Nick Karnesis
@nickarne.bsky.social
Always struggling to understand physics and stuff. Mostly stuff though.
Expertise: Gravitational waves and cabbage salads.
Views my own, unfortunately...
🌐 https://karnesis.github.io/aboutme/
Expertise: Gravitational waves and cabbage salads.
Views my own, unfortunately...
🌐 https://karnesis.github.io/aboutme/
Yep, took ages for me as well 😅
December 16, 2024 at 6:33 PM
Yep, took ages for me as well 😅
By the way, this non-stationary component of stochastic GW signal is generated by the millions of Double White Dwarfs in our galaxy 🌌
Bottomline: we now have a neat way of detecting non-Gaussianities or non-stationarities. Hopefully a useful tool for the community! 🧵 5/5
Bottomline: we now have a neat way of detecting non-Gaussianities or non-stationarities. Hopefully a useful tool for the community! 🧵 5/5
November 20, 2024 at 12:47 PM
By the way, this non-stationary component of stochastic GW signal is generated by the millions of Double White Dwarfs in our galaxy 🌌
Bottomline: we now have a neat way of detecting non-Gaussianities or non-stationarities. Hopefully a useful tool for the community! 🧵 5/5
Bottomline: we now have a neat way of detecting non-Gaussianities or non-stationarities. Hopefully a useful tool for the community! 🧵 5/5
In our newest work we apply this idea to stochastic signals in the LISA data, and it works quite well! Take a look at this figure, where the ξ parameter of the hyperbolic (a ξ>0 indicates non-Gaussianities), clearly points to the part of the data where the astrophysical component dominates. 🧵 4/5
November 20, 2024 at 12:47 PM
In our newest work we apply this idea to stochastic signals in the LISA data, and it works quite well! Take a look at this figure, where the ξ parameter of the hyperbolic (a ξ>0 indicates non-Gaussianities), clearly points to the part of the data where the astrophysical component dominates. 🧵 4/5
This idea was first used in the context of Gravitational Waves Data Analysis by our brilliant student A. Sasli in this paper: arxiv.org/abs/2305.04709
Among others, she found that the only downside in using this likelihood instead of the usual Gaussian one, is the extra computational power. 🧵 3/5
Among others, she found that the only downside in using this likelihood instead of the usual Gaussian one, is the extra computational power. 🧵 3/5
Heavy-tailed likelihoods for robustness against data outliers: Applications to the analysis of gravitational wave data
In recent years, the field of Gravitational Wave Astronomy has flourished. With the advent of more sophisticated ground-based detectors and space-based observatories, it is anticipated that Gravitatio...
arxiv.org
November 20, 2024 at 12:47 PM
This idea was first used in the context of Gravitational Waves Data Analysis by our brilliant student A. Sasli in this paper: arxiv.org/abs/2305.04709
Among others, she found that the only downside in using this likelihood instead of the usual Gaussian one, is the extra computational power. 🧵 3/5
Among others, she found that the only downside in using this likelihood instead of the usual Gaussian one, is the extra computational power. 🧵 3/5
In this paper, we try to estimate the overall shape of the statistics of the residual data, using the hyperbolic likelihood. The hyperbolic likelihood is quite flexible, and can adjust its shape according to the distribution of the given data! 🧵 2/5
November 20, 2024 at 12:47 PM
In this paper, we try to estimate the overall shape of the statistics of the residual data, using the hyperbolic likelihood. The hyperbolic likelihood is quite flexible, and can adjust its shape according to the distribution of the given data! 🧵 2/5