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Register a new userBlade Runner -What kind objects are there in the JVO ALMA Archive?-
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The JVO ALMA Archive provides users one of the easiest ways to access the ALMA archival data. The users can have a quick look at a 3 or 4-dimensional data cube without downloading multiple huge tarballs from a science portal of ALMA Regional Centers (ARCs). Since we just synchronize all datasets with those of ARCs, the metadata are identical to the upstream, including ``target name for each dataset. The name is not necessarily a common one like NGC numbers, but sometimes one of sequential numbers assigned in an observation proposal. Compilation of these artificial names into astronomical ones could provide users more flexible and powerful search interfaces; for instance, with the knowledge of the redshift for each source, the users can easily find the datasets which observed their interested emission/absorption lines at not the observer frame but the rest frame, fitting well with theoretical studies. To implement this functionality, cross-identification of all the sources in our archive with those in some other astronomical databases such as NED and SIMBAD is required. We developed a tiny Java application named ``Blade Runner for this purpose. The program works as a crawler for both the JVO ALMA Archive and SIMBAD, storing all information onto a SQLite-based database file; this portable design enables us to communicate results to each other even under different computing environments. In this paper, we introduce its software design and our recent work on the application, and report a preliminary result on the source identification in our archive.
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ALMA products are stored in the Science Archive in the form of FITS images. It is a common idea that the FITS image headers should collect in their keywords all the information that an archive User might want to search for in order to quickly select, compare, or discard datasets. With this perspective in mind, we first present a short description of the current status of the ALMA FITS archive and images. We realized that at the moment most of the parameters that could be useful for a general User are still missing in the archived data. We then provide a CASA task generating the image header keywords that we suggest to be relevant for the scientific exploitation of the ALMA archival data. The proposed tool could be also applied to several types of interferometer data and part of it is implemented in a web interface. An example of the scientific application of the keywords is also discussed.
In December 2016, the Atacama Large Millimeter/submillimeter Array (ALMA) carried out the first regular observations of the Sun. These early observations and the reduction of the respective data posed a challenge due to the novelty and complexity of observing the Sun with ALMA. The difficulties with producing science-ready time-resolved imaging products in a format familiar and usable by solar physicists based on the measurement sets delivered by ALMA had so far limited the availability of such data. With the development of the Solar ALMA Pipeline (SoAP), it has now become possible to routinely reduce such data sets. As a result, a growing number of science-ready solar ALMA datasets is now offered in the form of Solar ALMA Science Archive (SALSA). So far, SALSA contains primarily time series of single-pointing interferometric images at cadences of one or two seconds. The data arrays are provided in FITS format. We also present the first version of a standardised header format that accommodates future expansions and fits within the scope of other standards including the ALMA Science Archive itself and SOLARNET. The headers also include information designed to aid the reproduction of the imaging products from the raw data. Links to co-observations, if available, with a focus on those of the Interface Region Imaging Spectrograph (IRIS), are also provided. SALSA is accompanied by the Solar ALMA Library of Auxiliary Tools (SALAT) that contains IDL and Python routines for convenient loading and quick-look analysis of SALSA data.
The interstellar medium (ISM) is subject, on one hand, to heating and cooling processes that tend to segregate it into distinct phases due to thermal instability (TI), and on the other, to turbulence-driving mechanisms that tend to produce strong nonlinear fluctuations in all the thermodynamic variables. In this regime, large-scale turbulent compressions in the stable warm neutral medium (WNM) dominate the clump-formation process rather than the linear developent of TI. Cold clumps formed by this mechanism are often bounded by sharp density and temperature discontinuities, which however are not contact discontinuities as in the classical 2-phase model, but rather phase transition fronts, across which there is net mass and momentum flux from the WNM into the clumps. The clumps grow mainly by accretion through their boundaries, are in both thermal and ram pressure balance with their surroundings, and are internally turbulent as well, thus also having significant density fluctuations inside. The temperature and density of the cold and warm gas around the phase transition fronts fluctuate with time and location due to fluctuations in the turbulent pressure. Moreover, shock-compressed diffuse unstable gas can remain in the unstable regime for up to a few Myr before it undergoes a phase transition to the cold phase. These processes populate the classically forbidden density and temperature ranges. Since gas at all temperatures appears to be present in bi- or tri-stable turbulence, we conclude that the word phase applies only locally, surrounding phase transition sites in the gas. Globally, the word phase must relax its meaning to simply denote a certain temperature or density range.
The Additional Representative Images for Legacy (ARI-L) project is a European Development project for ALMA Upgrade approved by the Joint ALMA Observatory (JAO) and the European Southern Observatory (ESO), started in June 2019. It aims to increase the legacy value of the ALMA Science Archive (ASA) by bringing the reduction level of ALMA data from Cycles 2-4 close to that of data from more recent Cycles processed for imaging with the ALMA Pipeline. As of mid-2021 more than 150000 images have been returned to the ASA for public use. At its completion in 2022, the project will have provided enhanced products for at least 70% of the observational data from Cycles 2-4 processable with the ALMA Pipeline. In this paper we present the project rationale, its implementation, and the new opportunities offered to ASA users by the ARI-L products. The ARI-L cubes and images complement the much limited number of archival image products generated during the data quality assurance stages (QA2), which cover only a small fraction of the available data for those Cycles. ARI-L imaging products are highly relevant for many science cases and significantly enhance the possibilities for exploiting archival data. Indeed, ARI-L products facilitate archive access and data usage for science purposes even for non-expert data miners, provide a homogeneous view of all data for better dataset comparisons and download selections, make the archive more accessible to visualization and analysis tools, and enable the generation of preview images and plots similar to those possible for subsequent Cycles.
Computer science has grown rapidly since its inception in the 1950s and the pioneers in the field are celebrated annually by the A.M. Turing Award. In this paper, we attempt to shed light on the path to influential computer scientists by examining the characteristics of the 72 Turing Award laureates. To achieve this goal, we build a comprehensive dataset of the Turing Award laureates and analyze their characteristics, including their personal information, family background, academic background, and industry experience. The FP-Growth algorithm is used for frequent feature mining. Logistic regression plot, pie chart, word cloud and map are generated accordingly for each of the interesting features to uncover insights regarding personal factors that drive influential work in the field of computer science. In particular, we show that the Turing Award laureates are most commonly white, male, married, United States citizen, and received a PhD degree. Our results also show that the age at which the laureate won the award increases over the years; most of the Turing Award laureates did not major in computer science; birth order is strongly related to the winners success; and the number of citations is not as important as one would expect.
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