Set of samples of astronomical data gathered by 7 different surveys: Calar Alto Legacy Integral Field Area Survey (CALIFA), Herschel Multi-tiered Extragalactic Survey (HerMES), Low Frequency ARray (LOFAR), Sloan Digital Sky Survey's Baryon Oscillation Spectroscopic Survey (SDSS_BOSS), Sloan Digital Sky Survey's Mapping Nearby Galaxies at Apache Point Observatory (SDSS_MaNGA), VIsible MultiObject Spectrograph (VIMOS) and Wide-Field Infrared Survey Explorer (WISE) Universitat Autònoma de Barcelona. Group on Interactive Coding of Images (GICI). Description of methods used for collection-generation of data: Data obtained from observatory data archive website CALIFA: CALIFA DR3 data archive https://califa.caha.es/FTP-PUB/reduced/V500/reduced_v2.2/ - HerMES: Herschel data base in Marseille https://hedam.lam.fr/HerMES/download_files.html - LOFAR: LoTSS Data Release 2 (DR2) https://lofar-surveys.org/dr2_release.html - SDSS_BOSS: SDSS DR9 Science Archive Server (SAS) https://dr9.sdss.org/fields - SDSS_MaNGA: SDSS data access https://www.sdss4.org/dr17/manga/manga-data/data-access/ - VIMOS: ESO ESO Science Archive Facility (Raw Data) http://archive.eso.org/eso/eso_archive_main.html - WISE: NASA/IPAC infgrared science archive - WISE Image Service https://irsa.ipac.caltech.edu/applications/wise/?_action=layout.showDropDown&. Methods for processing the data - Data was converted from FITS format to raw using python library astropy, separating the FITS multiple extension into different .raw files. Instrument- or software- specific information needed to interpret the data: imagej or fiji. Telescope diameters: CALIFA: Calar Alto 3.5 m telescope - HerMES: Far Infrared and Submilimetre Telescope (FIRST) 3.5 m - LOFAR: 70,000 LOFAR antennas spread across Europe, with the majority in the Netherlands. Antennas located in the Netherlands are combined and a virtual telescope is created with a collecting surface of about 120 kilometers in diameter. SDSS_BOSS: 2.5 m wide-angle optical telescope, SDSS_MaNGA: 2.5 m wide-angle optical telescope, VIMOS: Melipal (UT3) 8.2 m, WISE: 40 cm infrared telescope. Quality-assurance procedures performed on the data: Images pixel value differences are zero compared to original FITS, Images are checked for good S/N ratio and no lens, atmospehric or CCD distortions.

This data is the gray scale, number and apparent magnitude data of stars observed by KAI-4021 on the ground. 1. StarMap1 refers to the data when the optical axis points to point to 101.636°for the right ascension, 31.054°for the declination. 2. StarMap2 refers to the data when the optical axis points to point to 106.778°for the right ascension, 31.068°for the declination. 3. StarMap3 refers to the data when the optical axis points to point to 112.501°for the right ascension, 31.069°for the declination. 4. StarMapi.track_num refers to the number of navigation stars in the image. 5. StarMapi.Mag refers to the apparent magnitude. 6. StarMapi.win_info refers to the 8*8 gray data of each star in the image. 7. StarMapi.ID_frm refers refers to the number of each star in our catalogue.

Set of samples of astronomical data gathered by 7 different surveys: Calar Alto Legacy Integral Field Area Survey (CALIFA), Herschel Multi-tiered Extragalactic Survey (HerMES), Low Frequency ARray (LOFAR), Sloan Digital Sky Survey's Baryon Oscillation Spectroscopic Survey (SDSS_BOSS), Sloan Digital Sky Survey's Mapping Nearby Galaxies at Apache Point Observatory (SDSS_MaNGA), VIsible MultiObject Spectrograph (VIMOS) and Wide-Field Infrared Survey Explorer (WISE)

The IRAS Point Source Catalog, Version 2.0, is a catalog of some 250,000 well-confirmed infrared point sources observed by the Infrared Astronomical Satellite (IRAS), i.e., sources with angular extents less than approximately 0.5, 0.5, 1.0, and 2.0 arcminutes in the in-scan direction at 12, 25, 60, and 100 microns (µm), respectively. Positions, flux densities, uncertainties, associations with known astronomical objects and various cautionary flags are given for each objectin the catalog. Away from confused regions of the sky, the survey is complete to about 0.4, 0.5, 0.6, and 1.0 Janskies (Jy) at 12, 25, 60, and 100 microns, respectively. Typical position uncertainties are about 2 to 6 arcseconds in the in-scan direction and about 8 to 16 arcseconds in the cross-scan direction. This online version of the IRASPSC was created by the HEASARC in April 2002 based on ADC/CDS Catalog II/125 (the main file). This is a service provided by NASA HEASARC .

This paper considers the problem of change detection using local distributed eigen monitoring algorithms for next generation of astronomy petascale data pipelines such as the Large Synoptic Survey Telescopes (LSST). This telescope will take repeat images of the night sky every 20 seconds, thereby generating 30 terabytes of calibrated imagery every night that will need to be coanalyzed with other astronomical data stored at different locations around the world. Change point detection and event classification in such data sets may provide useful insights to unique astronomical phenomenon displaying astrophysically significant variations: quasars, supernovae, variable stars, and potentially hazardous asteroids. However, performing such data mining tasks is a challenging problem for such high-throughput distributed data streams. In this paper we propose a highly scalable and distributed asynchronous algorithm for monitoring the principal components (PC) of such dynamic data streams. We demonstrate the algorithm on a large set of distributed astronomical data to accomplish well-known astronomy tasks such as measuring variations in the fundamental plane of galaxy parameters. The proposed algorithm is provably correct (i.e. converges to the correct PCs without centralizing any data) and can seamlessly handle changes to the data or the network. Real experiments performed on Sloan Digital Sky Survey (SDSS) catalogue data show the effectiveness of the algorithm.

Scientists often must simultaneously localize and discover signals. For instance, in genetic fine-mapping, high correlations between nearby genetic variants make it hard to identify the exact locations of causal variants. So the statistical task is to output as many disjoint regions containing a signal as possible, each as small as possible, while controlling false positives. Similar problems arise, for example, when locating stars in astronomical surveys and in changepoint detection. Common Bayesian approaches to these problems involve computing a posterior distribution over signal locations. However, existing procedures to translate these posteriors into credible regions for the signals fail to capture all the information in the posterior, leading to lower power and (sometimes) inflated false discoveries. We introduce Bayesian Linear Programming (BLiP), which can efficiently convert any posterior distribution over signals into credible regions for signals. BLiP overcomes an extremely high-dimensional and nonconvex problem to verifiably nearly maximize expected power while controlling false positives. Applying BLiP to existing state-of-the-art analyses of UK Biobank data (for genetic fine-mapping) and the Sloan Digital Sky Survey (for astronomical point source detection) increased power by 30%–120% in just a few minutes of additional computation. BLiP is implemented in pyblip (Python) and blipr (R). Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.

The Point Source Infrared Sky Model of Wainscoat et al. [ApJS, 83, 111 (1992)] is extended to make predictions for any filter lying wholly within the range 2.0 to 35.0 microns. The development of a library of complete 2-35 micron low-resolution spectra (with 0.1 micron step size) that represent the 87 categories of Galactic object and four types of extragalactic source implicit in the Model supports this extension. This library is based upon the "spectral template" technique whereby existing spectral fragments for individual sources (from ground-based, airborne, and satellite-borne instruments) are combined into complete spectra. Templates provide a natural way to represent the complete spectral energy distributions of celestial sources for which only infrared photometry and/or partial spectrosopy are available. Consequently, templates bear upon the important general problem of establishing midinfrared calibration sources. The new Model is validated by comparison with broadband K (2.2 micron) source counts., The Point Source Infrared Sky Model of Wainscoat et al. [ApJS, 83, 111 (1992)] is extended to make predictions for any filter lying wholly within the range 2.0 to 35.0 microns. The development of a library of complete 2-35 micron low-resolution spectra (with 0.1 micron step size) that represent the 87 categories of Galactic object and four types of extragalactic source implicit in the Model supports this extension. This library is based upon the "spectral template" technique whereby existing spectral fragments for individual sources (from ground-based, airborne, and satellite-borne instruments) are combined into complete spectra. Templates provide a natural way to represent the complete spectral energy distributions of celestial sources for which only infrared photometry and/or partial spectrosopy are available. Consequently, templates bear upon the important general problem of establishing midinfrared calibration sources. The new Model is validated by comparison with broadband K (2.2 micron) source counts.

No description is available. Visit https://dataone.org/datasets/e7cb63ce7dc39bf9953720195cc90323 for complete metadata about this dataset.

In this paper we continue our investigation on the isophotal nature, accuracy, and uniformity of the magnitude system adopted in the Southern Sky Redshift Survey extension (SSRS2). Extending our earlier work, we examine galaxies in the equatorial region, primarily in the declination range -17.5 deg <= Dec. <= 0 deg, over a large range of right ascension, covering the southern and northern Galactic caps. For this purpose, we have obtained CCD isophotal magnitudes in the B and R bands for 265 galaxies of different morphological types. Using this larger sample we confirm our earlier claim that the m(SSRS2) magnitudes are very nearly the magnitude measured within the isophote mu_B = 26 mag/arcsec^2, with a dispersion of about 0.30 mag. The relative zero-point offset between our m(SSRS2) magnitudes and CCD photometry is -0.02 mag from all data we have obtained. However, we detect a variation of the zero-point across different regions of the sky of +/- 0.10 mag for regions at large angular separations. We also estimate that the zero-point offset between the m(SSRS2) and Zwicky systems is relatively small (~0.10 mag), which should allow us to combine the data from the SSRS2 and the CfA2 Redshift Survey., In this paper we continue our investigation on the isophotal nature, accuracy, and uniformity of the magnitude system adopted in the Southern Sky Redshift Survey extension (SSRS2). Extending our earlier work, we examine galaxies in the equatorial region, primarily in the declination range -17.5 deg <= Dec. <= 0 deg, over a large range of right ascension, covering the southern and northern Galactic caps. For this purpose, we have obtained CCD isophotal magnitudes in the B and R bands for 265 galaxies of different morphological types. Using this larger sample we confirm our earlier claim that the m(SSRS2) magnitudes are very nearly the magnitude measured within the isophote mu_B = 26 mag/arcsec^2, with a dispersion of about 0.30 mag. The relative zero-point offset between our m(SSRS2) magnitudes and CCD photometry is -0.02 mag from all data we have obtained. However, we detect a variation of the zero-point across different regions of the sky of +/- 0.10 mag for regions at large angular separations. We also estimate that the zero-point offset between the m(SSRS2) and Zwicky systems is relatively small (~0.10 mag), which should allow us to combine the data from the SSRS2 and the CfA2 Redshift Survey.

This table contains the main catalog from Version 2.3 of the Midcourse Space Experiment (MSX) Point Source Catalog (PSC), which supersedes the previous version (1.2) that was released in 1999, and contains 100,000 more sources than the latter. The MSX PSC main catalog used to create this Browse table contains all the sources found in the Galactic Plane survey, and the primary high-latitude regions (the IRAS gaps regions, and the Large Magellanic Cloud). Note that this HEASARC table does not contain the MSX PSC supplementary catalogs, viz. the singleton catalog, the low-reliability catalog, or the minicatalogs for 19 selected regions. The principal objective of the astronomy experiments onboard the MSX satellite was to complete the census of the mid-infrared (4.2-25 micron or um) sky: namely, the areas missed by the IRAS mission (about 4% of the sky was not surveyed by IRAS), and the Galactic Plane (where the sensitivity of IRAS was degraded by confusion noise in regions of high source densities or of structured extended emission). The photometry is based on co-added image plates, as opposed to single-scan data, which results in improved sensitivity and hence reliability in the fluxes. Comparison with Tycho-2 positions indicates that the astrometric accuracy of the new catalog is more than 1" better than that in Version 1.2. The infrared instrument on MSX was named SPIRIT III; it was a 35-cm clear aperture off-axis telescope with five line scanned infrared focal-plane arrays of 18.3 arcseconds square pixels, with a high sensitivity (0.1 Jy at 8.3 um). The filter characteristics of the 6 spectral bands B1, B2, A, C, D and E are summarized below, where all wavelengths are in micron (µm):

 Band Center FWHM Points ---------------------------- B1 4.29 um 4.22 - 4.36 um B2 4.35 4.24 - 4.45 A 8.28 6.8 - 10.8 C 12.13 11.1 - 13.2 D 14.65 13.5 - 15.9 E 21.34 18.2 - 25.1 

The MSX catalog names of the sources have been defined according to International Astronomical Union (IAU) conventions with a unique identifier combined with the position of the source. In this case, the MSX PSC V2.3 sources are named using the convention MSX6C GLLL.llll+/-BB.bbbb, where MSX6C denotes that this is MSX data run using Version 6.0 of the CONVERT software, and GLLL.llll+/-BB.bbbb gives the Galactic coordinates of the source. This database table was first created by the HEASARC in November 2002 and then updated in April 2005, based on the 11-Dec-2003 version of the CDS Catalog V/114 (specifically, the files gb_gt6.dat, gp_m05m2.dat, gp_m2m6.dat, gp_p05p2.dat, gp_p2p6.dat, and gp_pm05.dat which comprise the main catalog). This is a service provided by NASA HEASARC .

The refractor astronomical telescope market, while a niche segment within the broader astronomical equipment industry, exhibits steady growth driven by several key factors. Increased interest in amateur astronomy, fueled by accessible online resources and educational initiatives, is a primary driver. The relative ease of use and maintenance of refractor telescopes compared to reflectors contributes to their popularity among beginners and hobbyists. Technological advancements, such as improved lens coatings and enhanced optical designs, are leading to higher-quality images and improved performance, further stimulating market demand. The market is segmented by aperture size (e.g., 60mm, 80mm, 102mm, etc.), focal length, and intended use (e.g., planetary observation, deep-sky observation, astrophotography). Established brands like Celestron, Meade Instruments, and Sky-Watcher dominate the market, benefiting from strong brand recognition and established distribution networks. However, smaller manufacturers and online retailers are also gaining traction, offering competitive pricing and specialized products. While the market faces challenges such as competition from other telescope types (reflectors and catadioptrics) and the price sensitivity of some consumer segments, the overall growth trajectory remains positive, projected to maintain a healthy Compound Annual Growth Rate (CAGR) over the forecast period. The projected market size for 2025 is estimated at $250 million, based on reasonable industry estimations considering the niche nature of the market and the available data points from similar astronomy equipment sectors. The CAGR, assuming a conservative estimate given the factors mentioned above, is likely around 5% for the forecast period (2025-2033). This growth is largely attributed to sustained interest in amateur astronomy and technological improvements in refractor telescope design and manufacturing. Regional market share will vary depending on factors like economic development, levels of scientific literacy, and accessibility to astronomical resources. North America and Europe are expected to hold significant shares, while other regions, like Asia-Pacific, will show increasing growth potential. The presence of established players along with emerging smaller brands will ensure that the market will continue to be relatively competitive, leading to further innovation and price adjustments.

Supplementary material to DESI's publication 'The DESI BHB view of the MW-LMC interaction' to comply with the data management plan. Paper prepared for submission to Monthly Notices of the Royal Astronomical Society.

Directory contains data points for all figures with file names indicating the figure to which the data corresponds. Data for 20 figures in 58 .fits files included in the .zip directory.

We report on Planetary Camera observations of the central region of 30 Doradus in the Large Magellanic Cloud. These images of 30 Doradus are the first "deep" HST exposures that have appropriate photometric calibration. The B band (F439W) image, which shows R136a at the center of the PC6 CCD chip, reveals over 200 stars within 3" of the center of R136a, and over 800 stars in a 35"x35" area. We used Malumuth et al.'s [The First Year of HST Observations, edited by A. L. Kinney and J. C. Blades (ST ScI, Baltimore) (1991)] PSF-fitting method to measure the magnitudes of all stars on the PC6 chip. These new B magnitudes, along with U and V magnitudes from archival PC images, yield a luminosity function, mass density profile, and initial mass function of the 30 Doradus ionizing cluster. The mass distribution is well fit by a King model with a core radius, Rc = 0.96" (0.24 pc), a tidal radius, Rt = 110" (28 pc), and a total mass, M = 16800 Msun. Both the luminosity function and initial mass function show evidence for mass segregation, in the sense that the central region has a higher fraction of massive stars than the outer regions. This is the first observational evidence for mass segregation in a very young cluster (age ~3 million years). The observations admit the hypothesis that the mass segregation occurred in the process of star formation and/or that the mass segregation is the result of dynamical evolution., We report on Planetary Camera observations of the central region of 30 Doradus in the Large Magellanic Cloud. These images of 30 Doradus are the first "deep" HST exposures that have appropriate photometric calibration. The B band (F439W) image, which shows R136a at the center of the PC6 CCD chip, reveals over 200 stars within 3" of the center of R136a, and over 800 stars in a 35"x35" area. We used Malumuth et al.'s [The First Year of HST Observations, edited by A. L. Kinney and J. C. Blades (ST ScI, Baltimore) (1991)] PSF-fitting method to measure the magnitudes of all stars on the PC6 chip. These new B magnitudes, along with U and V magnitudes from archival PC images, yield a luminosity function, mass density profile, and initial mass function of the 30 Doradus ionizing cluster. The mass distribution is well fit by a King model with a core radius, Rc = 0.96" (0.24 pc), a tidal radius, Rt = 110" (28 pc), and a total mass, M = 16800 Msun. Both the luminosity function and initial mass function show evidence for mass segregation, in the sense that the central region has a higher fraction of massive stars than the outer regions. This is the first observational evidence for mass segregation in a very young cluster (age ~3 million years). The observations admit the hypothesis that the mass segregation occurred in the process of star formation and/or that the mass segregation is the result of dynamical evolution.

The Faint Source Survey (FSS) is the definitive Infrared Astronomical Satellite data set for faint point sources. The FSS was produced by point-source filtering the individual detector data streams and then coadding those data streams using a trimmed-average algorithm. The resulting images, or plates, give the best estimate from the IRAS survey data of the point source flux density at every surveyed point of the sky. The Faint Source Catalog (FSC) is a compilation of the sources extracted from the FSS plates that have met reasonable reliability requirements. Averaged over the whole catalog, the FSC is at least 98.5% reliable at 12 and 25 microns, and ~94% at 60 microns. For comparison, the IRAS Point Source Catalog (PSC) is >99.997% reliable, but the sensitivity of the FSC exceeds that of the PSC by about a factor of 2.5. The FSC contains data for 173,044 point sources in unconfused regions with flux densities typically above 0.2 Jy at 12, 25, and 60 microns, and above 1.0 Jy at 100 microns. The FSS plates are somewhat more sensitive but less reliable than the FSC; typically, only sources with SNR>5-6 in the plates are contained in the FSC. Sources with SNR>3 but which do not meet the reliability requirements of the FSC are catalogued in the Faint Source Reject File (FSR, Cat. II/275). The data products, the processing methods used to produce them, results of an analysis of these products, and cautionary notes are given in the Explanatory Supplement to the IRAS Faint Source Survey (see references in fsc.txt). Cone search capability for table II/156A/main (IRAS Faint Sources)

The Faint Source Survey (FSS) is the definitive Infrared Astronomical Satellite (IRAS) data set for faint point sources. The FSS was produced by point-source filtering the individual detector data streams and then coadding those data streams using a trimmed-average algorithm. The resulting images, or plates, give the best estimate from the IRAS survey data of the point source flux density at every surveyed point of the sky. The Faint Source Catalog (FSC) is a compilation of the sources extracted from the FSS plates that have met reasonable reliability requirements. Averaged over the whole catalog, the FSC is at least 98.5% reliable at 12 and 25 microns, and ~94% at 60 microns. For comparison, the IRAS Point Source Catalog (PSC) is >99.997% reliable, but the sensitivity of the FSC exceeds that of the PSC by about a factor of 2.5. This increase in sensitivity results from the co-adding of the three separate hours-confirming (HCON) passes over the sky which were used for confirmation and not added together for the Point Source Catalog. The FSC also contains 99,973 infrared sources which are not in the PSC. The FSC contains data for 173,044 point sources in unconfused regions with flux densities typically above 0.2 Jy at 12, 25, and 60 microns, and above 1.0 Jy at 100 microns. The FSS plates are somewhat more sensitive but less reliable than the FSC; typically, only sources with SNR > 5 - 6 in the plates are contained in the FSC. The data products, the processing methods used to produce them, results of an analysis of these products, and cautionary notes are given in the Explanatory Supplement to the IRAS Faint Source Survey. This database table contains the IRAS Faint Source Catalog (FSC) (Version 2.0, released in September 1990) non-associations data. The associations data for the IRAS FSC is contained in the file https://cdsarc.cds.unistra.fr/ftp/cats/II/156A/assoc.dat.gz The FSC is limited in galactic latitude to the unconfused regions of sky in which the absolute value of BII is greater than or equal to 10 degrees at 12 and 25 microns and greater than or equal to 20 degrees at 60 microns. Because of the presence of the infrared "cirrus" at 100 microns, the FSC does not contain sources detected ONLY at 100 microns. Sources with a 100 micron detection were included in the catalog if they were bandmerged with high reliability detections at other spectral bands. For the faintest sources, the reliability exceeds 90% at 12 and 25 microns, and 80% at 60 microns. The HEASARC recreated this database table in August 2005, based on the CDS table, in an effort to modernize its parameter names and documentation, as well as to add Galactic coordinates. This is a service provided by NASA HEASARC .

The Extreme Ultraviolet Explorer (EUVE) has conducted an all-sky survey to locate and identify point sources of emission in four extreme ultraviolet wavelength bands centered at approximately 100, 200, 400, and 600 A. A companion deep survey of a strip along half the ecliptic plane was simultaneously conducted. In this catalog we report the sources found in these surveys using rigorously defined criteria uniformly applied to the data set. These are the first surveys to be made in the three longer wavelength bands, and a substantial number of sources were detected in these bands. We present a number of statistical diagnostics of the surveys, including their source counts, their sensitivities, and their positional error distributions. We provide a separate list of those sources reported in the EUVE Bright Source List which did not meet our criteria for inclusion in our primary list. We also provide improved count rate and position estimates for a majority of these sources based on the improved methodology used in this paper. In total, this catalog lists a total of 410 point sources, for which 372 have plausible optical, ultraviolet, or X-ray identifications, which are also listed., The Extreme Ultraviolet Explorer (EUVE) has conducted an all-sky survey to locate and identify point sources of emission in four extreme ultraviolet wavelength bands centered at approximately 100, 200, 400, and 600 A. A companion deep survey of a strip along half the ecliptic plane was simultaneously conducted. In this catalog we report the sources found in these surveys using rigorously defined criteria uniformly applied to the data set. These are the first surveys to be made in the three longer wavelength bands, and a substantial number of sources were detected in these bands. We present a number of statistical diagnostics of the surveys, including their source counts, their sensitivities, and their positional error distributions. We provide a separate list of those sources reported in the EUVE Bright Source List which did not meet our criteria for inclusion in our primary list. We also provide improved count rate and position estimates for a majority of these sources based on the improved methodology used in this paper. In total, this catalog lists a total of 410 point sources, for which 372 have plausible optical, ultraviolet, or X-ray identifications, which are also listed.

The data used in this paper is from the 16th issue of SDSS. SDSS-DR16 contains a total of 930,268 photometric images, with 1.2 billion observation sources and tens of millions of spectra. The data obtained in this paper is downloaded from the official website of SDSS. Specifically, the data is obtained through the SkyServerAPI structure by using SQL query statements in the subwebsite CasJobs. As the current SDSS photometric table PhotoObj can only classify all observed sources as point sources and surface sources, the target sources can be better classified as galaxies, stars and quasars through spectra. Therefore, we obtain calibrated sources in CasJobs by crossing SpecPhoto with the PhotoObj star list, and obtain target position information (right ascension and declination). Calibrated sources can tell them apart precisely and quickly. Each calibrated source is labeled with the parameter "Class" as "galaxy", "star", or "quasar". In this paper, observation day area 3462, 3478, 3530 and other 4 areas in SDSS-DR16 are selected as experimental data, because a large number of sources can be obtained in these areas to provide rich sample data for the experiment. For example, there are 9891 sources in the 3462-day area, including 2790 galactic sources, 2378 stellar sources and 4723 quasar sources. There are 3862 sources in the 3478 day area, including 1759 galactic sources, 577 stellar sources and 1526 quasar sources. FITS files are a commonly used data format in the astronomical community. By cross-matching the star list and FITS files in the local celestial region, we obtained images of 5 bands of u, g, r, i and z of 12499 galaxy sources, 16914 quasar sources and 16908 star sources as training and testing data.1.1 Image SynthesisSDSS photometric data includes photometric images of five bands u, g, r, i and z, and these photometric image data are respectively packaged in single-band format in FITS files. Images of different bands contain different information. Since the three bands g, r and i contain more feature information and less noise, Astronomical researchers typically use the g, r, and i bands corresponding to the R, G, and B channels of the image to synthesize photometric images. Generally, different bands cannot be directly synthesized. If three bands are directly synthesized, the image of different bands may not be aligned. Therefore, this paper adopts the RGB multi-band image synthesis software written by He Zhendong et al. to synthesize images in g, r and i bands. This method effectively avoids the problem that images in different bands cannot be aligned. The pixel of each photometry image in this paper is 2048×1489.1.2 Data tailoringThis paper first clipped the target image, image clipping can use image segmentation tools to solve this problem, this paper uses Python to achieve this process. In the process of clipping, we convert the right ascension and declination of the source in the star list into pixel coordinates on the photometric image through the coordinate conversion formula, and determine the specific position of the source through the pixel coordinates. The coordinates are regarded as the center point and clipping is carried out in the form of a rectangular box. We found that the input image size affects the experimental results. Therefore, according to the target size of the source, we selected three different cutting sizes, 40×40, 60×60 and 80×80 respectively. Through experiment and analysis, we find that convolutional neural network has better learning ability and higher accuracy for data with small image size. In the end, we chose to divide the surface source galaxies, point source quasars, and stars into 40×40 sizes.1.3 Division of training and test dataIn order to make the algorithm have more accurate recognition performance, we need enough image samples. The selection of training set, verification set and test set is an important factor affecting the final recognition accuracy. In this paper, the training set, verification set and test set are set according to the ratio of 8:1:1. The purpose of verification set is used to revise the algorithm, and the purpose of test set is used to evaluate the generalization ability of the final algorithm. Table 1 shows the specific data partitioning information. The total sample size is 34,000 source images, including 11543 galaxy sources, 11967 star sources, and 10490 quasar sources.1.4 Data preprocessingIn this experiment, the training set and test set can be used as the training and test input of the algorithm after data preprocessing. The data quantity and quality largely determine the recognition performance of the algorithm. The pre-processing of the training set and the test set are different. In the training set, we first perform vertical flip, horizontal flip and scale on the cropped image to enrich the data samples and enhance the generalization ability of the algorithm. Since the features in the celestial object source have the flip invariability, the labels of galaxies, stars and quasars will not change after rotation. In the test set, our preprocessing process is relatively simple compared with the training set. We carry out simple scaling processing on the input image and test input the obtained image.

This is the full dataset for the manuscript Astronomical calibration of the Ypresian timescale: implications for seafloor spreading rates and the chaotic behavior of the solar system? by Westerhold, T., Röhl, U., Frederichs, T., Agnini, C., Raffi, I., Zachos, J. C., and Wilkens, R. H. published in Climate of the Past, 13, 1129-1152, https://doi.org/10.5194/cp-13-1129-2017, 2017. It contains 48 tables with XRF core scanning data, bulk and benthic stable isotope data compiled, raw inclination-declination-intensity data, Paleomagnetic interpretation, magnetostratigraphy, calcareous nanofossil events, mapping pairs for correlation of different hole in a drill site, tie points to correlated between drill sites for ODP Sites 1258, 1262, 1263, 1265, 1267 (Tables S1-44). Tables S45 to 48 contain a combined magnetostratigraphy, a 405-kyr tuning age model, tie points for a detailed astronomical age model, and comparison of magnetochron durations.

the purpose of this proposal is to probe into the origin of a number of protostellar candidates isolated from molecular clouds. these sources were selected from the iras point source catalog as candidates for protostars, and their anticorrelation with molecular materials was found through a comparison of the iras data with our 13co(j10) map obtained for a search for molecular clouds in the cygnus region (80<l<104, 7.5<b<10.5 deg). at least in this region, the isolated sources are, to our surprise, majority of protostellar candidates, occupying ~70% of all the iras point sources selected as candidates for protostars. this may indicate that very compact clouds which might escape detection in our previous 13co survey are playing a leading role of star formation in our galaxy. on the other hand, these objects might be other astronomical objects such as galaxies or planetary nebulae, although most of their optical counterparts are absent on the palomar sky survey prints. nothing but the iras data is available for these sources at the moment, and all what is known about them is that they are accompanied by cold dust without apparent connection to molecular clouds. using sensitive cam equipped with iso, we propose to make deep imaging of the isolated protostellar candidates around 10 micron in order to investigate the distribution of the dust emission around them. we also propose to determine their spectra at far infrared wavelengths of 160 and 200 micron with isophot in order to estimate the dust temperature precisely. these data from iso are indespensible in order to investigate the properties of dust associated with these objects,which may give us a hint for their origin. all the observations can be carried out only by iso, since we cannot access to those wavelengths from the groundbased observations. truncated!, Please see actual data for full text [truncated!, Please see actual data for full text]

Version 2.3 of the Midcourse Space Experiment (MSX) Point Source Catalog (PSC), which supersedes the version (1.2) that was released in 1999 (Cat. V/107), contains over 100,000 more sources than the previous version. The photometry is based on co-added image plates, as opposed to single-scan data, which results in improved sensitivity and hence reliability in the fluxes. Comparison with Tycho-2 positions indicates that the astrometric accuracy of the new catalog is more than 1'' better than that in Version 1.2. In addition to the Galactic plane, Areas Missed by the Infrared Astronomical Satellite (IRAS), and the Large Magellanic Cloud, which were included in the previous catalog, Version 2.3 includes data from the Small Magellanic Cloud, eight nearby galaxies, and several molecular clouds and star forming regions. The infrared instrument on MSX was named SPIRIT III; it was a 35cm clear aperture off-axis telescope with five line scanned infrared focal plane arrays of 18.3arcsec square pixels, with a high sensitivity (0.1Jy at 8.3micron). The 6 bands are B1 (4.29micron, FWHM 4.22-4.36micron), B2 (4.25micron, 4.24-4.45micron), A (8.28micron, 6.8-10.8micron), C (12.13micron, 11.1-13.2micron), D (14.65micron, 13.5-15.9micron), and E (21.34micron, 18.2-25.1micron). The MSX catalog names of the sources have been defined according to International Astronomical Union (IAU) conventions with a unique identifier combined with the position of the source. In this case, the MSX PSC V2.3 sources are named using the convention MSX6C GLLL.llll+/-BB.bbbb, where MSX6C denotes that this is MSX data run using Version 6.0 of the CONVERT software, and GLLL.llll+/-BB.bbbb gives the Galactic coordinates of the source. (Names in the minicatalogs may differ slightly from those given in Kraemer et al. 2002AJ....124.2990K, 2003AJ....126.1423K) For ease of handling, the main catalog is broken into six files: five for the Galactic plane survey, plus the primary high latitude regions (the IRAS gaps and the LMC). The supplementary catalogs are the singleton catalog, the low-reliability catalog, and minicatalogs for 19 selected regions. All catalogs have the same format. However, the minicatalogs for the galaxies (except the SMC) and Orion do not have all the fields filled in because they were solely created from the images, not from the Point Source Extractor; there are no singleton files for these regions. Also, the minicatalogs may not have singleton or low-reliability counterparts if no sources met the inclusion criteria. All told, there are a total of 45 data files.