scholarly journals Mid-infrared interferometry with high spectral resolution

2010 ◽  
Author(s):  
Edward H. Wishnow ◽  
William Mallard ◽  
Vikram Ravi ◽  
Sean Lockwood ◽  
Walt Fitelson ◽  
...  
Keyword(s):  
Spectral Resolution ◽  
High Spectral Resolution ◽  
Mid Infrared ◽  
Infrared Interferometry

scholarly journals Time-resolved mid-infrared dual-comb spectroscopy

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Muhammad A. Abbas ◽  
Qing Pan ◽  
Julien Mandon ◽  
Simona M. Cristescu ◽  
Frans J. M. Harren ◽  
...  
Keyword(s):  
Spectral Resolution ◽  
Vibrational Excitation ◽  
High Spectral Resolution ◽  
Acquisition Time ◽  
Time Resolved ◽  
Short Acquisition Time ◽  
Mid Infrared ◽  
Infrared Wavelength ◽  
Potential Applications ◽  
Kinetics Of

AbstractDual-comb spectroscopy can provide broad spectral bandwidth and high spectral resolution in a short acquisition time, enabling time-resolved measurements. Specifically, spectroscopy in the mid-infrared wavelength range is of particular interest, since most of the molecules have their strongest rotational-vibrational transitions in this “fingerprint” region. Here we report time-resolved mid-infrared dual-comb spectroscopy, covering ~300 nm bandwidth around 3.3 μm with 6 GHz spectral resolution and 20 μs temporal resolution. As a demonstration, we study a CH4/He gas mixture in an electric discharge, while the discharge is modulated between dark and glow regimes. We simultaneously monitor the production of C2H6 and the vibrational excitation of CH4 molecules, observing the dynamics of both processes. This approach to broadband, high-resolution, and time-resolved mid-infrared spectroscopy provides a new tool for monitoring the kinetics of fast chemical reactions, with potential applications in various fields such as physical chemistry and plasma/combustion analysis.

scholarly journals Hybrid high spectral resolution broadband mid-infrared SFG spectrometer development and demonstration

2021 ◽  
Author(s):  
Karolis Madeikis ◽  
Robertas Kananavicius ◽  
Rokas Danilevicius ◽  
Audrius Zaukevicius ◽  
Januskevicius Regimantas ◽  
...  
Keyword(s):  
Spectral Resolution ◽  
High Spectral Resolution ◽  
Mid Infrared

EXES: The Echelon-cross-echelle Spectrograph for SOFIA

2018 ◽  
Vol 07 (04) ◽  
pp. 1840013 ◽  
Author(s):  
M. J. Richter ◽  
C. N. DeWitt ◽  
M. McKelvey ◽  
E. Montiel ◽  
R. McMurray ◽  
...  
Keyword(s):  
Spectral Resolution ◽  
Infrared Astronomy ◽  
High Spectral Resolution ◽  
Echelle Spectrograph ◽  
Mid Infrared ◽  
Low Background ◽  
And Performance ◽  
Operational Modes ◽  
Echelon Grating

The Echelon-cross-echelle spectrograph (EXES) is a high spectral resolution, mid-infrared spectrograph designed for and operated on the Stratospheric Observatory for Infrared Astronomy (SOFIA). EXES has multiple operational modes, but is optimized for high spectral resolution. The heart of the instrument is a one meter long, diamond-machined echelon grating. EXES also uses a 10242 Si:As detector optimized for low-background flux. We will discuss the design, operation and performance of EXES.

scholarly journals Cloud identification and classification from high spectral resolution data in the far and mid infrared

2019 ◽  
Author(s):  
Tiziano Maestri ◽  
William Cossich ◽  
Iacopo Sbrolli
Keyword(s):  
Spectral Resolution ◽  
Learning Algorithm ◽  
Far Infrared ◽  
Synthetic Dataset ◽  
High Spectral Resolution ◽  
Spectral Radiance ◽  
Electromagnetic Spectrum ◽  
Cloud Detection ◽  
Mid Infrared ◽  
Univariate Distribution

Abstract. A new Cloud Identification and Classification algorithm, named CIC, is presented. CIC is a machine-learning algorithm, based on Principal Component Analysis, able to perform a cloud detection and scene classification using a univariate distribution and a threshold, which serves as a binary classifier. CIC is tested on a widespread synthetic dataset of high spectral resolution radiances in the far and mid infrared part of the spectrum simulating measures from the ESA Earth Explorer Fast Track 9 competing mission FORUM (Far Infrared Outgoing Radiation Understanding and Monitoring) that is currently (2018/19) undergoing the industrial and scientific Phase-A studies. Simulated spectra are representatives of many diverse climatic areas, ranging from the tropical to polar regions. Application of the algorithm to the synthetic dataset provides high scores for clear/cloud identification, especially when optimisation processes are performed. One of the main results consists in pointing out the high information content of spectral radiance in the far-infrared region of the electromagnetic spectrum to identify cloudy scenes specifically thin cirrus clouds.

scholarly journals Investigation of circumstellar shells by mid-infrared heterodyne spectroscopy

1987 ◽  
Vol 122 ◽  
pp. 553-554
Author(s):  
U. Schrey ◽  
S. Drapatz ◽  
H.U. Käufl ◽  
H. Rothermel ◽  
S. K. Ghosh
Keyword(s):  
Penetration Depth ◽  
Spatial Resolution ◽  
Spectral Resolution ◽  
High Spatial Resolution ◽  
High Spectral Resolution ◽  
Mid Infrared ◽  
Heterodyne Spectroscopy ◽  
High Penetration ◽  
Circumstellar Shells

Heterodyne spectroscopy at 11 μm combines high spectral resolution (λ/Δ λ ~106), high spatial resolution (< 1 arcsec at 3 m telescopes) and high penetration depth. Therefore, it seems promising to use it also for the investigation of bright circumstellar atmospheres.

scholarly journals Cloud identification and classification from high spectral resolution data in the far infrared and mid-infrared

2019 ◽  
Vol 12 (7) ◽  
pp. 3521-3540 ◽  
Author(s):  
Tiziano Maestri ◽  
William Cossich ◽  
Iacopo Sbrolli
Keyword(s):  
Spectral Resolution ◽  
Similarity Index ◽  
Far Infrared ◽  
Synthetic Dataset ◽  
High Spectral Resolution ◽  
Training Dataset ◽  
Spectral Radiance ◽  
Electromagnetic Spectrum ◽  
Mid Infrared ◽  
Univariate Distribution

Abstract. A new cloud identification and classification algorithm named CIC is presented. CIC is a machine learning algorithm, based on principal component analysis, able to perform a cloud detection and scene classification using a univariate distribution of a similarity index that defines the level of closeness between the analysed spectra and the elements of each training dataset. CIC is tested on a widespread synthetic dataset of high spectral resolution radiances in the far- and mid-infrared part of the spectrum, simulating measurements from the Fast Track 9 mission FORUM (Far-Infrared Outgoing Radiation Understanding and Monitoring), competing for the ESA Earth Explorer programme, which is currently (2018 and 2019) undergoing industrial and scientific Phase A studies. Simulated spectra are representatives of many diverse climatic areas, ranging from the tropical to polar regions. Application of the algorithm to the synthetic dataset provides high scores for clear or cloud identification, especially when optimisation processes are performed. One of the main results consists of pointing out the high information content of spectral radiance in the far-infrared region of the electromagnetic spectrum to identify cloudy scenes, specifically thin cirrus clouds. In particular, it is shown that hit scores for clear and cloudy spectra increase from about 70 % to 90 % when far-infrared channels are accounted for in the classification of the synthetic dataset for tropical regions.

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