Interests: Astrophysics; computing with data; statistics; data visualization; analytics

Astrophysics

My research broadly deals with cosmic feedback: the link between the growth and evolution of large-scale structures in the Universe (dominated by dark matter) and the galaxies and stars forming within them out of normal matter (baryons), most of which exists in the form of gas. The details of how this connection operates is a key outstanding problem in Astrophysics. See below for more information about cosmic feedback & galaxy clusters.

Galaxy clusters and groups represent component structures of the Universe and span a wide range of masses (around a factor of 1000). Comparing their properties across this mass range is a powerful way to probe non-gravitational physics associated with cosmic feedback, which has a relatively larger impact on smaller systems. By adopting a statistical approach, it is possible to characterise the subtle effects of cosmic feedback as expressed in the properties of populations of galaxies and galaxy systems; a task ideally suited to sophisticated statistical modelling using cutting edge analytical software.

With the volume, quality and availability of data all expanding rapidly, the need for Astronomers to employ modern statistical and analytic tools and techniques is becoming increasingly clear, in order to meet the challenges of the 21st century (what has been called Next generation Astronomy). These same challenges are also emerging in many other areas in academia, business and industry, and with them a new category of interdisciplinary researcher is now beginning to develop: the data scientist.

Data science & statistics

My research in astrophysics involves a lot of working with data, as well as numerical and statistical analysis. For these tasks I rely on the outstanding R language/environment, which is rapidly becoming the lingua franca of data analysis and statistical computing (here are a few reasons why).

According to Google's Chief Economist, Hal Varian, the future belongs to statistics:

Because now we really do have essentially free and ubiquitous data. So the complimentary scarce factor is the ability to understand that data and extract value from it.

Through learning and using R (since 2004), I have become very interested in applied statistics and the emerging field of data science. This is a broad topic which encompasses interdisciplinary skills such as data gathering and manipulation, statistical analysis and data visualization. The following blog articles provide some further insight into the subject: Rise of the data scientist; What is data science?; A taxonomy of data science; and a recent arXiv paper offers an astrophysical perspective on data science.

Astrophysics offers great potential for data science, in the form of large and rapidly growing archives of publicly available data (both observed and simulated) that is essentially entirely free of proprietory/commercial and ethical restrictions on their use. By gaining experience with the highly transferable skills and tools of data science, pure academic researchers can also fulfil the increasingly important requirement of real world usefulness.




Cosmic feedback & galaxy clusters

Clusters and groups of galaxies are ideal laboratories for studying feedback, since they are high density environments that host a rich variety of baryon and gravitational physics, and are also the largest collapsed and bound structures in the Universe. Within this environment, gas cools and condenses out either to form stars or to be accreted onto supermassive black holes at the centre of galaxies. These two endpoints for cooling provide subsequent feedback in the form of exploding supernova and massive outbursts from active galactic nuclei, respectively. The energy released can be coupled to the hot gas and at least partially regulate its cooling.

A close study of cosmic feedback in galaxy clusters is possible becaue the gas inside these structures is compressed and shock heated up to millions of degrees as they collapse and form. At these high temperatures, gas shines brightly in X-rays, presenting a sharp contrast against the relatively faint background glow emanating from the rest of the Universe. X-ray observations (made with orbiting telescopes such as Chandra and XMM-Newton) are particularly useful in diagnosing physical conditions within galaxy clusters, allowing their masses to be measured and characterising the amount and distribution of hot gas that they contain.

More information about some of my work can be found on the key results page (see also the highlights listed on the right panel above). There are also pages on some of the research projects I am involved in, as well as a list of my publications and talks and links to online data from some of my papers, which can be downloaded.



Quick links



Highlights


Gas density cuspiness vs. X-ray/BCG offsetCool cores in galaxy clusters can be destroyed by merging. Sanderson, Edge & Smith, 2009, MNRAS, 398, 1698. Click for more...



Kernel-smoothed density distribution of entropy profile log slopesGalaxy cluster population is bimodal: cool-core vs. non-cool core clusters. Sanderson, O'Sullivan & Ponman, 2009, MNRAS, 395, 764. Click for more...



Entropy vs. mean temperatureClusters of galaxies have excess entropy in their hot gas. Ponman, Sanderson & Finoguenov, 2003, MNRAS, 343, 331. Click for more...



Copyright © 2010-2013 Alastair Sanderson