Hi everyone! Today I wanted to announce a paper that we have been working on for over a year now. We found some signs of eccentricity in binary black holes observed by LIGO-Virgo-KAGRA: I thought I would go over the paper a bit in this thread! . 1/15

But the reason I'm switching to BlueSky is because: . 1). Everytime I open X am bombarded with stuff that I don't care about. With Bluesky I can control what I see .2). I don't like Elon, and Elon doesn't own Bluesky.

Join millions who have switched to Grok.

Decided to switch to BlueSky: I had two reasons for downloading twitter. In order they are:. 1). Communicating science with the public .2). Discussing science with other scientists . 1/2.

RT @SXSProject: 🎉New preprint day!🎉 This is a big one! First complete binary black hole inspiral, merger, & ringdown using our next-generat….

Agree, s/o to @physicskaze & @tedwards2412 for their open-source development on Jim and Ripple to help make the 1s inference possible.

RT @physicskaze: This dropped last week, and what a great work led by @maximilian_dax.and @stephen_r_green. I have been somrwhat critical o….

I was on the PE ROTA for this week (helping to analyze the events offline). The back-to-back-to-back-to-back BBHs reminded me of when Kendrick was dropping his diss tracks 😭.

RT @AnnalenaKofler: New tutorial alert: Run our new introductory DINGO tutorial locally or on Google Colab 1/8 http….

RT @PhysLynch: My newest paper is finally on the arXiv today!! It's all about near-identity averaging transformations for extreme mass rati….

This does seem a bit arbitrary, which is why a more precise definition is given later, but for now it’ll do. L(D|H) for stochastic hypothesis has a simple form known as a Shannon-Fano code. This is small when p(D|H) is large and goes like -log p(D|H). More later!.

First the “length” of a hypothesis, L(H), and second the “length” of the data if encoded given the hypothesis L(D|H). L(H), is the number of bits you would use to write this up with a computer program like C for example.

Rather, we assign a quantity called the MDL to a model. This MDL should portray how much a hypothesis, compresses the observed data. Crudely, this consists of two components.

I’ve been interested in this lately, as I’ve been reading about compression algorithms. We can think of MDL as a type of model selection, but unlike the Bayes factor we don’t have to compare two models.

In the last days I’ve been reading about the minimum description length (MDL) in by Peter Grünwald. I will summarize the main ideas I learned from chapter 1.

So yes this was the first paper in my PhD and I'm overall happy with how it turned out. I am excited for the current O4 observing run of the LVK and seeing if there are some potentially eccentric events! . 15/15. P.S. I will get back to the paper summaries soon I promise!.

This number can be anywhere from 99.5% to 99.999% depending on the glitch mitigation used. (Note this number does not take into account spin-precession effects). 14/15

We also compute a new statistic based on ( which quantifies the probability that a binary black hole is eccentric. We can also ask what is the probability that at least one event in the population is eccentric. 13/15.

Ultimately, due to the glitch and the comparison with spin-precession we cannot definitively say that GW200129 is eccentric, rather just that it shows some (Bayesian) evidence for eccentricity. 12/15.

In addition, currently, most eccentric waveform models don't model spin-precession. Spin-precession happens when the spins of the black holes are misaligned with the orbital angular momentum. So we need to compare our hypothesis with this possibility (last two columns):. 11/15

Note, GW200129, has a Bayes factor (betting odds) of 10^4.7 ! The problem with this event is there is a glitch in the detector. Researchers (2206.11932) have developed methods to subtract glitches and how you subtract the glitch can change the level of eccentricity present. 10/15

So what do we see? Well, in three events there are signs of eccentricity. Below, you can see the distribution of eccentricity, colored by the Bayes factor between the eccentric model and quasi-circular model. (The Bayes factor is like the betting odds between two models). 9/15