Phd Proposal: Artificial Intelligence For Mass Measurement Of Exotic Isotopes

4 Oct 2023

Job Information

Organisation/Company

GANIL

Department

Direction

Research Field

Physics » Computational physics

Researcher Profile

First Stage Researcher (R1)

Country

France

Application Deadline

30 Jun 2024 - 00:00 (Europe/Paris)

Type of Contract

Temporary

Job Status

Full-time

Offer Starting Date

1 Oct 2024

Is the job funded through the EU Research Framework Programme?

Not funded by an EU programme

Is the Job related to staff position within a Research Infrastructure?

No

Offer Description

The PhD thesis consists in optimizing the precision of mass measurements of exotic isotopes by using states-of-the-art acquisition techniques, which include machine learning/Artificial Intelligence.

The Super Spectrometer Separator (S3) facility at GANIL-SPIRAL2 will produce in the coming years unprecedented intensities of exotic isotopes by fusion reactions in the N=Z region around the doubly magic 100Sn and in the Z>92 transactinide / superheavy region. With the S3-Low Energy Branch (S3 LEB) a number of isotopes will be available for precise laser spectroscopy and mass measurements. The Piège à Ions Linéaire du Ganil pour la Résolution des Isotopes en Masse (PILGRIM) is a Multi-Reflection time-of-flight Mass Spectrometer (MR-ToF-MS), which has demonstrated a relative accuracy of 10-7 and a resolving power above 105 with an off-line alkali ion source. These states-of-the-art performances were obtained with low ion detection rates (a few pps). A recently improved ion detection system permitting the generation of very short signals (<1.4ns FWHM) will enable to cope with higher rates. In order to benefit fully of this improvement, the FASTERdata acquisition system needs to be upgraded with a high frequency sampling rate, from the present 500 MHz to 5 GHz. This upgrade is now being undertaken at LPC Caen. The new FASTER acquisition will have then to be adapted to the needs of PILGRIM, for the efficient and precise measurement of time-of-flights of individual ions within narrow bunches (10-20 ions in a bunch of ~100ns). Machine learning techniques will have to be employed for the pattern recognition of events, to account for pile-ups, gain deficit and baseline fluctuation for high rates that will affect the precise determination of the time-of-flights. The PhD student will assume responsibility in developing these techniques with the FASTER developers and the physicist in charge of PILGRIM. He/She will be involved in all the upgrades of PILGRIM for improving its resolution and accuracy. He/She will contribute the first data taking with on-line radioactive ion beams delivered by S3 LEB, in order to probe the reliability of the upgraded acquisition system, and in order to probe nuclear physics models away from stability.

Requirements

Research Field

Physics » Computational physics

Education Level

Master Degree or equivalent

Skills/Qualifications

Computing techniques including machine learning / AI

Familiar with techniques used in experimental physics for the detection of particles

Basic knowledge of statistics for the analysis of experimental data

Interested in basic science, nuclear physics models and theory

Fluent in English

Languages

ENGLISH

Level

Good

Research Field

Physics

Additional Information

Website for additional job details

https: // www. ganil-spiral2.eu/jobs/doctorat-et-post-doc/phd-positions/

Work Location(s)

Number of offers available

1

Company/Institute

GANIL

Country

France

City

Caen

Street

boulevard Becquerel

Geofield

Where to apply

E-mail

[email protected]

Contact

City

CAEN

Website

https: // www. ganil-spiral2.eu

Street

Boulevard Henri Becquerel

STATUS: EXPIRED