exFock is the latest iteration of the Fock Cluster. This has been in use by the NRC theory group for several years, and recently superseded. As of early 2023, some nodes have been passed on to the experimental groups for general computational use. (For a general introduction to computational clusters, try this overview or the wikipaedia page.)
exFock is a 14+1 node linux cluster for CPU-compute, each with dual Intel Xeon E5-2660 v4 @ 2.00GHz CPU, 128Gb ECC RAM and ~700Gb fast scratch space, running Ubuntu 16LTS. The cluster is managed by a head node with ~8Tb shared storage, with a gigabit Ethernet backbone. (Limited GPU-compute is also available, via NVidia Quadro K2200 cards – one per node, CUDA compute capability 3.0, 640 CUDA cores, 4Gb RAM per card.)
Computational jobs can be dispatched via Slurm or Docker. The former was in use by the theory group (mainly for electronic structure and molecular dynamics codes, running from local shared installations); the latter has just been added to exFock and is suggested for ongoing general use to avoid issues with OS-specific applications and compilation, as well as general sandboxing and as an easy way to enable local user development and testing capabilities (since the Docker containers are fully portable). Note that initial users may need some basic background knowledge whilst exFock is in the setup/trial stage, although the barrier to entry will gradually drop over time. A global installation of Jupyter (Hub) is also planned in the near future, for general data analysis tasks, and will offer a lower barrier to entry for users interested in data analysis tasks.
If you’re interested in using exFock, please use the registration form below (or via the google form page). For more on our heterogeneous compute projects, see here.
Testing for bib file locations Feb 2021
UPDATE Sept. 2021, testing embed from Github page.
NOTE: embeds not working in “preview” but OK on page proper. Some issues with height/scroll-bar, not yet resolved.
Note Github pages version currently also not rendering, not sure why – should be at https://uqogroup.github.io/UQO-group-publications/UQO_group_GScholar.html – OK after setting Jekyll theme!
Definitely not working correctly (although almost!) for GDrive hosted files. Better to use Zotero or Github for group access, probably.
Margaret Gregory, Paul Hockett, Albert Stolow, Varun Makhija
arXiv:2012.04561, Dec. 2020
The application of a matrix-based reconstruction protocol for obtaining Molecular Frame (MF) photoelectron angular distributions (MFPADs) from laboratory frame (LF) measurements (LFPADs) is explored. Similarly to other recent works on the topic of MF reconstruction, this protocol makes use of time-resolved LF measurements, in which a rotational wavepacket is prepared and probed via photoionization, followed by a numerical reconstruction routine; however, in contrast to other methodologies, the protocol developed herein does not require determination of photoionization matrix elements, and consequently takes a relatively simple numerical form (matrix transform making use of the Moore-Penrose inverse). Significantly, the simplicity allows application of the method to the successful reconstruction of MFPADs for polyatomic molecules. The scheme is demonstrated numerically for two realistic cases, N2 and C2H4. The new technique is expected to be generally applicable for a range of MF reconstruction problems involving photoionization of polyatomic molecules.
The recent JCEP* seminar series is now availble online as a series of videos. This should provide a flavour of current research at NRC and the University of Ottawa, for those interested.
* JCEP = Joint Centre for Extreme Photonics
“The Joint Centre for Extreme Photonics (JCEP) was formed in 2019 as a joint undertaking between the National Research Council (NRC) and the University of Ottawa (uOttawa). It is composed of 12 Fellows: 6 from NRC and 6 from uOttawa. Extreme photonics covers research topics ranging from single-photon sources to intense femtosecond lasers.”
Update July 2020: now published as Appl. Phys. Lett. 117, 044001 (2020); https://doi.org/10.1063/5.0012429
[Submitted on 1 May 2020]
Peter Svihra, Yingwen Zhang, Paul Hockett, Steven Ferrante, Benjamin Sussman, Duncan England, Andrei Nomerotski
We describe a simple multivariate technique of likelihood ratios for improved discrimination of signal and background in multi-dimensional quantum target detection. The technique combines two independent variables, time difference and summed energy, of a photon pair from the spontaneous parametric down-conversion source into an optimal discriminant. The discriminant performance was studied in experimental data and in Monte-Carlo modelling with clear improvement shown compared to previous techniques. As novel detectors become available, we expect this type of multivariate analysis to become increasingly important in multi-dimensional quantum optics.