What if they had put off the LIGO upgrades?

If a neutron star falls into a black hole but no one has upgraded the gravitational observatory to the required sensitivity, does it fail completely to change our view of the universe?

The Advanced Laser Interferometry Gravitational Observatory (aLIGO) consists of a pair of Fabry–Pérot Interferometers spaced about 3000 km apart, each sporting two cavities about 4 km long and sensitive to length changes smaller than a proton. The tubes containing the optics operate at a vacuum with about 10 times lower pressure than that experienced by the International Space Station in low earth orbit. The lasers put out in excess of 100 kW of laser power, and the power in the chambers is further amplified by each photon reflecting off of the test mass and back several hundred times. Each 20 kg test mass is balanced precariously on threads of glass thinner than things that are really rather thin already. In other words, it’s a huge friggin’ laser powerful enough to burn a burrito, with components precariously balanced in an inside out space ship.

On the 14th of September 2015, these instruments recorded measurements that would support the idea that spacetime changes size when masses accelerate. We usually refer to the instruments and all aspects of the research program supporting it by the same acronym: LIGO. Perhaps you’ve heard of it?

Although the colloquial story is that LIGO recorded the historic GW150914 gravitational wave event during an engineering run even before beginning formal scientific data collection, this isn’t strictly true. In fact LIGO had been performing science runs at Hanford and Livingston sites since 2002. In 2005, LIGO reached an original design sensitivity of strain detection on the order of one part in 1021. Another way to think about, and the common way to report, the sensitivity of the instruments is the distance at which a typical neutron-pair inspiral could nominally be detected. One part in 1021 strain sensitivity corresponds to a search distance of about 8 million parsecs (about 26 million light years). This was the sort of sensitivity LIGO was capable of up until the latter part of 2010. As impressive as that is, there were no gravitational wave detections during operation of LIGO from 2002 to 2010.

The now famous GW150914 and subsequent detections GW151226 and GW170104 came after a comprehensive suite of upgrades that boosted sensitivity to a search distance of 80 million parsecs (~262 light years) away. Four years of shutdown beginning in 2010 marked the transition from “intial LIGO” to “advanced LIGO” (aLIGO). Four years sounds like quite a while in human time, and an especially conservative experimenter might be wont to keep collecting data until proof-of-concept is established. As long as the machine is working in some rudimentary fashion, pushing to eke out just one detection before shutting down for risky upgrades might sound like it makes sense. What if LIGO had put off the upgrades to instead continue with scientific runs? Not much, as it turns out.

Our best guess for the frequency of observable events is based on what aLIGO picked up in the first science run. The first advanced run had about 1100 hours of uptime, time when both instruments were locked-in and active. During this run aLIGO’s picked up 2 confirmed events (and one almost event, yet unconfirmed), giving us a rate of 2 events per 1100 hours in a volume of 2.145 trillion parsecs cubed (the search volume for an 80 Mega-parsec detection distance). This leads us to expect 1 detection for every 22.92 days of run time, or about 16 detections per year, not considering instrument downtime.

Prioritizing data collection at the cost of forgoing upgrades, we would probably still be waiting on the big announcement. Operating at a pre-2014 sensitivity of 8 Mparsecs, we could expect a detection on average once ever 62 years. Assuming a Poisson distribution (events are random), the chances of one or more detections in 4 years of data collection, pre-aLIGO sensitivity, would be just a tick over 6%. For a 50/50 split in the odds of making a detection, we’d have to wait 44 years. Chances are, funding bodies could very well lose interest in that time, and we certainly would not have seen the international enthusiasm in gravitational wave research resulting from the GW150914 announcement.

The moral of the story? The difference between being “productive” and creating something great lies in the old “work smarter, not harder” paradigm. Blind diligence and the perseverance to keep on plugging away has little chance to push the boundaries of what is known to be possible.

Curious about any of the calculations discussed above? Tinker with my notes in this Jupyter notebook


Things to Think About From 2016


A word cloud of theScinder’s output for 2016, made with wordle.net


This subject includes throwbacks to 2015, when I did most of my writing about CRISPR/Cas9. That’s not to say 2016 didn’t contain any major genetic engineering news. In particular scientists are continue to move ahead with the genetic modification of human embryos.

If you feel like I did before I engaged in some deeper background reading, you can catch up with my notes on the basics. I used the protein structures for existing gene-editing techniques to highlight the differences between the old-school gene editing techniques and editing with cas9. I also compared the effort it takes to modify a genome with cas9 to how difficult it was using zinc-finger nucleases, the previous state-of-the-art (spoiler: it amounts to days of difference).

TLDR: The advantage of genetic engineering with Cas9 over previous methods is the difference between writing out a sequence of letters and solving complex molecular binding problems.

aLIGO and the detection of gravitational waves

Among the most impressive scientific breakthroughs of the previous hundred years or so, a bunch of clever people with very sensitive machines announced they’ve detected the squidge-squodging of space. A lot of the LIGO data is available from the LIGO Open Science Center, and this is a great way to learn signal processing techniques in Python. I synchronized the sound of gravitational wave chirp GW150914 to a simulated visualization (from SXS) of a corresponding black hole inspiral and the result is the following video. You can read my notes about the process here. I also modified the chirp to play the first few notes of the “Super Mario Brothers” theme.

Machine Learning

I’ve just started an intensive study of the subject, but machine learning continues to dip its toes into everything to do with modern human life. We have a lot of experience with meat-based learning programs, which should give us some insight into how to avoid common pitfalls. The related renewed interest in artificial intelligence should make the next few years interesting. If we do end up with a “hard” general artificial intelligence sometime soon, it might make competition a bit tough, if you could call it competition at all.

Devote a few seconds of thought to the twin issues of privacy and data ownership.


2016 also marked a renewed interest in manned space exploration, largely because of the announcement from space enthusiast Elon Musk that he’s really stoked to send a few people to Mars. NASA is still interested in Mars as well, and might be a good partner to temper Musk’s enthusiasm. In the Q&A at about 1:21 in the video below, Musk seems to suggest a willingness to die as the primary prerequisite for his first batch of settlers. There’s some known unavoidable and unknown unknowable dangers in the venture, but de-prioritizing survivability as a mission constraint runs a better chance of delaying manned exploration as long as it remains as expensive as Musk optimistically expects.

Here’s some stuff that’s a little a lot less serious about living on Mars.

It doesn’t grab the headlines with such vigor, but Jeff Bezo’s Blue Origins had an impressive year: retiring their first rocket after five flights and exceeding the mission design in a final test of a launch escape system.
Blue Origin is also working on an orbital launch system called New Glenn, in honor of the first astronaut from the USA to orbit the earth.

In that case, where are we headed?

The previous year provided some exciting moments to really trip the synapses, but we had some worrying turns as well. The biggest challenges of the next few decades will all have technical components, and understanding them doesn’t come for free. Humanity is learning more about biology at more fundamental levels, and medicine won’t look the same in ten years. A lot of people seem unconcerned that we probably won’t make the 2 degrees Celsius threshold for limiting climate change, although not worrying about something doesn’t mean it won’t kill anyone. Scientists and engineers have been clever enough to develop machine learners to assist our curiosity, and it’s exciting to think that resurgent interest in AI might give us someone to talk to soon. Hopefully they’ll be better conversationalists than the currently available chatbots, and a second opinion on the nature of the universe could be useful. It’s not going to be easy to keep up with improving automation, and humans will have to think about what working means to them.

Take some time to really dig into these subjects. You probably already have some thoughts and opinions on some of them, so try to read a contrary take. If you can’t think of evidence that might change your mind, you don’t deserve your conclusions.

Remember that science, technological development, and innovation have a much larger long-term effect on humans and our place in the universe than the petty machinations of human fractionation. So keep learning, figure out something new, and remember that if you possess general intelligence you can approach any subject. On the other hand, autogenous annihilation is one of the most plausible solutions to the Fermi Paradox. This is no time to get Kehoed

Super Gravity Brothers


The GW150914 blackhole merger event recorded by aLIGO, represented in a wavelet (morlet base) spectrogram. This spectrogram was based on the audio file released with the original announcment.

The data from the second detection, GW151226, is another beast entirely in that the signal is very much buried in the noise.

Raw data:


Wavelet Spectrogram: gw151226CWTspec

The LIGO Open Science Center makes these data available, along with signal processing tutorials.

Now to see how the professionals do it:

I used MATLAB’s wavelet toolbox for the visualisations, aided by this example