Interview with Sarrvesh Sridhar, Operations Scientist at the SKAO
ODISSEE’s Impact on SKAO’s Evolution
In this interview, Sarrvesh Sridhar, Operations Scientist at SKAO, discusses the observatory’s data revolution, and the importance of cross-disciplinary collaboration.
Can you introduce yourself?
My name is Sarrvesh Sridhar. I work as an operations scientist for the SKA Observatory. I’m based in the global headquarters, which is situated next door to the historic Jodrell Bank Observatory in the United Kingdom.
In my day-to-day work, I act as a bridge between the engineering teams and the astronomy community. In a way, I function as a translator between the scientific needs of astronomers and the technical constraints of the engineers.
« The solutions developed in the community do not scale to the data volumes we expect to generate with the SKA. »
SKAO is often presented as the world’s largest producer of scientific data. Can you tell us about this challenge?
The SKAO is a flagship radio observatory that will enter early science operations within the next few years. One of the biggest challenges for us is the enormous amount of data that the telescope will eventually produce. This data must be processed and analysed so that astronomers can actually perform scientific research with it. The main challenge is therefore how to handle such massive datasets.
There are existing solutions that have historically been developed in the community, but many of them do not scale to the data volumes we expect to generate with the SKA telescopes in the next decade and beyond. As a community, we therefore need to develop new ways of analysing this data, not only focusing on scientific accuracy but also on computational efficiency that aligns with our sustainability commitments.
Projects like ODISSEE help explore new approaches to address these challenges.
ODISSEE is a European-funded project, while SKA also develops its own solutions. How do these different approaches connect?
It’s not really directed in a centralised way. There is a lot of blue-sky research that needs to happen, and different groups work on their own approaches. You can think of it as having a solution that has two parts.
Part one is something that you need to do today. What we are doing right now is this short-term solution: when astronomers begin getting early science data next year, how are we going to process that data? For now, we reuse many of the traditional approaches developed for previous generations of telescopes for data processing. However, we don’t apply them exactly as they were. We adapt and modify them to fit the needs of the SKAO. At the same time, the scale of data we expect requires us to rethink some of the fundamental approaches. Instead of simply following what has been done over the past 20 or 30 years, we are exploring new solutions that could both perform better and handle much larger datasets.
But where projects like ODISSEE come into the picture is helping with the second part, the long-term vision. For example, there are a lot of new ideas coming up in the AI and machine-learning space.
The SKA telescopes are designed to have a 50-year lifespan at least, so long-term thinking is essential. However, it’s not about reaching a fixed solution at a specific moment. The telescopes themselves will evolve over time. The system we have on day one will definitely not be the same on day ten or day one hundred. There will be new observing modes, upgrades, and extensions throughout the lifetime of the observatory. As the telescopes evolve, the computing infrastructure must evolve as well, and the ODISSEE project is working precisely to create this flexible future environment.
« Where projects like ODISSEE come into the picture is helping with the long-term vision. »
« ODISSEE is one of the few big projects tackling the big data challenge from both the hardware and software points of view. »
One important aspect of the ODISSEE project is training new experts. How important is this for SKAO?
Historically in radio astronomy, there were astronomers who developed techniques and computer scientists who made those techniques efficient. But because telescopes are getting bigger and bigger, we can no longer have those two skillsets separately. We need astronomers who can write good compute code, and computer scientists who understand the techniques produced by facilities like the SKAO.
This is where multidisciplinary projects like ODISSEE come into play, because they bring expertise from many domains, like particle physics at CERN, for example. I think projects like ODISSEE are a rich ground for cross-pollination of ideas and also a great place where young talents can be trained. One of the SKAO’s commitments is to train the next generation who will run the telescope 20 or 30 years from now. Projects like this help make that happen.
Another positive thing is that most projects I have been involved in were focused either on hardware for radio astronomy or software for radio astronomy. ODISSEE is one of the few big projects tackling the big data challenge from both the hardware and software points of view, which I find very exciting.
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