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Register a new userFraction of clear skies above astronomical sites: a new analysis from the GOES12 satellite
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Comparing the number of clear nights (cloud free) available for astronomical observations is a critical task because it should be based on homogeneous methodologies. Current data are mainly based on different judgements based on observer logbooks or on different instruments. In this paper we present a new homogeneous methodology on very different astronomical sites for modern optical astronomy, in order to quantify the available night time fraction. The data are extracted from night time GOES12 satellite infrared images and compared with ground based conditions when available. In this analysis we introduce a wider average matrix and 3-Bands correlation in order to reduce the noise and to distinguish between clear and stable nights. Temporal data are used for the classification. In the time interval 2007-2008 we found that the percentage of the satellite clear nights is 88% at Paranal, 76% at La Silla, 72.5% at La Palma, 59% at Mt. Graham and 86.5% at Tolonchar. The correlation analysis of the three GOES12 infrared bands B3, B4 and B6 indicates that the fraction of the stable nights is lower by 2% to 20% depending on the site.
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In this paper we have evaluated the amount of available telescope time at four interesting sites for astronomical instrumentation. We use the GOES 12 data for the years 2008 and 2009. We use a homogeneous methodology presented in several previous papers to classify the nights as clear (completely cloud-free), mixed (partially cloud-covered), and covered. Additionally, for the clear nights, we have evaluated the amount of satellite stable nights which correspond to the amount of ground based photometric nights, and the clear nights corresponding to the spectroscopic nights. We have applied this model to two sites in the Northern Hemisphere (San Pedro Martir (SPM), Mexico; Izana, Canary Islands) and to two sites in the Southern Hemisphere (El Leoncito, Argentine; San Antonio de Los Cobres (SAC), Argentine). We have obtained, from the two years considered, a mean amount of cloud free nights of 68.6% at Izana, 76.0% at SPM, 70.6% at Leoncito and 70.0% at SAC. We have evaluated, among the cloud free nights, an amount of stable nights of 62.6% at Izana, 69.6% at SPM, 64.9% at Leoncito, and 59.7% at SAC.
The analysis of the night cloud cover is very important for astronomical observation in real time, considering a typical observation time of about 15 minutes, and to have a statistics of the night cloud cover. In this paper we use the SQM (Sky Quality Meter) for high resolution temporal analysis of the La Silla and Asiago (Ekar observatory) sky: 3 and 5 minutes respectively. We investigate the annual temporal evolution of the natural contributions of the sky in a site not influenced by artificial light at night (ALAN) and one highly influenced respectively. We also make a correlation between GOES and AQUA satellites data and ground-based SQM data to confirm a relationship between the SQM data and cloud cover. We develop an algorithm that allows the use of the SQM for night cloud detection and we reach a correlation of 97.2% at La Silla and 94.6% at Asiago with the nighttime cloud cover detected by the GOES and AQUA satellites. Our algorithm also classifies the photometric (PN) and spectroscopic nights (SN). We measure 59.1% PN and 21.7% SN for a total percentage of clear nights of 80.8% at La Silla in 2018. The respective Ekar observatory values are 31.1% PN, 24.0% SN and 55.1% of total clear nights time. Application to the SQM network would involve the development of long-term statistics and big data forecasting models, for site testing and real-time astronomical observation.
Exoplanets, short for `extra solar planets, are planets outside our solar system. They are objects with masses less than around 15 Jupiter-masses that orbit stars other than the Sun. They are small enough so they can not burn deuterium in their cores, yet large enough that they are not so called `dwarf planets like Pluto. To discover life elsewhere in the universe, particularly outside our own solar system, a good starting point would be to search for planets orbiting nearby Sun-like stars, since the only example of life we know of thrives on a planet we call Earth that orbits a G-type dwarf star. Furthermore, understanding the population of exoplanetary systems in the nearby solar neighbourhood allows us to understand the mechanisms that built our own solar system and gave rise to the conditions necessary for our tree of life to flourish. Signal processing is an integral part of exoplanet detection. From improving the signal-to-noise ratio of the observed data to applying advanced statistical signal processing methods, among others, to detect signals (potential planets) in the data, astronomers have tended, and continue to tend, towards signal processing in their quest of finding Earth-like planets. The following methods have been used to detect exoplanets.
The Characterising Exoplanet Satellite (CHEOPS) is a space mission designed to perform photometric observations of bright stars to obtain precise radii measurements of transiting planets. The high-precision photometry of CHEOPS relies on careful on-ground calibration of its payload. For that purpose, intensive pre-launch campaigns of measurements were carried out to calibrate the instrument and characterise its photometric performances. We report on main results of these campaigns, provide a complete analysis of data sets and estimate in-flight photometric performance by mean of end-to-end simulation. The on-ground photometric stability of the instrument is found to be of the order of 15 parts per million over 5 hours. Our end-to-end simulation shows that measurements of planet-to-star radii ratio with CHEOPS can be determined with a precision of 2% for a Neptune-size planet transiting a K-dwarf star and 5% for an Earth-size planet orbiting a Sun-like star. It corresponds to signal-to-noise ratios on the transit depths of 25 and 10 respectively, allowing the characterisation and detection of these planets. The pre-launch CHEOPS performances are shown to be compliant with the mission requirements.
I describe a new, open-source astronomical image-fitting program called Imfit, specialized for galaxies but potentially useful for other sources, which is fast, flexible, and highly extensible. A key characteristic of the program is an object-oriented design which allows new types of image components (2D surface-brightness functions) to be easily written and added to the program. Image functions provided with Imfit include the usual suspects for galaxy decompositions (Sersic, exponential, Gaussian), along with Core-Sersic and broken-exponential profiles, elliptical rings, and three components which perform line-of-sight integration through 3D luminosity-density models of disks and rings seen at arbitrary inclinations. Available minimization algorithms include Levenberg-Marquardt, Nelder-Mead simplex, and Differential Evolution, allowing trade-offs between speed and decreased sensitivity to local minima in the fit landscape. Minimization can be done using the standard chi^2 statistic (using either data or model values to estimate per-pixel Gaussian errors, or else user-supplied error images) or Poisson-based maximum-likelihood statistics; the latter approach is particularly appropriate for cases of Poisson data in the low-count regime. I show that fitting low-S/N galaxy images using chi^2 minimization and individual-pixel Gaussian uncertainties can lead to significant biases in fitted parameter values, which are avoided if a Poisson-based statistic is used; this is true even when Gaussian read noise is present.
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