Dr. Who and How?


Having just started my PhD in Astrophysics at UCL, I thought I would share a post with a brief overview of the area i’m now in, my first impressions of research life and the challenges i’m facing. Usual science-jammed posts will return from next week.

Having shifted my academic focus from theoretical physics to astrophysics, I have been confronted with the necessity to learn a whole host of new skills. My main nemesis right now is the computer and mastering its language. It is hard to de-tangle whether this is primarily due to moving away from what was during my studies a ‘pen and paper’ subject or whether, when it gets to the level of research, any novel work in the sciences in the modern day is going to require computational know-how. Astrophysics inherently requires analysis of large data sets to make advances. Think about it – we are one planet amongst billions of stars hosting billions of other planets. The information that reaches us from outer space is in the form of radiation, across all different wavelengths. We have satellites constantly performing observations in all different directions of the sky and then storing all this information. There is a lot of data to analyse and we need to do it efficiently. 

My research is in the field of exoplanets, planets that orbit stars other than our sun. Within this area my broad focuses involve trying to understand the atmospheres of these planets and model the physical processes going on here. Understanding the atmospheres of planets can help exoplanetary scientists understand what the planet is made of and answer bigger questions such as the planet’s formation and whether life could possibly be present. To analyse these atmospheres one of the tools we use is a programme designed to take the data from the light we receive from the host planet’s star and then backwardly deduce the planets parameters such as it’s size, density, and components making up its atmosphere (for more on this see Strange New Worlds). Such a complex task requires, as you can imagine, a very intelligent computer programme. To use this programme, I need to get to grips with the interface on which it runs and the commands required at the different levels of processing. To fully appreciate and manipulate the programme’s capabilities I need to be able to speak the different languages  it is coded in and then alter the specific lines of code that are address the physics i’m interested in. The bottom line being – computer and coding literacy is essential and I wish i’d got to grips with it earlier in my studies, primarily python and the terminal. I plan to do some posts on the basic structure of these in the near future as I am finding them weirdly fascinating. 

The rest of the portion of my time I am spending reviewing academic papers in my area of research and trying to identify areas of unanswered questions in order to scope out a project that will contribute novelly to the field. I think it is common for supervisors to suggest an area for exploration but given the fluid and uncertain nature of research it seems this can go off in an entirely unexpected direction later down the line. As a result of this research can be vague and a shock when transitioning from regimented full time education. Direction from one’s supervisor must be accompanied with a heavy amount of independent focus, goal setting and organisation. However due to the nature of science, more often than not goals will not be achieved. Goals are essentially scientific hypothesises and unless you are extremely brilliant more often than not your initial ideas will not work, they will have to be refined and adapted again and again. For every success there will most likely have to be ten preceding failures. The bottom line being – research is uncertain and failure is common.

Lastly a key feature i’ve picked up on during my first month is collaboration. Researchers work together, bouncing ideas of each other and constantly combining their skill sets. In order to make a novel break through in an area of science will most often than not require a hell of a lot of skills. Modern exoplanetary science for example combines physical processes, chemistry, statistics, coding and machine learning. Academics will have a breadth of understanding of the areas involved in their research but undoubtably will have a specialism. Papers will always have a number of authors, often from more than one institution. Academia is small world where it seems names are commonly known amongst those in the same community. Though perhaps this is even more prevalent in the exoplanetary community because of the mindset that we are a group of people sharing one planet studying the thousands of our extraterrestrial companions. 

My writing at RTU will become more varied over the PhD as I find myself learning interesting concepts in areas of maths, coding and physics that I’d never dabbled in before. Theoretical physics editions will of course remain but over the doctorate I hope to become a scientist with a much wider skill set and will try reflect this here at the blog.

Read it at the source


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