In the Dark

It’s time to announce yet another new paper at the Open Journal of Astrophysics. This one was published on Friday 9th June.

The latest paper is the 19th paper so far in Volume 6 (2023) and the 84th in all,  so with more than half of 2023 remaining and many papers still in the pipeline we’re on track to reach a total of 100 papers by the end of 2023!

The primary classification for this paper is Cosmology and Nongalactic Astrophysics and its title is “Categorizing models using Self-Organizing Maps: an application to modified gravity theories probed by cosmic shear”. For the uninitiated, a Self-Organizing Map is a machine-learning technique that makes large-dimensional data sets easier to analyze. This paper is yet another one about weak gravitational lensing (cosmic shear), which is obviously what the cool kids do these days.

The authors are: Agnès Ferté (JPL); Shoubaneh Hemmati (IPAC); Daniel Masters (IPAC); Brigitte Montminy (JPL); Peter L. Taylor (JPL); Eric Huff (JPL);  and Jason Rhodes (JPL).

(JPL=Jet Propulsion Laboratory, IPAC= Infrared Processing & Analysis Center, both associated with California Institute of Technology, Pasadena, USA)

Here is a screen grab of the overlay which includes the  abstract:

You can click on the image of the overlay to make it larger should you wish to do so. You can find the officially accepted version of the paper on the arXiv here.

P.S. The first author tweeted about this paper:

This entry was posted on June 13, 2023 at 9:29 am and is filed under OJAp Papers, Open Access, The Universe and Stuff with tags arXiv:2110.13171, Machine Learning, Open Access, Open Journal of Astrophysics, Self-Organizing Maps. You can follow any responses to this entry through the RSS 2.0 feed. You can leave a response, or trackback from your own site.