BEGIN:VCALENDAR VERSION:2.0 PRODID:-//Sabre//Sabre VObject 4.5.8//EN CALSCALE:GREGORIAN BEGIN:VTIMEZONE TZID:Europe/Zurich X-LIC-LOCATION:Europe/Zurich TZURL:http://tzurl.org/zoneinfo/Europe/Zurich BEGIN:DAYLIGHT TZOFFSETFROM:+0100 TZOFFSETTO:+0200 TZNAME:CEST DTSTART:19810329T020000 RRULE:FREQ=YEARLY;BYMONTH=3;BYDAY=-1SU END:DAYLIGHT BEGIN:STANDARD TZOFFSETFROM:+0200 TZOFFSETTO:+0100 TZNAME:CET DTSTART:19961027T030000 RRULE:FREQ=YEARLY;BYMONTH=10;BYDAY=-1SU END:STANDARD END:VTIMEZONE BEGIN:VEVENT UID:news1578@dmi.unibas.ch DTSTAMP;TZID=Europe/Zurich:20231123T195049 DTSTART;TZID=Europe/Zurich:20231123T161500 SUMMARY:DMI Kolloquium: Tomasz Kacprzak (Swiss Data Science Center\, Paul S cherrer Institute/ETH Zürich) DESCRIPTION:Tomasz Kacprzak is a Senior Scientist at ETH Zurich and a Senio r Data Scientist at the Swiss Data Science Center at the Paul Scherrer Ins titute. He obtained his PhD in Physics and Astronomy from the University C ollege London\, as well as previously a MSc in Machine Learning from the s ame university. His focus is on interdisciplinary science in physics and a rtificial intelligence\, in particular on applications of machine learning and high-performance computing to solve otherwise-untraceable problems in physics and cosmology. On the cosmology side\, he is involved in the Dark Energy Survey project\, the Euclid satellite\, and the Square Kilometer A rray. More information here [http://tomaszkacprzak.github.io].\\r\\nAbstra ct: The upcoming mega-telescopes\, such as European Space Agency’s recen tly launched Euclid satellite\, and the upcoming radio Square Kilometer Ar ray (SKA)\, will provide images of our universe over 10 billion years of c osmic history\, and enable us to study its evolution with unprecedented le vel of detail. In the framework of simulations-based inference\, the big t elescopes deliver a throve of high-resolution observations and big compute rs provide the feature-rich numerical theory prediction. By using AI train ed on simulations and performing inference on observations\, we will be ab le us to differentiate between complex models of dark matter\, dark energy \, modified gravity\, and astrophysics\; a task unattainable by classical pen-and-paper analysis. This way\, big computers paired with big telescope s will enable next breakthroughs in our understanding of the universe. In this talk\, I will review the current and future applications of HPC and A I in cosmology in astrophysics\, focusing on areas where they already play an indispensable role. I will also present a number of yet-unsolved probl ems in data science\, the solutions to which are critical for enabling nex t generation measurements in cosmology. X-ALT-DESC:

Tomasz Kacprzak is a Senior Scientist at ETH Zurich and a Sen ior Data Scientist at the Swiss Data Science Center at the Paul Scherrer I nstitute. He obtained his PhD in Physics and Astronomy from the University College London\, as well as previously a MSc in Machine Learning from the same university. His focus is on interdisciplinary science in physics and artificial intelligence\, in particular on applications of machine learni ng and high-performance computing to solve otherwise-untraceable problems in physics and cosmology. On the cosmology side\, he is involved in the Da rk Energy Survey project\, the Euclid satellite\, and the Square Kilometer Array.
More information h ere.

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Abstract:
The upcoming mega-telescopes\, such as Eu ropean Space Agency’s recently launched Euclid satellite\, and the upcom ing radio Square Kilometer Array (SKA)\, will provide images of our univer se over 10 billion years of cosmic history\, and enable us to study its ev olution with unprecedented level of detail. In the framework of simulation s-based inference\, the big telescopes deliver a throve of high-resolution observations and big computers provide the feature-rich numerical theory prediction. By using AI trained on simulations and performing inference on observations\, we will be able us to differentiate between complex models of dark matter\, dark energy\, modified gravity\, and astrophysics\; a ta sk unattainable by classical pen-and-paper analysis. This way\, big comput ers paired with big telescopes will enable next breakthroughs in our under standing of the universe. In this talk\, I will review the current and fut ure applications of HPC and AI in cosmology in astrophysics\, focusing on areas where they already play an indispensable role. I will also present a number of yet-unsolved problems in data science\, the solutions to which are critical for enabling next generation measurements in cosmology.

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