Month: June 2018

Prospects for observing and localizing gravitational-wave transients with Advanced LIGO, Advanced Virgo and KAGRA

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This paper, known as the Observing Scenarios Document with the Collaboration, outlines the observing plans of the ground-based detectors over the coming decade. If you want to search for electromagnetic or neutrino signals from our gravitational-wave sources, this is the paper for you. It is a living review—a document that is continuously updated.

This is the second published version, the big changes since the last version are

  1. We have now detected gravitational waves
  2. We have observed our first gravitational wave with a mulitmessenger counterpart [bonus note]
  3. We now include KAGRA, along with LIGO and Virgo

As you might imagine, these are quite significant updates! The first showed that we can do gravitational-wave astronomy. The second showed that we can do exactly the science this paper is about. The third makes this the first joint publication of the LIGO Scientific, Virgo and KAGRA Collaborations—hopefully the first of many to come.

I lead both this and the previous version. In my blog on the previous version, I explained how I got involved, and the long road that a collaboration must follow to get published. In this post, I’ll give an overview of the key details from the new version together with some behind-the-scenes background (working as part of a large scientific collaboration allows you to do amazing science, but it can also be exhausting). If you’d like a digest of this paper’s science, check out the LIGO science summary.

Commissioning and observing phases

The first section of the paper outlines the progression of detector sensitivities. The instruments are incredibly sensitive—we’ve never made machines to make these types of measurements before, so it takes a lot of work to get them to run smoothly. We can’t just switch them on and have them work at design sensitivity [bonus note].

Possible advanced detector sensitivity

Target evolution of the Advanced LIGO and Advanced Virgo detectors with time. The lower the sensitivity curve, the further away we can detect sources. The distances quoted are binary neutron star (BNS) ranges, the average distance we could detect a binary neutron star system. The BNS-optimized curve is a proposal to tweak the detectors for finding BNSs. Figure 1 of the Observing Scenarios Document.

The plots above show the planned progression of the different detectors. We had to get these agreed before we could write the later parts of the paper because the sensitivity of the detectors determines how many sources we will see and how well we will be able to localize them. I had anticipated that KAGRA would be the most challenging here, as we had not previously put together this sequence of curves. However, this was not the case, instead it was Virgo which was tricky. They had a problem with the silica fibres which suspended their mirrors (they snapped, which is definitely not what you want). The silica fibres were replaced with steel ones, but it wasn’t immediately clear what sensitivity they’d achieve and when. The final word was they’d observe in August 2017 and that their projections were unchanged. I was sceptical, but they did pull it out of the bag! We had our first clear three-detector observation of a gravitational wave 14 August 2017. Bravo Virgo!

LIGO, Virgo and KAGRA observing runs

Plausible time line of observing runs with Advanced LIGO (Hanford and Livingston), advanced Virgo and KAGRA. It is too early to give a timeline for LIGO India. The numbers above the bars give binary neutron star ranges (italic for achieved, roman for target); the colours match those in the plot above. Currently our third observing run (O3) looks like it will start in early 2019; KAGRA might join with an early sensitivity run at the end of it. Figure 2 of the Observing Scenarios Document.

Searches for gravitational-wave transients

The second section explain our data analysis techniques: how we find signals in the data, how we work out probable source locations, and how we communicate these results with the broader astronomical community—from the start of our third observing run (O3), information will be shared publicly!

The information in this section hasn’t changed much [bonus note]. There is a nice collection of references on the follow-up of different events, including GW170817 (I’d recommend my blog for more on the electromagnetic story). The main update I wanted to include was information on the detection of our first gravitational waves. It turned out to be more difficult than I imagined to come up with a plot which showed results from the five different search algorithms (two which used templates, and three which did not) which found GW150914, and harder still to make a plot which everyone liked. This plot become somewhat infamous for the amount of discussion it generated. I think we ended up with something which was a good compromise and clearly shows our detections sticking out above the background of noise.

CBC and burst search results

Offline transient search results from our first observing run (O1). The plot shows the number of events found verses false alarm rate: if there were no gravitational waves we would expect the points to follow the dashed line. The left panel shows the results of the templated search for compact binary coalescences (binary black holes, binary neutron stars and neutron star–black hole binaries), the right panel shows the unmodelled burst search. GW150914, GW151226 and LVT151012 are found by the templated search; GW150914 is also seen in the burst search. Arrows indicate bounds on the significance. Figure 3 of the Observing Scenarios Document.

Observing scenarios

The third section brings everything together and looks at what the prospects are for (gravitational-wave) multimessenger astronomy during each observing run. It’s really all about the big table.

Ranges, binary neutron star detections, and localization precesion

Summary of different observing scenarios with the advanced detectors. We assume a 70–75% duty factor for each instrument (including Virgo for the second scenario’s sky localization, even though it only joined our second observing run for the final month). Table 3 from the Observing Scenarios Document.

I think there are three really awesome take-aways from this

  1. Actual binary neutron stars detected = 1. We did it!
  2. Using the rates inferred using our observations so far (including GW170817), once we have the full five detector network of LIGO-Hanford, LIGO-Livingston, Virgo, KAGRA and LIGO-India, we could be detected 11–180 binary neutron stars a year. That something like between one a month to one every other day! I’m kind of scared…
  3. With the five detector network the sky localization is really good. The median localization is about 9–12 square degrees, about the area the LSST could cover in a single pointing! This really shows the benefit of adding more detector to the network. The improvement comes not because a source is much better localized with five detectors than four, but because when you have five detectors you almost always have at least three detectors(the number needed to get a good triangulation) online at any moment, so you get a nice localization for pretty much everything.

In summary, the prospects for observing and localizing gravitational-wave transients are pretty great. If you are an astronomer, make the most of the quiet before O3 begins next year.

arXiv: 1304.0670 [gr-qc]
Journal: Living Reviews In Relativity; 21:3(57); 2018
Science summary: A Bright today and brighter tomorrow: Prospects for gravitational-wave astronomy With Advanced LIGO, Advanced Virgo, and KAGRA
Prospects for the next update: After two updates, I’ve stepped down from preparing the next one. Wooh!

Bonus notes

GW170817 announcement

The announcement of our first multimessenger detection came between us submitting this update and us getting referee reports. We wanted an updated version of this paper, with the current details of our observing plans, to be available for our astronomer partners to be able to cite when writing their papers on GW170817.

Predictably, when the referee reports came back, we were told we really should include reference to GW170817. This type of discovery is exactly what this paper is about! There was avalanche of results surrounding GW170817, so I had to read through a lot of papers. The reference list swelled from 8 to 13 pages, but this effort was handy for my blog writing. After including all these new results, it really felt like this was version 2.5 of the Observing Scenarios, rather than version 2.

Design sensitivity

We use the term design sensitivity to indicate the performance the current detectors were designed to achieve. They are the targets we aim to achieve with Advanced LIGO, Advance Virgo and KAGRA. One thing I’ve had to try to train myself not to say is that design sensitivity is the final sensitivity of our detectors. Teams are currently working on plans for how we can upgrade our detectors beyond design sensitivity. Reaching design sensitivity will not be the end of our journey.

Binary black holes vs binary neutron stars

Our first gravitational-wave detections were from binary black holes. Therefore, when we were starting on this update there was a push to switch from focusing on binary neutron stars to binary black holes. I resisted on this, partially because I’m lazy, but mostly because I still thought that binary neutron stars were our best bet for multimessenger astronomy. This worked out nicely.

Accuracy of inference on the physics of binary evolution from gravitational-wave observations

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Gravitational-wave astronomy lets us observing binary black holes. These systems, being made up of two black holes, are pretty difficult to study by any other means. It has long been argued that with this new information we can unravel the mysteries of stellar evolution. Just as a palaeontologist can discover how long-dead animals lived from their bones, we can discover how massive stars lived by studying their black hole remnants. In this paper, we quantify how much we can really learn from this black hole palaeontology—after 1000 detections, we should pin down some of the most uncertain parameters in binary evolution to a few percent precision.

Life as a binary

There are many proposed ways of making a binary black hole. The current leading contender is isolated binary evolution: start with a binary star system (most stars are in binaries or higher multiples, our lonesome Sun is a little unusual), and let the stars evolve together. Only a fraction will end with black holes close enough to merge within the age of the Universe, but these would be the sources of the signals we see with LIGO and Virgo. We consider this isolated binary scenario in this work [bonus note].

Now, you might think that with stars being so fundamentally important to astronomy, and with binary stars being so common, we’d have the evolution of binaries figured out by now. It turns out it’s actually pretty messy, so there’s lots of work to do. We consider constraining four parameters which describe the bits of binary physics which we are currently most uncertain of:

  • Black hole natal kicks—the push black holes receive when they are born in supernova explosions. We now the neutron stars get kicks, but we’re less certain for black holes [bonus note].
  • Common envelope efficiency—one of the most intricate bits of physics about binaries is how mass is transferred between stars. As they start exhausting their nuclear fuel they puff up, so material from the outer envelope of one star may be stripped onto the other. In the most extreme cases, a common envelope may form, where so much mass is piled onto the companion, that both stars live in a single fluffy envelope. Orbiting inside the envelope helps drag the two stars closer together, bringing them closer to merging. The efficiency determines how quickly the envelope becomes unbound, ending this phase.
  • Mass loss rates during the Wolf–Rayet (not to be confused with Wolf 359) and luminous blue variable phases–stars lose mass through out their lives, but we’re not sure how much. For stars like our Sun, mass loss is low, there is enough to gives us the aurora, but it doesn’t affect the Sun much. For bigger and hotter stars, mass loss can be significant. We consider two evolutionary phases of massive stars where mass loss is high, and currently poorly known. Mass could be lost in clumps, rather than a smooth stream, making it difficult to measure or simulate.

We use parameters describing potential variations in these properties are ingredients to the COMPAS population synthesis code. This rapidly (albeit approximately) evolves a population of stellar binaries to calculate which will produce merging binary black holes.

The question is now which parameters affect our gravitational-wave measurements, and how accurately we can measure those which do?

Merger rate with redshift and chirp mass

Binary black hole merger rate at three different redshifts z as calculated by COMPAS. We show the rate in 30 different chirp mass bins for our default population parameters. The caption gives the total rate for all masses. Figure 2 of Barrett et al. (2018)

Gravitational-wave observations

For our deductions, we use two pieces of information we will get from LIGO and Virgo observations: the total number of detections, and the distributions of chirp masses. The chirp mass is a combination of the two black hole masses that is often well measured—it is the most important quantity for controlling the inspiral, so it is well measured for low mass binaries which have a long inspiral, but is less well measured for higher mass systems. In reality we’ll have much more information, so these results should be the minimum we can actually do.

We consider the population after 1000 detections. That sounds like a lot, but we should have collected this many detections after just 2 or 3 years observing at design sensitivity. Our default COMPAS model predicts 484 detections per year of observing time! Honestly, I’m a little scared about having this many signals…

For a set of population parameters (black hole natal kick, common envelope efficiency, luminous blue variable mass loss and Wolf–Rayet mass loss), COMPAS predicts the number of detections and the fraction of detections as a function of chirp mass. Using these, we can work out the probability of getting the observed number of detections and fraction of detections within different chirp mass ranges. This is the likelihood function: if a given model is correct we are more likely to get results similar to its predictions than further away, although we expect their to be some scatter.

If you like equations, the from of our likelihood is explained in this bonus note. If you don’t like equations, there’s one lurking in the paragraph below. Just remember, that it can’t see you if you don’t move. It’s OK to skip the equation.

To determine how sensitive we are to each of the population parameters, we see how the likelihood changes as we vary these. The more the likelihood changes, the easier it should be to measure that parameter. We wrap this up in terms of the Fisher information matrix. This is defined as

\displaystyle F_{ij} = -\left\langle\frac{\partial^2\ln \mathcal{L}(\mathcal{D}|\left\{\lambda\right\})}{\partial \lambda_i \partial\lambda_j}\right\rangle,

where \mathcal{L}(\mathcal{D}|\left\{\lambda\right\}) is the likelihood for data \mathcal{D} (the number of observations and their chirp mass distribution in our case), \left\{\lambda\right\} are our parameters (natal kick, etc.), and the angular brackets indicate the average over the population parameters. In statistics terminology, this is the variance of the score, which I think sounds cool. The Fisher information matrix nicely quantifies how much information we can lean about the parameters, including the correlations between them (so we can explore degeneracies). The inverse of the Fisher information matrix gives a lower bound on the covariance matrix (the multidemensional generalisation of the variance in a normal distribution) for the parameters \left\{\lambda\right\}. In the limit of a large number of detections, we can use the Fisher information matrix to estimate the accuracy to which we measure the parameters [bonus note].

We simulated several populations of binary black hole signals, and then calculate measurement uncertainties for our four population uncertainties to see what we could learn from these measurements.

Results

Using just the rate information, we find that we can constrain a combination of the common envelope efficiency and the Wolf–Rayet mass loss rate. Increasing the common envelope efficiency ends the common envelope phase earlier, leaving the binary further apart. Wider binaries take longer to merge, so this reduces the merger rate. Similarly, increasing the Wolf–Rayet mass loss rate leads to wider binaries and smaller black holes, which take longer to merge through gravitational-wave emission. Since the two parameters have similar effects, they are anticorrelated. We can increase one and still get the same number of detections if we decrease the other. There’s a hint of a similar correlation between the common envelope efficiency and the luminous blue variable mass loss rate too, but it’s not quite significant enough for us to be certain it’s there.

Correaltions between population parameters

Fisher information matrix estimates for fractional measurement precision of the four population parameters: the black hole natal kick \sigma_\mathrm{kick}, the common envelope efficiency \alpha_\mathrm{CE}, the Wolf–Rayet mass loss rate f_\mathrm{WR}, and the luminous blue variable mass loss rate f_\mathrm{LBV}. There is an anticorrealtion between f_\mathrm{WR} and \alpha_\mathrm{CE}, and hints at a similar anticorrelation between f_|mathrm{LBV} and \alpha_\mathrm{CE}. We show 1500 different realisations of the binary population to give an idea of scatter. Figure 6 of Barrett et al. (2018)

Adding in the chirp mass distribution gives us more information, and improves our measurement accuracies. The fraction uncertainties are about 2% for the two mass loss rates and the common envelope efficiency, and about 5% for the black hole natal kick. We’re less sensitive to the natal kick because the most massive black holes don’t receive a kick, and so are unaffected by the kick distribution [bonus note]. In any case, these measurements are exciting! With this type of precision, we’ll really be able to learn something about the details of binary evolution.

Standard deviation of measurements of population parameters

Measurement precision for the four population parameters after 1000 detections. We quantify the precision with the standard deviation estimated from the Fisher inforamtion matrix. We show results from 1500 realisations of the population to give an idea of scatter. Figure 5 of Barrett et al. (2018)

The accuracy of our measurements will improve (on average) with the square root of the number of gravitational-wave detections. So we can expect 1% measurements after about 4000 observations. However, we might be able to get even more improvement by combining constraints from other types of observation. Combining different types of observation can help break degeneracies. I’m looking forward to building a concordance model of binary evolution, and figuring out exactly how massive stars live their lives.

arXiv: 1711.06287 [astro-ph.HE]
Journal: Monthly Notices of the Royal Astronomical Society; 477(4):4685–4695; 2018
Favourite dinosaur: Professor Science

Bonus notes

Channel selection

In practise, we will need to worry about how binary black holes are formed, via isolated evolution or otherwise, before inferring the parameters describing binary evolution. This makes the problem more complicated. Some parameters, like mass loss rates or black hole natal kicks, might be common across multiple channels, while others are not. There are a number of ways we might be able to tell different formation mechanisms apart, such as by using spin measurements.

Kick distribution

We model the supernova kicks v_\mathrm{kick} as following a Maxwell–Boltzmann distribution,

\displaystyle p(v_\mathrm{kick}) = \sqrt{\frac{2}{\pi}}  \frac{v_\mathrm{kick}^2}{\sigma_\mathrm{kick}^3} \exp\left(\frac{-v_\mathrm{kick}^2}{2\sigma_\mathrm{kick}^2}\right),

where \sigma_\mathrm{kick} is the unknown population parameter. The natal kick received by the black hole v^*_\mathrm{kick} is not the same as this, however, as we assume some of the material ejected by the supernova falls back, reducing the over kick. The final natal kick is

v^*_\mathrm{kick} = (1-f_\mathrm{fb})v_\mathrm{kick},

where f_\mathrm{fb} is the fraction that falls back, taken from Fryer et al. (2012). The fraction is greater for larger black holes, so the biggest black holes get no kicks. This means that the largest black holes are unaffected by the value of \sigma_\mathrm{kick}.

The likelihood

In this analysis, we have two pieces of information: the number of detections, and the chirp masses of the detections. The first is easy to summarise with a single number. The second is more complicated, and we consider the fraction of events within different chirp mass bins.

Our COMPAS model predicts the merger rate \mu and the probability of falling in each chirp mass bin p_k (we factor measurement uncertainty into this). Our observations are the the total number of detections N_\mathrm{obs} and the number in each chirp mass bin c_k (N_\mathrm{obs} = \sum_k c_k). The likelihood is the probability of these observations given the model predictions. We can split the likelihood into two pieces, one for the rate, and one for the chirp mass distribution,

\mathcal{L} = \mathcal{L}_\mathrm{rate} \times \mathcal{L}_\mathrm{mass}.

For the rate likelihood, we need the probability of observing N_\mathrm{obs} given the predicted rate \mu. This is given by a Poisson distribution,

\displaystyle \mathcal{L}_\mathrm{rate} = \exp(-\mu t_\mathrm{obs}) \frac{(\mu t_\mathrm{obs})^{N_\mathrm{obs}}}{N_\mathrm{obs}!},

where t_\mathrm{obs} is the total observing time. For the chirp mass likelihood, we the probability of getting a number of detections in each bin, given the predicted fractions. This is given by a multinomial distribution,

\displaystyle \mathcal{L}_\mathrm{mass} = \frac{N_\mathrm{obs}!}{\prod_k c_k!} \prod_k p_k^{c_k}.

These look a little messy, but they simplify when you take the logarithm, as we need to do for the Fisher information matrix.

When we substitute in our likelihood into the expression for the Fisher information matrix, we get

\displaystyle F_{ij} = \mu t_\mathrm{obs} \left[ \frac{1}{\mu^2} \frac{\partial \mu}{\partial \lambda_i} \frac{\partial \mu}{\partial \lambda_j}  + \sum_k\frac{1}{p_k} \frac{\partial p_k}{\partial \lambda_i} \frac{\partial p_k}{\partial \lambda_j} \right].

Conveniently, although we only need to evaluate first-order derivatives, even though the Fisher information matrix is defined in terms of second derivatives. The expected number of events is \langle N_\mathrm{obs} \rangle = \mu t_\mathrm{obs}. Therefore, we can see that the measurement uncertainty defined by the inverse of the Fisher information matrix, scales on average as N_\mathrm{obs}^{-1/2}.

For anyone worrying about using the likelihood rather than the posterior for these estimates, the high number of detections [bonus note] should mean that the information we’ve gained from the data overwhelms our prior, meaning that the shape of the posterior is dictated by the shape of the likelihood.

Interpretation of the Fisher information matrix

As an alternative way of looking at the Fisher information matrix, we can consider the shape of the likelihood close to its peak. Around the maximum likelihood point, the first-order derivatives of the likelihood with respect to the population parameters is zero (otherwise it wouldn’t be the maximum). The maximum likelihood values of N_\mathrm{obs} = \mu t_\mathrm{obs} and c_k = N_\mathrm{obs} p_k are the same as their expectation values. The second-order derivatives are given by the expression we have worked out for the Fisher information matrix. Therefore, in the region around the maximum likelihood point, the Fisher information matrix encodes all the relevant information about the shape of the likelihood.

So long as we are working close to the maximum likelihood point, we can approximate the distribution as a multidimensional normal distribution with its covariance matrix determined by the inverse of the Fisher information matrix. Our results for the measurement uncertainties are made subject to this approximation (which we did check was OK).

Approximating the likelihood this way should be safe in the limit of large N_\mathrm{obs}. As we get more detections, statistical uncertainties should reduce, with the peak of the distribution homing in on the maximum likelihood value, and its width narrowing. If you take the limit of N_\mathrm{obs} \rightarrow \infty, you’ll see that the distribution basically becomes a delta function at the maximum likelihood values. To check that our N_\mathrm{obs} = 1000 was large enough, we verified that higher-order derivatives were still small.

Michele Vallisneri has a good paper looking at using the Fisher information matrix for gravitational wave parameter estimation (rather than our problem of binary population synthesis). There is a good discussion of its range of validity. The high signal-to-noise ratio limit for gravitational wave signals corresponds to our high number of detections limit.