Mr. Farbod Jahandar Profile

Farbod Jahandar

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Publications (4)

Proceedings Article | 19 October 2023 Presentation + Paper

Development of a 1U new space camera for EMCCD and CCD sensors with enhanced low-light sensitivity

Olivier Daigle, Oleg Djazovski, Jean Dupuis, René Doyon, Farbod Jahandar, Sarah Bouguéroua, Marie-Eve Ducharme, Adam Gilbert, Jeremy Turcotte

Proceedings Volume 12729, 127291C (2023) https://doi.org/10.1117/12.2679002

KEYWORDS: Electron multiplying charge coupled devices, Prototyping, Vibration, Cameras, Design and modelling, Clocks, Temperature metrology, Dark current, Interfaces, Charge-coupled devices

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Proceedings Article | 9 July 2018 Paper

Automated testing of optical fibres: towards the design of the Maunakea Spectroscopic Explorer Fibre Transmission System

Stephanie Monty, Farbod Jahandar, Jooyoung Lee, Kim Venn, Colin Bradley, Darren Erickson, David Crampton, Victor Nicolov, Collin Kielty, Celine Mazoukh, Patrick Hall

Proceedings Volume 10702, 107027I (2018) https://doi.org/10.1117/12.2311952

KEYWORDS: Cameras, Optical benches, Optical testing, Collimation, Spectroscopy, Optical fibers, Telescopes, Spectrographs, Error analysis, CCD cameras

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Proceedings Article | 9 July 2018 Paper

MSE FiTS: the ultimate multi-fiber optic transmission system

Kim Venn, Darren Erickson, David Crampton, Rafal Pawluczyk, Paul Fournier, Pat Hall, Colin Bradley, Alan McConnachie, John Pazder, Farbod Jahandar, Stephanie Monty, Jooyoung Lee, Celine Mazoukh, Collin Kielty, Victor Nicolov, Kei Szeto, Alexis Hill

Proceedings Volume 10702, 107027S (2018) https://doi.org/10.1117/12.2312715

KEYWORDS: Spectrographs, Telescopes, Spectroscopy, Structured optical fibers, Observatories, Spectral resolution, Optical fabrication, Fiber optics, Antireflective coatings

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The Maunakea Spectroscopic Explorer (MSE) is a next-generation observatory, designed to provide highly multiplexed, multi-object spectroscopy over a wide field of view. The observatory will consist of (1) a telescope with an 11.25 m aperture, (2) a 1.5 square-degree science field of view, (3) fibre optic positioning and transmission systems, and (4) a suite of low (R=3000), moderate (R=6000) and high resolution (R=40,000) spectrographs. The Fibre Transmission System (FiTS) consists of 4332 optical fibres, designed to transmit the light from the telescope prime focus to the dedicated spectrographs. The ambitious science goals of MSE require the Fibre Transmission System to deliver performance well beyond the current state of the art for multi-fibre systems, e.g., the sensitivity to observe magnitude 24 objects (@ SNR=2) over a very broad wavelength range (0.37 – 1.8 μm) while achieving relative spectrophotometric accuracy of < 3% and radial velocity precision of 20 km/s (@ SNR=5). This paper details the design of the FiTS fibre system. It places FiTS into context with existing and planned spectroscopic facilities, such as Subaru/PFS, KPNO/DESI, ESO/4MOST and Gemini/GRACES. The results and lessons learned from GRACES are particularly applicable, since FiTS and GRACES share many team members, including industrial partner FiberTech Optica (Kitchener, ON). The FiTS system consists of 57 identical fibre cables. These cables have been designed to be modular, facilitating efficient construction and automated acceptance testing. Each cable consists of 76 fibres, including 57 fibres feeding light to the low and moderate resolution spectrographs and 19 fibres feeding the high-resolution spectrographs. Thus, the MSE/FiTS consists of 4332 fibres in total. Novel construction techniques utilizing continuous high-NA (f/2) fibres, pioneered by FiberTech Optica, are outlined and test results showing < 5% focal ratio degradation (FRD) in V-band are presented. The effect on FRD from varying the input f/# is also shown. Where test data is unavailable, system error budgets have been created to assess design choices on options such as fibre material, anti-reflection coatings, and fibre-optic connectors

Proceedings Article | 6 July 2018 Paper

StarNet: a deep learning analysis of infrared stellar spectra

Collin Kielty, Spencer Bialek, S. Fabbro, K. Venn, T. O'Briain, F. Jahandar, S. Monty

Proceedings Volume 10707, 107072W (2018) https://doi.org/10.1117/12.2313544

KEYWORDS: Spectroscopy, Machine learning, Analytical research, Stars, Signal to noise ratio, Infrared spectroscopy, Astronomy, Astrophysics

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In an era when spectroscopic surveys are capable of collecting spectra for hundreds of thousands of stars, fast and efficient analysis methods are required to maximize scientific impact. These surveys provide a homogeneous database of stellar spectra that are ideal for machine learning applications. One such application, StarNet, is a convolutional neural network developed to derive stellar labels (temperature, surface gravity, and metallicity) from both SDSS-III APOGEE and synthetic stellar spectra. It has demonstrated excellent precision and accuracy over a wide range of signal-to-noise ratios, when trained on either observed or synthetic spectra. Though StarNet was developed using the high-resolution (R ∼ 20, 000) APOGEE spectra and corresponding ASSeT synthetic grid, we suggest that this technique is applicable to other spectral resolutions, spectral surveys, and wavelength regimes. As a demonstration, we present a version of StarNet trained on lower resolution, R=6000, ASSeT synthetic spectra. This resolution was selected to prepare for spectra delivered by Gemini/NIFS and the forthcoming Gemini/GIRMOS instruments. Results suggest that the stellar parameters determined from this medium-resolution StarNet version can be comparable in precision to the high-resolution APOGEE results. This success can be attributed to (1) a large training set of synthetic spectra (N∼200,000) with a priori stellar labels, and (2) the use of the entire spectrum in the solution rather than a few weighted windows, which is common in other automated spectral analysis methods (e.g. FERRE). Remaining challenges in our StarNet applications include rectification, continuum normalization, and wavelength coverage. Here with present preliminary results on the impact of imperfect continuum rectification when compared to normalized synthetic data. Solutions to these problems will contribute to efficient spectroscopic surveys, their data reduction pipelines, and the precision in their post-data products (for the planned Maunakea Spectroscopic Explorer).

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