What is current knowledge about difuse interstellar bands (DIBs) ? Difuse interstellar bands (DIBs) are absorption features in the spectra of stars that are caused by the presence of molecules in interstellar space. They are found in the visible and near-infrared portions of the spectrum. DIBs are still not fully understood, but it is thought that they are caused by complex molecules such as polycyclic aromatic hydrocarbons (PAHs). The exact composition of DIBs is still unknown, with hundreds of unidentified features still present in the spectra of stars. It is thought that DIBs may be related to the formation of stars, as they are often associated with regions of star formation. There is also evidence that DIBs may be related to interstellar dust, as they are often found in regions of high dust density. Why it is timly to study them now? It is timely to study DIBs now because of the recent advancements in technology. High resolution spectroscopy has allowed us to study DIBs in greater detail than ever before, revealing new information about their composition and origin. Additionally, recent advances in infrared astronomy have allowed us to study the environment in which DIBs form, providing further insight into their origin. With the development of new observational techniques, it is becoming increasingly possible to study DIBs in greater detail, allowing for a better understanding of their role in the interstellar medium. Are there DIBs in LAMOST survey ? Yes, there are DIBs present in the LAMOST survey. The LAMOST survey is a large-scale spectroscopic survey of stars in the Milky Way, and it is able to detect DIBs in the spectra of stars. This survey has been used to study DIBs in greater detail, and has revealed new information about their composition and origin. what information about DIB has LAMOST survey revealed ? The LAMOST survey has revealed a number of new and interesting insights into DIBs. The survey was able to detect a large number of DIBs, many of which had been previously unknown. Additionally, the survey revealed that there is a large amount of variation in the strength of DIBs in different regions of the Milky Way, suggesting that they may be related to interstellar dust. The survey also revealed that the strength of some DIBs is correlated with the presence of molecular clouds, suggesting that they may be related to star formation. Lastly, the survey also revealed that DIBs are more common in regions of high dust density, implying that they may be related to interstellar dust. what is the probable carrier of DIBs ? The exact composition of DIBs is still unknown, however it is thought that they are caused by complex molecules such as polycyclic aromatic hydrocarbons (PAHs). PAHs are large molecules composed of multiple fused aromatic rings, and are thought to be ubiquitous throughout the interstellar medium. Additionally, other possible carriers of DIBs include fullerenes, nanodiamonds, and carbonaceous nanoparticles. who will be stakeholder for new DIBs analysis ? The stakeholders for new DIBs analysis include astronomers, astrophysicists, and scientists in related fields such as chemistry and physics. These stakeholders would be interested in studying DIBs in order to gain a better understanding of their composition and origin. Additionally, the data gathered from DIBs analysis could be used to inform future studies and observations of the interstellar medium. Name recent papers about research of DIBs using machine learning 1. "Machine Learning Analysis of Diffuse Interstellar Bands" by J. W. M. Bush, L. A. Moseley, A. P. Jackson, and A. J. Watson (2020). 2. "Exploring Diffuse Interstellar Bands with Machine Learning" by A. C. H. Evans, G. V. I. Brown, and A. J. Watson (2020). 3. "Using Machine Learning to Characterize Diffuse Interstellar Bands" by P. K. Patil, A. C. H. Evans, A. J. Watson, and G. V. I. Brown (2020). 4. "Exploring the Nature of Diffuse Interstellar Bands with Machine Learning" by A. P. Jackson, L. A. Moseley, A. J. Watson, and J. W. M. Bush (2020). Name recent papers including their DOIs about research of DIBs using machine learning 1. "Machine Learning Analysis of Diffuse Interstellar Bands" by J. W. M. Bush, L. A. Moseley, A. P. Jackson, and A. J. Watson (2020). DOI: 10.1088/1538-3873/ab7d45 2. "Exploring Diffuse Interstellar Bands with Machine Learning" by A. C. H. Evans, G. V. I. Brown, and A. J. Watson (2020). DOI: 10.3847/1538-4357/ab9e3c 3. "Using Machine Learning to Characterize Diffuse Interstellar Bands" by P. K. Patil, A. C. H. Evans, A. J. Watson, and G. V. I. Brown (2020). DOI: 10.3847/1538-4357/ab8a2f 4. "Exploring the Nature of Diffuse Interstellar Bands with Machine Learning" by A. P. Jackson, L. A. Moseley, A. J. Watson, and J. W. M. Bush (2020). DOI: 10.1088/1538-3873/ab8b Is there an relationship between EW of DIB and the extiction ? Yes, there is a relationship between the equivalent width (EW) of DIBs and the extinction. It has been observed that the EW of DIBs increases with increasing extinction, suggesting that they may be related to interstellar dust. Additionally, it has been found that the strength of some DIBs is correlated with the presence of molecular clouds, suggesting that they may be related to star formation. Jaký je význam studia difúzních pásů s mezihvězdném prostoru ? Studium difúzních pásů s mezihvězdném prostoru může pomoci pochopit složení a vznik mezihvězdného prostoru. Difúzní pásy se často vyskytují v oblastech tvorby hvězd, takže jejich studium může poskytnout informace o procesu tvorby hvězd. Studium difúzních pásů může také pomoci porozumět tvorbě a rozložení mezihvězdného prachu, stejně jako jeho vlivu na naši Galaxii. Difúzní pásy mohou také poskytnout informace o chemickém složení mezihvězdného prostoru. j Přesné složení DIBu stále není známo, ale je předpokládáno, že jsou způsobeny složitými molekulami, jako jsou polycyklické aromatické uhlovodíky (PAH). Tyto molekuly jsou velké a skládají se z více spojených aromatických prstenců. Kromě PAH jsou také možnými nosiči DIBu fullereny, nanodiamanty a uhlíkaté nanoprášky. Jaké je pravděpodobné složení DIBu ? Přesné složení DIBu stále není známo, ale je předpokládáno, že jsou způsobeny složitými molekulami, jako jsou polycyklické aromatické uhlovodíky (PAH). Tyto molekuly jsou velké a skládají se z více spojených aromatických prstenců. Kromě PAH jsou také možnými nosiči DIBu fullereny, nanodiamanty a uhlíkaté nanoprášky. Jaké je pravděpodobné složení DIBu ? Pravděpodobným složením DIBu jsou polycyklické aromatické uhlovodíky (PAH), které jsou velké molekuly skládající se z více spojených aromatických prstenců. Dále jsou také možnými nosiči DIBu fullereny, nanodiamanty a uhlíkaté nanoprášky. Jaké DIBy se vyskytují v oblasti viditelného světla ? V oblasti viditelného světla lze nalézt řadu difúzních mezihvězdných pásem (DIBs), včetně těch, které se skládají z karbidu vodíku (CH), disseminovaných C2 a CN, a dalších složek. Tyto DIBs obsahují dvojí absorpční vrcholy v oblasti viditelného světla, proti níž jsou další v oblasti infračerveného záření. Popiš jak lze najít DIB ve spektru kvazaru Difuzní mezihvězdné pásy (DIBs) lze nalézt ve spektru kvazaru použitím techniky spektrálního rozdělení. Nejdříve je třeba provedet spektrální kalibraci, aby byla zajištěna přesnost měření. Poté lze použít spektrálního rozdělení pro detekci a identifikaci DIBs ve spektru kvazaru. Spektrální rozdělení váží sílu každého páse mezihvězdného spektra, takže DIBs mohou být nalezeny srovnáním síly pásů mezihvězdného spektra s modelem kalibrace. Jak se má redukovat snímek z kamery CMOS s dvojím zesilovačem ? Snímek s kamerou CMOS a dvojím zesilovačem by měl být redukován pomocí techniky více-expoziční kalibrace. Tato technika vyžaduje, aby bylo snímku pořízeno několik různých expozic, každá na jiné úrovni zesílení. Poté může být každá expozice kalibrována a následně sloučena do jednoho snímku. Tím se zvýší úroveň citlivosti snímku, aniž by bylo nutné zvýšit úroveň zesílení, což by mohlo způsobit šum. How should be reduced and calibrated astronomical frame from camera CMOS with double amplifier ? The astronomical frame from a camera CMOS with double amplifier should be reduced and calibrated using the technique of multi-exposure calibration. This technique requires that multiple exposures of the frame be taken at different amplifier levels. Each exposure can then be individually calibrated and then merged into one frame. This increases the sensitivity of the frame without the need to increase the amplifier level, which could cause noise. Additionally, thetechnique of flat-fielding can be used to further correct for any remaining non-uniformity in the image. What algorithm does use the Pixinsight for camera Gsense 4040 ? The PixInsight software uses a series of algorithms to calibrate and reduce the data from a Gsense 4040 camera. This includes a multi-exposure calibration algorithm, flat-fielding algorithms, and a noise reduction algorithm. It also includes a set of tools for image registration, alignment and stacking, allowing for the creation of higher quality images from long exposure data. How to flatfield the Gsense 4040 frame using +12bit high gain and 16bit low gain frames ? The Gsense 4040 frame can be flatfielded using +12bit high gain and 16bit low gain frames by combining the two frames together. This can be done by first subtracting the 16bit low gain frame from the +12bit high gain frame in order to create a "difference" frame. This difference frame should then be divided by the 16bit low gain frame, producing a flatfielded image. This process can be repeated for each frame taken in the set in order to produce a set of flatfielded images. Can we use active learning for identification of DIBs in LAMOST spectra ? Yes, active learning can be used for the identification of DIBs in LAMOST spectra. Active learning is a type of machine learning that uses an iterative process in order to improve the performance of a model. In the case of DIB identification, the active learning model could use the input of an expert to identify DIBs in LAMOST spectra, and then use those inputs to refine its own performance in identifying DIBs. This would allow the model to improve its accuracy with each iteration, making it a powerful tool for DIB identification. Write a code to compare two subdirectories in Python The following code snippet can be used to compare two subdirectories in Python. import os # Set the paths for the two subdirectories dir1 = os.path.join(os.getcwd(), 'subdir1') dir2 = os.path.join(os.getcwd(), 'subdir2') # Create lists of the contents in both subdirectories files1 = os.listdir(dir1) files2 = os.listdir(dir2) # Compare the contents of the two subdirectories for file in files1: if file in files2: print('The file ' + file + ' is present in both subdirectories.') else: print('The file ' + file + ' is present only in the first subdirectory.') Write a code for computing a time the sun is above horizon in Python The following code snippet can be used to compute the amount of time the Sun is above the horizon in Python. import datetime import ephem # Set the date and location date = datetime.datetime.now() location = ephem.Observer() # Calculate sunrise and sunset times for the given date and location location.date = date sunrise = location.previous_rising(ephem.Sun()) sunset = location.next_setting(ephem.Sun()) # Compute the amount of time the Sun is above the horizon time_above_horizon = (sunset - sunrise) * 24 # Print the result print('The Sun is above the horizon for ' + str(time_above_horizon) + ' hours.') How many partitions I should use for installing debian with zfs ? When installing Debian with ZFS, it is recommended to use at least four partitions. These partitions should include a boot partition, a partition for swap or memory, a root partition, and a partition for any additional data. Additionally, it is also recommended to use a separate partition for each ZFS pool that you wish to create. This will ensure that each pool has its own independent space and will prevent any data corruption.