scholarly journals Quantum-assisted telescope arrays

2019 ◽  
Vol 100 (2) ◽  
Author(s):  
E. T. Khabiboulline ◽  
J. Borregaard ◽  
K. De Greve ◽  
M. D. Lukin
Keyword(s):  
2018 ◽  
Vol 619 ◽  
pp. A140 ◽  
Author(s):  
A. Vigorito ◽  
C. Calabrese ◽  
S. Melandri ◽  
A. Caracciolo ◽  
S. Mariotti ◽  
...  

Context. The continuously enhanced sensitivity of radioastronomical observations allows the detection of increasingly complex organic molecules. These systems often exist in a large number of isomers leading to very congested spectra. Aims. We explore the conformational space of 1,2-butanediol and provide sets of spectroscopic parameters to facilitate searches for this molecule at millimeter wavelengths. Methods. We recorded the rotational spectrum of 1,2-butanediol in the 59.6–103.6 GHz frequency region (5.03–2.89 mm) using a free-jet millimeter-wave absorption spectrometer, and we analyzed the properties of 24 isomers with quantum chemical calculations. Selected measured transition lines were then searched on publicly available ALMA Band 3 data on IRAS 16293-2422 B. Results. We assigned the spectra of six conformers, namely aG′Ag, gG′Aa, g′G′Ag, aG′G′g, aG′Gg, and g′GAa, to yield the rotational constants and centrifugal distortion constants up to the fourth or sixth order. The most intense signal belong to the aG′Ag species, that is the global minimum. Search for the corresponding 30x,30 − 29x,29 transition lines toward IRAS 16293-2422 B was unsuccessful. Conclusions. Our present data will be helpful for identifying 1,2-butanediol at millimeter wavelengths with radio telescope arrays. Among all possible conformers, first searches should be focused on the aG′Ag conformers in the 400–800 GHz frequency spectral range.


2000 ◽  
pp. 444-458
Author(s):  
DANIEL J. SCHROEDER
Keyword(s):  

2019 ◽  
Vol 621 ◽  
pp. A114 ◽  
Author(s):  
Olena Zakharenko ◽  
Frank Lewen ◽  
Vadim V. Ilyushin ◽  
Maria N. Drozdovskaya ◽  
Jes K. Jørgensen ◽  
...  

Methyl mercaptan (also known as methanethiol), CH3SH, has been found in the warm and dense parts of high- as well as low- mass star-forming regions. The aim of the present study is to obtain accurate spectroscopic parameters of the S-deuterated methyl mercaptan CH3SD to facilitate astronomical observations by radio telescope arrays at (sub)millimeter wavelengths. We have measured the rotational spectrum associated with the large-amplitude internal rotation of the methyl group of methyl mercaptan using an isotopically enriched sample in the 150−510 GHz frequency range using the Köln millimeter wave spectrometer. The analysis of the spectra has been performed up to the second excited torsional state. We present modeling results of these data with the RAM36 program. CH3SD was searched for, but not detected, in data from the Atacama Large Millimeter/submillimeter Array (ALMA) Protostellar Interferometric Line Survey (PILS) of the deeply embedded protostar IRAS 16293−2422. The derived upper limit corresponds to a degree of deuteration of at most ∼18%.


2020 ◽  
Vol 499 (1) ◽  
pp. 379-390
Author(s):  
Alireza Vafaei Sadr ◽  
Bruce A Bassett ◽  
Nadeem Oozeer ◽  
Yabebal Fantaye ◽  
Chris Finlay

ABSTRACT Flagging of Radio Frequency Interference (RFI) in time–frequency visibility data is an increasingly important challenge in radio astronomy. We present R-Net, a deep convolutional ResNet architecture that significantly outperforms existing algorithms – including the default MeerKAT RFI flagger, and deep U-Net architectures – across all metrics including AUC, F1-score, and MCC. We demonstrate the robustness of this improvement on both single dish and interferometric simulations and, using transfer learning, on real data. Our R-Net model’s precision is approximately $90{{\ \rm per\ cent}}$ better than the current MeerKAT flagger at $80{{\ \rm per\ cent}}$ recall and has a 35 per cent higher F1-score with no additional performance cost. We further highlight the effectiveness of transfer learning from a model initially trained on simulated MeerKAT data and fine-tuned on real, human-flagged, KAT-7 data. Despite the wide differences in the nature of the two telescope arrays, the model achieves an AUC of 0.91, while the best model without transfer learning only reaches an AUC of 0.67. We consider the use of phase information in our models but find that without calibration the phase adds almost no extra information relative to amplitude data only. Our results strongly suggest that deep learning on simulations, boosted by transfer learning on real data, will likely play a key role in the future of RFI flagging of radio astronomy data.


2020 ◽  
Vol 123 ◽  
pp. 102491
Author(s):  
M. Holler ◽  
J.-P. Lenain ◽  
M. de Naurois ◽  
R. Rauth ◽  
D.A. Sanchez

Author(s):  
Daniel Gottesman ◽  
Thomas Jennewein ◽  
Sarah Croke ◽  
Latham Boyle

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