This year will see the start of operations for the Vera C. Rubin Observatory and its ten-year mission of repeatedly surveying the sky.  The immense amount of recorded data will allow scientists across the globe to discover a wide array of phenomena – from new objects in the solar system such as asteroids and comets, to stars both dimming and brightening, and, most interesting to me, supernovae.  These explosive astrophysical events are both very cool and very informative, giving us not only insight into the lifecycles of stars and galaxies but also allowing us to probe the nature of mysteries such as dark energy.

The other topic of interest regarding Rubin is the phrase immense amount of recorded data.  The Rubin data releases require a significant amount of processing for scientists to filter out aspects unimportant to their research.  In the case of transients – supernovae – a series of brokers have popped up worldwide to stream ‘alerts’ that astronomers can look through to identify interesting objects.  These span the width of the Atlantic; the Chilean-led ALeRCE; the Fink broker based in France; AMPEL in Berlin; and most importantly to me: Lasair; developed by The University of Edinburgh, Oxford University, and Queen’s University Belfast.  My current work aims to help automatically associate transients with their nearby ‘host’ galaxy; an important step for rapid supernovae detection.  This work will be implemented within Lasair and over the course of my PhD I have collaborated closely with members of the team in this and other projects.

The Rubin alerts and Broker Workshop held at Oxford this January aimed to bring these different groups together as the observatory entered its final stages of preparation to discuss how best to tackle the challenges of the incoming data.  It was a great opportunity to showcase my work to those it would be most relevant to – and with it only being in the early stages a good opportunity to get expert advice.

I have spoken previously in this series about the pressure of giving a conference talk.  Particularly early on in a PhD career each presentation is a new opportunity to debut your work and focuses to a new group of peers with who you hope to establish a good working relationship with in the coming years.  To compound this pressure, you typically care about your thesis much more than you would care about a routine undergraduate assignment or piece of industry analysis that would involve presenting work – any semblance of rejection or indifference can be discouraging.  Over the course of my PhD I have managed to control these feelings more, largely to the extent that presentations feel like a frustrating interruption to other work than the fulcrum of my efforts.

Thankfully I have a knack for being given talk slots that fall fairly early in a conference programme.  While this does shorten the prep time a bit it, also means I can kick my feet up a bit afterwards and focus on actually enjoying other talks.  I have spoken to others in the Rubin Broker community before; in meetings, online, and any other way but in person.  Finally being able to catch up ‘informally’ with people – passing them in corridors or bumping into them at coffee breaks – was a valuable experience in feeling like part of the team.

We sat for the conference dinner later that evening.  Dinner in Christ Church is always a bit odd in its formality in a way that’s completely redeemed by the servers’ determination to give you as much free drink as possible.  After a fantastic dessert my supervisor highlighted that many of the important discoveries made over the course of Rubin’s operations would be made by the younger researchers in the room (I include myself in that group – I’ll turn thirty when I’m ready and not a minute earlier).  With us gathered in excitement we watched an (admittedly underwhelming) test image from Rubin roll in and went on to celebrate elsewhere in the city.

The second day was more of the same, and with the pressure off I was able to engage with other people’s work and share insight on similar problems I’d encountered in my own.  It was interesting to compare to my earlier conferences where I’d felt more like someone who had walked in off the street – after two and a half years I felt like a member of the astronomical community; knowledgeable in my own right but able to ask the right questions of others.

This was summarised best at the final coffee break, when I bumped into an old Professor from Southampton, where I had completed my undergraduate degree. The last time we had spoken was long before I began at Queens – when I’d first considered beginning a PhD.  “It’s good to know you’ve landed on your feet!”

 

Joshua Weston

Joshua is in the third year of his LINAS Doctoral Scholarship.  The LINAS Doctoral Training Programme (DTP) seeks to develop a cohort of Doctoral Scholars who can address the implications of massive-scale data processing, artificial intelligence (AI) and machine learning (ML) for both the actual operation of algorithmically driven public decision-making in wider society, and within science and engineering.