Archive for Machine Learning

New Publication at the Open Journal of Astrophysics

Posted in Open Access, The Universe and Stuff with tags , , , , , , , , on March 24, 2021 by telescoper

Time to announce another publication in the Open Journal of Astrophysics. This one was published yesterday, actually, but I didn’t get time to post about it until just now. It is the third paper in Volume 4 (2021) and the 34th paper in all.

The latest publication is entitled Dwarfs from the Dark (Energy Survey): a machine learning approach to classify dwarf galaxies from multi-band images and is written by Oliver Müller  of the Observatoire Astronomique de Strasbourg (France) and Eva Schnider of the University of Basel (Switzerland).

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

 

You can click on the image to make it larger should you wish to do so. You can find the arXiv version of the paper here. This one is in the Instrumentation and Methods for Astrophysics Folder, though it does overlap with Astrophysics of Galaxies too.

It seems the authors were very happy with the publication process!

Incidentally, the Scholastica platform we are using for the Open Journal of Astrophysics is continuing to develop additional facilities. The most recent one is that the Open Journal of Astrophysics now has the facility to include supplementary files (e.g. code or data sets) along with the papers we publish. If any existing authors (i.e. of papers we have already published) would like us to add supplementary files retrospectively then please contact us with a request!

Machine Learning in the Physical Sciences

Posted in The Universe and Stuff with tags , , , , , on March 29, 2019 by telescoper

If, like me, you feel a bit left behind by goings-on in the field of Machine Learning and how it impacts on physics then there’s now a very comprehensive review by Carleo et al on the arXiv.

Here is a picture from the paper, which I have included so that this post has a picture in it:

The abstract reads:

Machine learning encompasses a broad range of algorithms and modeling tools used for a vast array of data processing tasks, which has entered most scientific disciplines in recent years. We review in a selective way the recent research on the interface between machine learning and physical sciences.This includes conceptual developments in machine learning (ML) motivated by physical insights, applications of machine learning techniques to several domains in physics, and cross-fertilization between the two fields. After giving basic notion of machine learning methods and principles, we describe examples of how statistical physics is used to understand methods in ML. We then move to describe applications of ML methods in particle physics and cosmology, quantum many body physics, quantum computing, and chemical and material physics. We also highlight research and development into novel computing architectures aimed at accelerating ML. In each of the sections we describe recent successes as well as domain-specific methodology and challenges.

The next step after Machine Learning will of course be Machine Teaching…