Dark matter is one of the big mysteries of cosmology. Theoretically it explains cosmic phenomena such as the scale at which galaxies cluster, and observationally we see its effect through things like gravitational lensing, but it hasn’t been observed directly. This means we have a limited understanding its exact nature. As a result, there have been lots of theoretical ideas about what dark matter could be. But we now know that whatever dark matter is, it isn’t warm and fuzzy. 

There are two broad aspects about dark matter that no one disagrees about (assuming it exists). The first is that it must be dark, meaning that it doesn’t interact much with light. If it did interact with light, we would see its effects through the absorption or scattering of light from stars and distant galaxies. The second is that it must have mass, since the models require that it interacts with regular matter gravitationally. Beyond that, almost anything goes.

The most models assume that dark matter is cold. In this case, cold vs warm refers to the speed at which dark matter particles typically move. In cold dark matter models, the particles are relatively heavy, with a mass similar to that of protons or more. Because of their high mass, these dark matter particles would move relatively slowly, at much the same speed as the gas and dust in our galaxy. Neutrinos, on the other hand, are warm dark matter. Neutrinos don’t interact strongly with light, and they do have mass, so they meet the basic requirement of dark matter. But neutrino mass is minuscule, and they typically move at speeds approaching the speed of light. Thus, neutrinos are an example of warm or hot dark matter. One of the things we observe about galaxies is that they have far more mass than their visible matter would suggest, so they must contain a lot of dark matter. This means dark matter clumps together just as gas and dust clumps to form galaxies. Warm dark matter such as neutrinos move much too quickly to clump together in this way, so it would seem that dark matter must be cold. While cold dark matter is a central part of the standard “concordance model” of cosmology, it isn’t without problems. One of the biggest problems is that cold dark matter predicts that large spiral galaxies like our Milky Way should have hundreds of small satellite galaxies surrounding it. We’ve only found about a dozen satellite galaxies. Even the distribution of stars within these dwarf galaxies doesn’t fit the dark matter model very well.

While warm dark matter like neutrinos doesn’t fit the data well, there are other warm models that might. They solve some of the issues with warm dark matter by suggesting dark matter is also “fuzzy.” This refers to its quantum nature. All matter has a quantum aspect to it. For example, an electron doesn’t orbit the nucleus of an atom like a planet around the Sun. Instead, the electron is in a “fuzzy” quantum state within the atom. Normally the fuzzy nature of quantum particles only acts at short distances, on the scale of a few atoms, but under the right conditions this kind of fuzzy quantum behavior can occur over large distances. In the fuzzy dark matter model, the dark matter particles can interact quantum mechanically over great distances, thus allowing them to behave in ways similar to cold dark matter.

Several computer simulations of the universe agree with the cold dark matter model on large scales, but a new study specifically looked at how the warm fuzzy model compares. To do this the team used observations from more than 100 quasars. Quasars are distant objects powered by the supermassive black holes in the centers of galaxies. They give off tremendous amounts of light and energy, and so we can see them across billions of light years. As the light from these quasars travels across the cosmos to reach us, it is distorted by diffuse filaments of hydrogen gas between galaxies, known as the intergalactic medium. The distribution of hydrogen in the intergalactic medium allows us to study how clusters of galaxies formed. The team compared this data to both cold and warm-fuzzy dark matter models. They found the warm-fuzzy model didn’t agree with observation. That doesn’t mean that warm-fuzzy dark matter doesn’t exist, but if it does exist it must be so diffuse and have such an extraordinarily tiny mass that it couldn’t have caused the clustering of galaxies we observe.

Cold dark matter still has its own problems, and the nature of dark matter still holds many mysteries. But we now know that for the most part dark matter isn’t warm and fuzzy.

Paper: Vid Iršič, et al. First Constraints on Fuzzy Dark Matter from Lyman-α Forest Data and Hydrodynamical Simulations. Phys. Rev. Lett. 119, 031302 (2017)

The post Dark Matter Isn’t Warm And Fuzzy appeared first on One Universe at a Time.

Imagine you’re at a party and you’re having a conversation with a friend. There’s music in the background, people are talking and laughing, and out of all that noise you’re able to focus in on your friend’s voice. How are you able to do that? While the details of human perception are complex, part of the reason you can pick your friend’s voice out of the noise is that you already know what your friend sounds like. It’s a trick we also use in gravitational physics. 

One of the biggest challenges with detecting gravitational waves is pulling the signal out of the noise. Even strong gravitational waves such as those from two merging black holes are incredibly faint, and there are lots of sources of gravitational noise. So to help distinguish a real signal from the noise you need to know what a real signal might look like. That means you need to use general relativity to create models of things such as binary black holes.

Unfortunately there isn’t a simple solution to the Einstein field equations for two large masses. We need to use computer simulations, and even then there are significant challenges. To make the solutions possible, models would typically use certain tricks such as choosing a reference frame that moves with the black holes, or looking at different aspects of a model black hole merger and stitching the solutions together. But in the mid 2000s computational physicists started to develop models that didn’t require these kinds of tricks. Some of my RIT colleagues, for example, created a model of two black holes starting at their closest stable orbit and spiraling inward to create the final merged black hole. Computer models such as this finally allowed us to simulate the gravitational waves from black holes in an accurate way.

The smoothed waveform of the detected gravitational waves compared with the waveform predicted by theoretical models. Credit: LIGO Collaborative

As the power of computer clusters grew, we were able to run simulations like this for a wide variety of situations, for black holes with different masses and different rates of rotation. This way we could build up a table of possible mergers, similar to the way we have an archive of how our various friends and family sound. So when LIGO detected a spike of gravitational waves that looked like a merger, they could compare the signal to a range of simulations. They were able to match the observed waveform to several similar theoretical mergers, which not only confirmed it was indeed a black hole merger, but could also determine characteristics of the black holes. We know, for example that the two black holes had masses of 36 and 29 solar masses, give or take about 4 solar masses. We also know the final black hole is about 62 solar masses (give or take about 4 solar masses) and that it rotates at a rate of about 67% of the theoretical maximum.

Without computer simulations of black hole mergers LIGO wouldn’t have been able determine the properties of the merging black holes. By using computer models hand in hand with observational data, we’ve been able to enter a new age of gravitational astronomy.

Paper: M. Campanelli, et al. Accurate Evolutions of Orbiting Black-Hole Binaries without Excision. Phys. Rev. Lett. 96, 111101 (2006)

The post How Computer Models Helped Discover Gravitational Waves appeared first on One Universe at a Time.

Now that gravitational waves have been directly observed, the next big goal in gravity physics is to better observe black holes. While there is lots of evidence showing black holes exist, we’d really like to observe them more directly.

The Event Horizon Telescope hopes to see the region around a black hole directly. Since the black hole itself doesn’t emit any light, what we hope to observe is the hot material close to the black hole. Because the closest material is relatively near the event horizon of a black hole, the light we see would be greatly distorted by gravity, causing its appearance to warp in shape and color. The science fiction movie Interstellar, for example, used computer simulations to create a simulated black hole showing some of these effects. But the black hole in Interstellar is an enhanced Hollywood version, and not quite what we’d actually observe.

A real black hole would look somewhat less dramatic, as seen in this video. Produced in part by some of my colleagues at RIT, the video shows the hard and soft x-rays emitted by the corona and accretion disk of a black hole, including the gravitational warping and Doppler shift caused by relativity. It gives us a good idea of what we expect to see near a black hole.

The post Edge Effect appeared first on One Universe at a Time.

If you want to keep information hidden, you’ll probably want to encrypt it. We do this all the time for things like credit card transactions, the data on your phone, and even this website. Encryption is a way to ensure that only the the intended recipient can get access to your information. That is, unless someone is able to crack the code.

One of the more common methods of encryption is known as public key encryption, where a large random number is entered into a key generator algorithm to create a pair of public and private keys. The public key can be used to encrypt a message which can only be decrypted with the private key. As long as the private key is kept private, this works pretty well. One one catch is that you need a large random number, and ideally it needs to be truly random. If someone could predict your random number, they could generate the same public and private key, and you’re out of luck.

But often “random” numbers are only pseudo-random. They look like random numbers, but use a particular algorithm to simulate randomness. To get better random numbers, you can use thermal fluctuations in your computer, or noise in weather data. Or, as in the case of a new paper, data from the cosmic microwave background. It might seem like the CMB is a really bad choice. After all, it can be seen by everyone, so if you use CMB data to create a random number why can’t someone else get the same number? But it turns out that’s not a problem.

The basic idea is to take a patch of sky and measure the distribution of energy from the CMB, specifically what’s known as the power spectrum. That spectrum is then compared to the theoretical ideal, and the difference creates a random number. Even if someone measured exactly the same patch of sky, they wouldn’t get the exact same result, and therefore wouldn’t get the same number. While the authors use the CMB as an example, they point out a similar method could be used to generate random numbers from the 21 centimeter line, supernova remnants, radio galaxies and other astrophysical phenomena. All you need is a basic radio telescope, and you have a random number generator.

It’s not likely that this astrophysical method is any better than what we use now. Thermal variations and weather patterns are pretty random as it is. But it’s an interesting idea to use the secrets of the universe to keep your own secrets.

Paper: Jeffrey S. Lee and Gerald B. Cleaver. The Cosmic Microwave Background Radiation Power Spectrum as a Random Bit Generator for Symmetric and Asymmetric-Key Cryptography.  arXiv:1511.02511 [cs.CR] (2015)

The post Cosmic Cryptology appeared first on One Universe at a Time.

When two black holes merge, the orientation of their axis of rotation can change abruptly. This is known as a spin flip, and typically happens when the orbital plane of two black holes is different from the rotational axes of the black holes. The total rotation (angular momentum) of the orbiting black holes is constant, so when they merge, the orientation of the merged black hole must be in the direction of the total. While we’ve known this for some time, it’s generally been thought that before the merger the rotation of the two black holes is fairly constant. Now new research finds this isn’t the case.

Spin flip when black holes merge. Credit: Wikipedia

The results are published in Physical Review Letters, and are based upon detailed computer simulations of merging black holes. While we know the equations of general relativity extremely well, the complexity of these equations means that even a general two-body solution can’t be solved exactly, unlike Newtonian gravity. While there are ways to find approximate solutions, detailed studies require solving Einstein’s equations numerically. With the rise of powerful supercomputer clusters and advanced software, we’ve finally reached the point were very detailed solutions can be obtained.

The shift of spin orientations over time.
Credit: Lousto and Healy

In this case, the team used the Blue Sky linux cluster at my university (RIT) to analyze the spin orientation of two orbiting black holes as they are close to merging. What they found was that the spins of the two black holes interact gravitationally (what is known as spin-spin coupling) and as a result they can transfer angular momentum between each other. This means that the orientations of their spins change over time.

This has important consequences for observations of merging black holes. We can determine the rotational orientations of black holes by the jets that stream from their poles, so one way to look for mergers is to look for an abrupt shift in the direction of a jet. What this work shows is that there can be gradual shifts leading up to a merger, so there isn’t necessarily a sharp shift in the jet.

Paper: Carlos O. Lousto and James Healy. Flip-Flopping Binary Black Holes. PRL 114, 141101 (2015)

The post Spin Flip appeared first on One Universe at a Time.

This month I’ve upgraded my home computer. My new desktop has a faster processor, double the storage space, and quadruple the RAM as my venerable old laptop. I don’t upgrade very often, so when it happens there’s a very noticeable uptick in computing power. It’s something we’ve become rather accustomed to. With each new phone, computer or tablet we have more power at our fingertips. This consequence of Moore’s law has also revolutionized the way we do astronomy.

The silicon revolution is what allowed deep space probes to exist. A round trip signal to the Moon takes about 2 seconds, but a round trip signal to Jupiter takes half an hour or more, which is simply too long to control a spacecraft in real time. Spacecraft need to be partly autonomous, and they need to be able to store data for later transmission back to Earth.

The Voyager spacecraft of the 1970s had about 32 kilobytes of storage, which is less than a singing birthday card these days.  By the 1990s, Pathfinder orbited Mars with about 64 megabytes of storage. Now New Horizons races toward Pluto with 8 gigabytes of memory. This kind of storage is absolutely necessary for New Horizons, since it will fly by Pluto so quickly that all of its stored data will have to be stored until it can slowly be radioed back to Earth from the edge of our solar system.

It took megabytes of data to produce this image. Credit: NASA

Processing power has also grown tremendously over the years, which has allowed us to analyze more data. The in the early 1990s, the COBE satellite pushed the envelope of astronomical data gathering when it collected about 46 megabytes of data per day. Over its lifetime COBE gathered nearly 300 gigabytes of data, necessary to measure the small fluctuation of the cosmic microwave background with precision. In contrast, the Planck satellite gathered data on the order of terabytes.

As storage size and computing power continue to rise, so will demands for more data and deeper analysis. The universe is a very big place, and there’s lots of things to study.

The post More Power appeared first on One Universe at a Time.

The Antikythera mechanism is a strange astronomical calculator. It was discovered in a shipwreck off the Greek island of Antikythera in 1900, and is astoundingly complex. It was a bronze clockwork device with at least 30 gears, and looks like something from the 1400s. But recent research indicates that it likely dates earlier than 200 B.C.

Because of its anachronistic nature, it’s often associated with ancient aliens or the lost city of Atlantis, but the device itself doesn’t calculate anything ancient astronomers didn’t already know. By that time observational astronomy was quite sophisticated. The manufacture of the device also doesn’t utilize any construction methods that weren’t known at the time. What’s astounding is that this astronomical knowledge and manufacturing was combined to create a precision portable calculator.

Simulated reconstruction of the device.

Although found on a Greek ship and having Greek engravings, the layout of the device follows Babylonian astronomy. The device calculated the Egyptian solar calendar, as well as the Metonic lunar calendar. It predicted solar eclipses, and even calculated the timing of the Olympic games. It even calculated the positions of the Sun, Moon and planets along the ecliptic. The planetary positions are not particularly accurate, since they use the Greek model of perfectly circular orbits. Still, it is an astounding demonstration of human ingenuity.

Given its sophistication, there are likely even earlier versions that were constructed, though we have never found anything else like it. How such a calculating device was developed, and when that knowledge was lost, remains a mystery.

Paper: Christián C. Carman & James Evans. On the epoch of the Antikythera mechanism and its eclipse predictor. Archive for History of Exact Sciences
Volume 68, Issue 6 , pp 693-774 (2014)

The post Clockwork Twin appeared first on One Universe at a Time.

One of the common ways we can map the distribution of matter in a galaxy is by observing the light emitted neutral hydrogen. This works pretty well because hydrogen is the most abundant element in the universe, and its emission lines are pretty distinctive. But for distant galaxies hydrogen emissions aren’t very bright. To observe them you need really long exposure times, and that limits the amount of galaxies you can observe. One alternative is to look at the emissions of carbon instead.

Carbon isn’t nearly as common as hydrogen, but its emission lines are brighter, particularly for distant galaxies where redshift is a factor. By mapping the distribution of carbon we can get an idea of the distribution of hydrogen. Of course this relies upon certain assumptions. For example, it’s generally thought that carbon and hydrogen are evenly mixed in a galaxy, so if you find lots of carbon there should also be lots of hydrogen.

Now a new paper introduces a method that greatly increases the precision of this method. The method uses computational simulations of galaxies and compares them to the distribution of carbon. In the the paper, the authors use a simulated observation of carbon emissions from the ALMA radio telescope array, and then ran hydrodynamic simulations to determine the distribution of hydrogen. They found that 80% of hydrogen in a galaxy could be mapped through carbon observations with significantly shorter exposure times.

The paper demonstrates that by combining observations and simulations we can probe young galaxies in more detail. This is particularly useful in studying galactic evolution. Now we’ll have to see how it works in the real world.

Paper: M. Tomassetti, et al. Atomic carbon as a powerful tracer of molecular gas in the high-redshift Universe: perspectives for ALMA. MNRAS Letters; doi: 10/193/mnras/slu137 (2014)

The post Carbon Chain appeared first on One Universe at a Time.

How do you weigh a galaxy?

With planets we can measure their distance from the Sun and their orbital speed. By observing their motion in detail we can calculate their mass very precisely. For binary stars we can use a similar method. Observe the size of their mutual orbits and their orbital period, and by Kepler’s laws you can determine their mass. We can use the same method to calculate the mass of the supermassive black hole in the center of our galaxy.

But all of these methods rely on observing the change of an object’s speed over time (its acceleration) either directly or indirectly. For galaxies you can’t really do that. Galaxies aren’t a single object moving in a simple way, but rather a complex system of stars, gas and dust all moving and interacting.

One way that we can calculate the mass of a galaxy is to observe the motion of particular stars in the galaxy. Their accelerations are too small to observe, but by looking at how the speeds of stars closer to the center compare to speeds further from the center we can get an idea of a galaxy’s mass. But because much of a galaxy’s mass is due to dark matter, it is difficult to determine the total mass of a galaxy. We can infer the distribution of dark matter in regions where there are stars, but how do we determine how far beyond the stars the dark matter extends (known as the dark matter halo).

In a recent paper in the Astrophysical Journal, a team looked at a computational approach to determining the mass of galaxies, in particular the masses of the Milky Way and the Andromeda galaxy. Their method was to look at the gravitational attraction between the two galaxies.

While the Milky Way and Andromeda are gravitationally attracting each other, that attraction isn’t very large. Sure, they both have billions of stars worth of mass, but they are also more than 2 million light years apart, and gravitational attraction is weaker at larger distances. So there isn’t a way to measure the acceleration due to gravity. We do, however, have a measure of the speeds of the galaxies relative to each other, and it is this data that the team analyzed.

Credit: Phelps, et al.

Basically, the team ran a computer simulation of the two galaxies, along with other members of the local group, using a method known as the numerical action method. This method assumes a mass for both galaxies, then calculates their velocities due to gravity following the principle of least action (which makes the velocities easier to calculate). By calculating the galactic velocities for a range of masses, you can compare the result with the actual observed motion. The better the statistical match, the more likely your assumed masses are the actual masses.

You can see the computational results in the figure above. Each of the four images starts with different data from the local group, and the more blue regions are closer matches to observation. By combining these different results, the best match is a mass of 2.5 trillion solar masses for the Milky Way, and 3.5 trillion solar masses for Andromeda (give or take a trillion solar masses with 95% confidence).

So it turns out we can weigh a galaxy, it just takes some computational physics to do it.

The post Galactic Scale appeared first on One Universe at a Time.

As I’ve written about before, the existence of dark matter is well supported by observational evidence. There isn’t much debate in the astronomical community on the existence of dark matter and the fact that it makes up a large part of the mass of galaxies. We’d still like to have a direct observation of dark matter to be certain sure, but there is general consensus on dark matter.

This doesn’t mean there is uniform agreement on the nitty-gritty details. One of these details concerns the distribution of dark matter within a galaxy. Since we can’t observe dark matter in a galaxy directly, we must secondary effects such as the gravitational lensing of background light or the speeds of stars within their galaxy. Through these types of observations we can see the gravitational effect of dark matter.

If the gravitational effects were entirely due to dark matter, then it would be a straightforward matter to measure the distribution of dark matter in a galaxy. But the gravitational effects are due to the mass distribution of both dark matter and regular matter. This means we must subtract out the gravitational effects of regular matter to study the effects of dark matter. This can be done, but it takes much more effort, and it also depends on the assumptions you have about dark matter. In astrophysics we say the results are model dependent.

Just to be clear, this does not mean we’re doing something unscientific like assuming dark matter is true to prove that dark matter is true (circular reasoning works because circular reasoning works), but rather we have clear evidence of dark matter and are trying to match that evidence to a particular model of dark matter.

An analogy would be if we observed skid marks of a car, but had only directly observed bikes making skid marks. Based on the width and length of the skid mark, it is clear it was not caused by a bike. The skid mark strongly supports the existence of a car, but to calculate the speed of a car when it started to skid we have to make an assumption about the car’s mass. It could have been a light car going very fast, or a heavy car going much slower. Our estimation of the car’s speed is therefore model dependant.

There are several types of dark matter that have been proposed. The most popular is known as cold dark matter (CDM), which currently seems to best agree with observation on very large scales. It is also the simplest model in many ways. Alternatives include warm dark matter, which is often invoked by models trying to integrate dark matter with alternative gravity models (since current alternative gravity models alone don’t agree with the large-scale structure of the universe). Then there is hot dark matter (which includes neutrinos), although observational evidence excludes hot dark matter from being a major contributor of dark matter in galaxies. It could also be a mixture of different types, and without direct observational evidence we can’t be sure.

Computer simulations of hot, warm and cold dark matter. Credit: University of Zurich

In 1995, Navarro, Frenk and White ran computer simulations of galaxies containing cold dark matter, and found that over time the dark matter tended to fall into a distribution of mass now known as the NFW profile. This model works very well on the scale of clusters of galaxies, and makes an excellent match to the large scale cosmic structure, but on individual galaxies it has a few problems. For example, simulations of the model predict more satellite galaxies around large galaxies than we actually observe. It also doesn’t match the motion of stars in smaller galaxies as well as we would like.

An alternative model develop by Burkert assumes that dark matter can reach a kind of thermal equilibrium. This gives rise to a slightly different dark matter distribution known as the Burkert profile, which matches small galaxies much better. There are other models as well, such as the Einasto profile that seems to work well.

With so many models, it might seem like we really don’t know much at all about dark matter, but this is not the case. In fact, a recent article in Astronomy and Astrophysics compared several profile models as applied to the data from four dwarf galaxies, and found that the models agreed very well.

All of these different profiles make certain assumptions about dark matter. By determining which profile has the best agreement with observation, we gain clues on the nature of dark matter.

At least until we can make direct observations of dark matter particles.

The post Profile Matching appeared first on One Universe at a Time.

One of the biggest advances of astronomy in the past decade has been the discovery of planets orbiting other stars, known as exoplanets. But just how many exoplanets have been discovered? According to the Extrasolar Planet Encyclopedia, a semi-official catalog based in Europe, there were as of the end of September last year 990 confirmed exoplanets and 2,321 candidate exoplanets. The discrepancy between these numbers has to do with the limitations of the observational data we currently have.

As an example, consider the star system known as Gliese 581. Gliese 581 has 3 confirmed planets, but likely has at least 4 or 6 worlds. The answer depends on how you model the data. The data for 581 comes from the High Accuracy Radial velocity Planet Searcher (HARPS), which measures the Doppler shift of the star to look for planets.

As planets orbit their star, the gravity of the planet tugs a bit at the star, causing the star to wobble a bit. The amount and rate of that wobble can tell us about the mass and orbit of the planet. We can measure the wobble of a star by measuring how the light of the star shifts slightly in color. As the star moves toward us, its light is shifted slightly to the blue, and as it moves away from us it is shifted slightly to the red. This is known as the Doppler shift, and it is similar to the way sound from a train or car can seem higher or lower in pitch if it is moving toward or away from us.

The difficulty of using the Doppler shift to measure the wobble of a star is two-fold. First, the amount of wobble a star has due to a planet is very small, so the Doppler shifts are small and hard to measure. Second, the surface of the star is not calm, so things like granules and stellar flares can cause their own Doppler shifts. This means the data has a great deal of noise in it.

The way to find the planets is then to take lots of data, and fit this data to possible planetary orbits. When you do this you can get lots of possible fits. Some fits are better than others, but some fits can conflict with each other. You aren’t likely, for example, to find two large planets in orbits that very close to each other because their gravitational interactions would make them unstable.

So to narrow the field of candidate planets, you can run computer simulations of different orbits to see if they are gravitationally stable. Given that Gliese 581 is about 7 – 10 billion years old, any planets that exist should be in orbits stable over billions of years. Using computational models you can then exclude any orbits that are not highly stable.

From the observational data of HARPS and dynamic modeling, there are three planets that are considered confirmed. These are the three innermost planets, with stable and fairly circular orbits. In computer models where the inner planets are allowed to have slightly non-circular orbits, a fourth outer planet matches data fairly well. This one has a relatively elliptical orbit about three times further out than the inner three. Because this planet is further out, its wobble effect on the star is smaller and it is harder to pick out of the observational noise. As a result, this one is considered a candidate planet.

There is an alternative model that assumes all the planetary orbits are circular. In the model they assumed the orbits were exactly circular, which isn’t at all likely, but makes the modelling easier. Fitting this model to the data there could be three outer planets, raising the total to six planets. This would mean there are 3 confirmed and 3 candidate planets in Gliese 581.

So how many planets does this system have? At least 3, likely 4, perhaps (but not likely) 6. More observational data, and more dynamical analysis will resolve things in the end. This fuzziness of numbers exists in lots of other systems, hence the difference between confirmed and candidate planets.

But when you think about it, being able to discover 990 confirmed exoplanets is quite amazing when you consider that a generation ago we had no confirmed planets beyond our solar system.

The post Countless Worlds appeared first on One Universe at a Time.

Dark matter is an aspect of the universe we still don’t fully understand. We have lots of evidence pointing to its existence (as I outlined in a series of posts a while back), and the best evidence we have points toward a specific type of matter known as cold dark matter (CDM). One big downside is that we have yet to find any direct detection of dark matter particles. In fact, many of the likely candidates for dark matter have been all but eliminated. Another is that cold dark matter doesn’t agree with our observations of dwarf galaxies. Now a new paper presents a solution to the second problem that might even help with the first.

The main problem with dwarf galaxies is that there are fewer of them around spiral galaxies than dark matter predicts. When we do dark matter computer simulations, the results always have more dwarf galaxies than we observe. This has been taken to mean that either the simulations are somehow flawed, or dark matter isn’t the complete solution we’ve thought. This new work looks at a modified version of dark matter, and how it effects these kinds of computer simulations.

Credit: Durham University

Normally, it is assumed that dark matter doesn’t interact with light directly at all. This means we can see its gravitational effects, but we don’t see anything such as absorption lines and the like, which we observe with regular matter. The reason for this is that dark matter makes up the majority of matter in the universe. About 90% of the mass in our own Milky Way consists of dark matter. If it interacted much with light, then we would have seen its effects on light by now. This new work proposes that dark matter does interact with light, but only very, very slightly.

Now you might think that if dark matter interacts so slightly with light that we don’t see its effect, then it certainly can’t differ that much from standard dark matter, but the team showed that this very small effect can build up over time, so that modern galaxies have fewer dwarf satellites, just as we observe. You can see this in the image above. The top left image is standard dark matter model, with too many satellite dwarf galaxies. The top right is a warm dark matter model that solves the dwarf galaxy problem but doesn’t agree with other observations. The bottom left is this new, light interacting dark matter model, and the bottom right is what happens when you make the light interaction too strong and get no dwarf galaxies.

So by modifying dark matter to include slight interactions with light, the predictions match dwarf galaxy observations. It should be noted that just because this modification works, that doesn’t mean it is the solution. Tweak theories are weak theories, as I’ve said before. This type of dark matter could also affect other things such as large scale structure, and this would need to be studied before we could be confident about this particular model. But the work does show that dark matter models can address some of the known problems with dark matter.

Paper: C. Bœhm, et al. Using the Milky Way satellites to study interactions between cold dark matter and radiation. MNRAS 445 (1): L31-L35 (2014).

The post Modified Dark Matter appeared first on One Universe at a Time.