Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userPhotometric Calibration on Lunar-based Ultraviolet Telescope for Its First Six Months of Operation on Lunar Surface
552
0
0.0
(
0
)
Ask ChatGPT about the research
No Arabic abstract
We reported the photometric calibration of Lunar-based Ultraviolet telescope (LUT), the first robotic astronomical telescope working on the lunar surface, for its first six months of operation on the lunar surface. Two spectral datasets (set A and B) from near-ultraviolet (NUV) to optical band were constructed for 44 International Ultraviolet Explorer (IUE) standards, because of the LUTs relatively wide wavelength coverage. Set A were obtained by extrapolating the IUE NUV spectra ($lambda<3200AA$) to optical band basing upon the theoretical spectra of stellar atmosphere models. Set B were exactly the theoretical spectra from 2000AA to 8000AA extracted from the same model grid. In total, seven standards have been observed in 15 observational runs until May 2014. The calibration results show that the photometric performance of LUT is highly stable in its first six months of operation. The magnitude zero points obtained from the two spectral datasets are also consistent with each other, i.e., $mathrm{zp=17.54pm0.09}$mag (set A) and $mathrm{zp=17.52pm0.07}$mag (set B).
rate research
Read More
We here report the photometric performance of Lunar-based Ultraviolet telescope (LUT), the first robotic telescope working on the Moon, for its 18-months operation. In total, 17 IUE standards have been observed in 51 runs until June 2015, which returns a highly stable photometric performance during the past 18 months (i.e., no evolution of photometric performance with time). The magnitude zero point is determined to be $17.53pm0.05$ mag, which is not only highly consistent with the results based on its first 6-months operation, but also independent on the spectral type of the standard from which the magnitude zero point is determined. The implications of this stable performance is discussed, and is useful for next generation lunar-based astronomical observations.
The Lunar Cherenkov technique is a promising method for UHE neutrino and cosmic ray detection which aims to detect nanosecond radio pulses produced during particle interactions in the Lunar regolith. For low frequency experiments, such as NuMoon, the frequency dependent dispersive effect of the ionosphere is an important experimental concern as it reduces the pulse amplitude and subsequent chances of detection. We are continuing to investigate a new method to calibrate the dispersive effect of the ionosphere on lunar Cherenkov pulses via Faraday rotation measurements of the Moons polarised emission combined with geomagnetic field models. We also extend this work to include radio imaging of the Lunar surface, which provides information on the physical and chemical properties of the lunar surface that may affect experimental strategies for the lunar Cherenkov technique.
The content of $mathrm{OH/H_2O}$ molecules in the tenuous exosphere of the Moon is still an open issue at present. We here report an unprecedented upper limit of the content of the OH radicals, which is obtained from the in-situ measurements carried out rm by the Lunar-based Ultraviolet Telescope, a payload of Chinese Change-3 mission. By analyzing the diffuse background in the images taken by the telescope, the column density and surface concentration of the OH radicals are inferred to be $<10^{11} mathrm{cm^{-2}}$ and $<10^{4} mathrm{cm^{-3}}$ (by assuming a hydrostatic equilibrium with a scale height of 100km), respectively, by assuming that the recorded background is fully contributed by their resonance fluorescence emission. The resulted concentration is lower than the previously reported value by about two orders of magnitude, and is close to the prediction of the sputtering model. In addition, the same measurements and method allow us to derive a surface concentration of $<10^{2} mathrm{cm^{-3}}$ for the neutral magnesium, which is lower than the previously reported upper limit by about two orders of magnitude. These results are the best known of the OH (MgI) content in the lunar exosphere to date.
In this work we show that modern data-driven machine learning techniques can be successfully applied on lunar surface remote sensing data to learn, in an unsupervised way, sufficiently good representations of the data distribution to enable lunar technosignature and anomaly detection. In particular we train an unsupervised distribution learning neural network model to find the Apollo 15 landing module in a testing dataset, with no dataset specific model or hyperparameter tuning. Sufficiently good unsupervised data density estimation has the promise of enabling myriad useful downstream tasks, including locating lunar resources for future space flight and colonization, finding new impact craters or lunar surface reshaping, and algorithmically deciding the importance of unlabeled samples to send back from power- and bandwidth-constrained missions. We show in this work that such unsupervised learning can be successfully done in the lunar remote sensing and space science contexts.
We propose a suite of telescopes be deployed as part of the Artemis III human-crewed expedition to the lunar south pole, able to collect wide-field simultaneous far-ultraviolet (UV), near-UV, and optical band images with a fast cadence (10 seconds) of a single part of the sky for several hours continuously. Wide-field, high-cadence monitoring in the optical regime has provided new scientific breakthroughs in the fields of exoplanets, stellar astrophysics, and astronomical transients. Similar observations cannot be made in the UV from within Earths atmosphere, but are possible from the Moons surface. The proposed observations will enable studies of atmospheric escape from close-in giant exoplanets, exoplanet magnetospheres, the physics of stellar flare formation, the impact of stellar flares on exoplanet habitability, the internal stellar structure of hot, compact stars, and the early-time evolution of supernovae and novae to better understand their progenitors and formation mechanisms.
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
