Complex organic molecules in the interstellar medium: IRAM 30 m line survey of Sagittarius B2(N) and (M)

The discovery of amino acids in meteorites and the detection of glycine in samples returned from a comet to Earth suggest that the interstellar chemistry is capable of producing such complex organic molecules. Our goal is to investigate the degree of chemical complexity that can be reached in the ISM. We performed an unbiased, spectral line survey toward Sgr B2(N) and (M) with the IRAM 30m telescope in the 3mm window. The spectra were analyzed with a simple radiative transfer model that assumes LTE but takes optical depth effects into account. About 3675 and 945 spectral lines with a peak signal-to-noise ratio higher than 4 are detected toward N and M, i.e. about 102 and 26 lines per GHz, respectively. This represents an increase by about a factor of 2 over previous surveys of Sgr B2. About 70% and 47% of the lines detected toward N and M are identified and assigned to 56 and 46 distinct molecules as well as to 66 and 54 less abundant isotopologues of these molecules, respectively. We also report the detection of transitions from 59 and 24 catalog entries corresponding to vibrationally or torsionally excited states of some of these molecules, respectively. Excitation temperatures and column densities were derived for each species but should be used with caution. Among the detected molecules, aminoacetonitrile, n-propyl cyanide, and ethyl formate were reported for the first time in space based on this survey, as were 5 rare isotopologues of vinyl cyanide, cyanoacetylene, and hydrogen cyanide. We also report the detection of transitions from within 12 new vib. or tors. excited states of known molecules. Although the large number of unidentified lines may still allow future identification of new molecules, we expect most of these lines to belong to vib. or tors. excited states or to rare isotopologues of known molecules for which spectroscopic predictions are currently missing. (abridged)Comment: Accepted for publication in A&A. 266 pages (39 pages of text), 111 tables, 8 figure

HeAT – a Distributed and GPU-accelerated Tensor Framework for Data Analytics

In order to cope with the exponential growth in available data, the efficiency of data analysis and machine learning libraries have recently received increased attention. Although corresponding array-based numerical kernels have been significantly improved, most are limited by the resources available on a single computational node. Consequently, kernels must exploit distributed resources, e.g., distributed memory architectures. To this end, we introduce HeAT, an array-based numerical programming framework for large-scale parallel processing with an easy-to-use NumPy-like API. HeAT utilizes PyTorch as a node-local eager execution engine and distributes the workload via MPI on arbitrarily large high-performance computing systems. It provides both low-level array-based computations, as well as assorted higher-level algorithms. With HeAT, it is possible for a NumPy user to take advantage of their available resources, significantly lowering the barrier to distributed data analysis. Compared with applications written in similar frameworks, HeAT achieves speedups of up to two orders of magnitude

HeAT -- a Distributed and GPU-accelerated Tensor Framework for Data Analytics

To cope with the rapid growth in available data, the efficiency of data analysis and machine learning libraries has recently received increased attention. Although great advancements have been made in traditional array-based computations, most are limited by the resources available on a single computation node. Consequently, novel approaches must be made to exploit distributed resources, e.g. distributed memory architectures. To this end, we introduce HeAT, an array-based numerical programming framework for large-scale parallel processing with an easy-to-use NumPy-like API. HeAT utilizes PyTorch as a node-local eager execution engine and distributes the workload on arbitrarily large high-performance computing systems via MPI. It provides both low-level array computations, as well as assorted higher-level algorithms. With HeAT, it is possible for a NumPy user to take full advantage of their available resources, significantly lowering the barrier to distributed data analysis. When compared to similar frameworks, HeAT achieves speedups of up to two orders of magnitude.Comment: 10 pages, 8 figures, 5 listings, 1 tabl

Rotational spectroscopy of isotopic vinyl cyanide, H2_2C=CH−-C≡\equivN, in the laboratory and in space

The rotational spectra of singly substituted $^{13}$C and $^{15}$N isotopic species of vinyl cyanide have been studied in natural abundances between 64 and 351 GHz. In combination with previous results, greatly improved spectroscopic parameters have been obtained which in turn helped to identify transitions of the $^{13}$C species for the first time in space through a molecular line survey of the extremely line-rich interstellar source Sagittarius B2(N) in the 3 mm region with some additional observations at 2 mm. The $^{13}$C species are detected in two compact ($\sim 2.3''$), hot (170 K) cores with a column density of $\sim 3.8 \times 10^{16}$ and $1.1 \times 10^{16}$ cm$^{-2}$, respectively. In the main source, the so-called ``Large Molecule Heimat'', we derive an abundance of $2.9 \times 10^{-9}$ for each $^{13}$C species relative to H$_2$. An isotopic ratio $^{12}$C/$^{13}$C of 21 has been measured. Based on a comparison to the column densities measured for the $^{13}$C species of ethyl cyanide also detected in this survey, it is suggested that the two hot cores of Sgr B2(N) are in different evolutionary stages. Supplementary laboratory data for the main isotopic species recorded between 92 and 342 GHz permitted an improvement of its spectroscopic parameters as well.Comment: 18 pages, including 2 tables, 3 figures; plus one supplementary text file plus one supplementary pdf file; J. Mol. Spectrosc., in press (to appear in the July or August issue of 2008

Herschel observations of EXtraordinary Sources: Analysis of the full Herschel/HIFI molecular line survey of Sagittarius B2(N)

A sensitive broadband molecular line survey of the Sagittarius B2(N) star-forming region has been obtained with the HIFI instrument on the Herschel Space Observatory, offering the first high-spectral resolution look at this well-studied source in a wavelength region largely inaccessible from the ground (625-157 um). From the roughly 8,000 spectral features in the survey, a total of 72 isotopologues arising from 44 different molecules have been identified, ranging from light hydrides to complex organics, and arising from a variety of environments from cold and diffuse to hot and dense gas. We present an LTE model to the spectral signatures of each molecule, constraining the source sizes for hot core species with complementary SMA interferometric observations, and assuming that molecules with related functional group composition are cospatial. For each molecule, a single model is given to fit all of the emission and absorption features of that species across the entire 480-1910 GHz spectral range, accounting for multiple temperature and velocity components when needed to describe the spectrum. As with other HIFI surveys toward massive star forming regions, methanol is found to contribute more integrated line intensity to the spectrum than any other species. We discuss the molecular abundances derived for the hot core, where the local thermodynamic equilibrium approximation is generally found to describe the spectrum well, in comparison to abundances derived for the same molecules in the Orion KL region from a similar HIFI survey.Comment: Accepted to ApJ. 64 pages, 14 figures. Truncated abstrac

The Helmholtz Analytics Toolkit (Heat) and its role in the landscape of massively-parallel scientific Python

When it comes to enhancing exploitation of massive data, machine learning methods are at the forefront of researchers’ awareness. Much less so is the need for, and the complexity of, applying these techniques efficiently across large-scale, memory-distributed data volumes. In fact, these aspects typical for the handling of massive data sets pose major challenges to the vast majority of research communities, in particular to those without a background in high-performance computing. Often, the standard approach involves breaking up and analyzing data in smaller chunks; this can be inefficient and prone to errors, and sometimes it might be inappropriate at all because the context of the overall data set can get lost. The Helmholtz Analytics Toolkit (Heat) library offers a solution to this problem by providing memory-distributed and hardware-accelerated array manipulation, data analytics, and machine learning algorithms in Python. The main objective is to make memory-intensive data analysis possible across various fields of research ---in particular for domain scientists being non-experts in traditional high-performance computing who nevertheless need to tackle data analytics problems going beyond the capabilities of a single workstation. The development of this interdisciplinary, general-purpose, and open-source scientific Python library started in 2018 and is based on collaboration of three institutions (German Aerospace Center DLR, Forschungszentrum Jülich FZJ, Karlsruhe Institute of Technology KIT) of the Helmholtz Association. The pillars of its development are... - ...to enable memory distribution of n-dimensional arrays, - to adopt PyTorch as process-local compute engine (hence supporting GPU-acceleration), - to provide memory-distributed (i.e., multi-node, multi-GPU) array operations and algorithms, optimizing asynchronous MPI-communication (based on mpi4py) under the hood, and - to wrap functionalities in NumPy- or scikit-learn-like API to achieve porting of existing applications with minimal changes and to enable the usage by non-experts in HPC. In this talk we will give an illustrative overview on the current features and capabilities of our library. Moreover, we will discuss its role in the existing ecosystem of distributed computing in Python, and we will address technical and operational challenges in further development