Using a powerful new image-processing technique, researchers at the University of Toronto (Ontario, Canada) have identified an exoplanet in images taken in 1998 with the Hubble Space Telescope's Near-Infrared Camera and Multi-Object Spectrometer (NICMOS).
Using a powerful new image-processing technique, researchers at the University of Toronto (Ontario, Canada) have identified an exoplanet in images taken in 1998 with the Hubble Space Telescope's Near-Infrared Camera and Multi-Object Spectrometer (NICMOS).
David Lafreniere, of the University of Toronto, adapted an image reconstruction technique that was first developed for ground-based observatories. Using the technique, he recovered the planet in NICMOS observations taken 10 years before the planet was discovered in images taken with the Keck and Gemini North telescopes (in 2007 and 2008).
The massive planet, estimated to be at least seven times Jupiter's mass, is 130 light-years away and orbits a young star known as HR 8799.
According to Lafreniere, "We've shown that NICMOS is more powerful than previously thought for imaging planets. Our new image-processing technique efficiently subtracts the glare from a star that spills over the coronagraph's edge, allowing us to see planets that are one-tenth the brightness of what could be detected before with Hubble."
NICMOS's view also provided new insights into the physical characteristics of the planet. This was possibly because NICMOS works at near-infrared wavelengths that are blocked by Earth's atmosphere due to absorption by water vapor.
The Hubble Space Telescope is a project of international cooperation between NASA and the European Space Agency and is managed by NASA's Goddard Space Flight Center in Greenbelt, Maryland.
The Advantages and Landscape of Hyperspectral Imaging Spectroscopy
December 9th 2024HSI is widely applied in fields such as remote sensing, environmental analysis, medicine, pharmaceuticals, forensics, material science, agriculture, and food science, driving advancements in research, development, and quality control.
AI, Deep Learning, and Machine Learning in the Dynamic World of Spectroscopy
December 2nd 2024Over the past two years Spectroscopy Magazine has increased our coverage of artificial intelligence (AI), deep learning (DL), and machine learning (ML) and the mathematical approaches relevant to the AI topic. In this article we summarize AI coverage and provide the reference links for a series of selected articles specifically examining these subjects. The resources highlighted in this overview article include those from the Analytically Speaking podcasts, the Chemometrics in Spectroscopy column, and various feature articles and news stories published in Spectroscopy. Here, we provide active links to each of the full articles or podcasts resident on the Spectroscopy website.
Diffuse Reflectance Spectroscopy to Advance Tree-Level NSC Analysis
November 28th 2024Researchers have developed a novel method combining near-infrared (NIR) and mid-infrared (MIR) diffuse reflectance spectroscopy with advanced data fusion techniques to improve the accuracy of non-structural carbohydrate estimation in diverse tree tissues, advancing carbon cycle research.
Regulatory Barriers: Unlocking Near-Infrared Spectroscopy’s Potential in Food Analysis
November 25th 2024Despite its widespread adoption in food quality analysis, near-infrared (NIR) spectroscopy lags behind in regulatory recognition. A study led by researchers from Italy and Spain highlights the disparity between its scientific applications and official methods, urging standardized regulations to fully leverage NIR's sustainability benefits.
The Advantages and Landscape of Hyperspectral Imaging Spectroscopy
December 9th 2024HSI is widely applied in fields such as remote sensing, environmental analysis, medicine, pharmaceuticals, forensics, material science, agriculture, and food science, driving advancements in research, development, and quality control.
AI, Deep Learning, and Machine Learning in the Dynamic World of Spectroscopy
December 2nd 2024Over the past two years Spectroscopy Magazine has increased our coverage of artificial intelligence (AI), deep learning (DL), and machine learning (ML) and the mathematical approaches relevant to the AI topic. In this article we summarize AI coverage and provide the reference links for a series of selected articles specifically examining these subjects. The resources highlighted in this overview article include those from the Analytically Speaking podcasts, the Chemometrics in Spectroscopy column, and various feature articles and news stories published in Spectroscopy. Here, we provide active links to each of the full articles or podcasts resident on the Spectroscopy website.
Diffuse Reflectance Spectroscopy to Advance Tree-Level NSC Analysis
November 28th 2024Researchers have developed a novel method combining near-infrared (NIR) and mid-infrared (MIR) diffuse reflectance spectroscopy with advanced data fusion techniques to improve the accuracy of non-structural carbohydrate estimation in diverse tree tissues, advancing carbon cycle research.
Regulatory Barriers: Unlocking Near-Infrared Spectroscopy’s Potential in Food Analysis
November 25th 2024Despite its widespread adoption in food quality analysis, near-infrared (NIR) spectroscopy lags behind in regulatory recognition. A study led by researchers from Italy and Spain highlights the disparity between its scientific applications and official methods, urging standardized regulations to fully leverage NIR's sustainability benefits.
2 Commerce Drive
Cranbury, NJ 08512