“Massaro and his colleagues used the blazars’ unique signals to do follow-up on 313 unidentified gamma-ray sources detected by the Fermi telescope. Using WISE’s preliminary data release (which covers about half the sky), they found that 156 of them are associated with at least one blazar candidate. Their findings will appear in an upcoming Astrophysical Journal.”
(Repurposing existing space missions to do kinds of science not originally anticipated by their builders is not new — the idea goes back at least as far as the 1960’s. But finding ever-novel ways to enable new science without necessarily building and launching new spacecraft is a good way to stretch thin budgets. The WISE discovery is a great example. -JCB)
(via lonecenturion)
Source: strictlyastronomy
