ORCID Profile
0000-0001-8282-1635
Current Organisations
University of Manchester
,
Square Kilometre Array Organisation
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Publisher: Oxford University Press (OUP)
Date: 04-2021
Abstract: We report extremely faint 144 MHz radio emission from two gravitationally lensed quasars, SDSS J1004+4112 (z = 1.730) and SDSS J2222+2745 (z = 2.803), using the LOFAR Two Metre Sky Survey (LoTSS) data release 2. After correcting for the lensing magnifications, the two objects have intrinsic flux-densities of 13 ± 2 and 58 ± 6 μJy, respectively, corresponding to 144 MHz rest-frame luminosities of 1023.2 ± 0.2 and 1024.42 ± 0.05 W Hz−1, respectively. In the case of SDSS J1004+4112, the intrinsic flux density is close to the confusion limit of LoTSS, making this radio source the faintest to be detected thus far at low frequencies, and the lowest luminosity known at z ≳ 0.65. Under the assumption that all of the radio emission is due to star-formation processes, the quasar host galaxies are predicted to have star-formation rates of $5.5^{+1.8}_{-1.4}$ and $73^{+34}_{-22}$ M⊙ yr−1, respectively. Further multiwavelength observations at higher angular resolution will be needed to determine if any of the detected radio emission is due to weak jets associated with the quasars.
Publisher: Oxford University Press (OUP)
Date: 30-05-2023
Abstract: The Square Kilometre Array Observatory (SKAO) will explore the radio sky to new depths in order to conduct transformational science. SKAO data products made available to astronomers will be correspondingly large and complex, requiring the application of advanced analysis techniques to extract key science findings. To this end, SKAO is conducting a series of Science Data Challenges, each designed to familiarize the scientific community with SKAO data and to drive the development of new analysis techniques. We present the results from Science Data Challenge 2 (SDC2), which invited participants to find and characterize 233 245 neutral hydrogen (H i) sources in a simulated data product representing a 2000 h SKA-Mid spectral line observation from redshifts 0.25–0.5. Through the generous support of eight international supercomputing facilities, participants were able to undertake the Challenge using dedicated computational resources. Alongside the main challenge, ‘reproducibility awards’ were made in recognition of those pipelines which demonstrated Open Science best practice. The Challenge saw over 100 participants develop a range of new and existing techniques, with results that highlight the strengths of multidisciplinary and collaborative effort. The winning strategy – which combined predictions from two independent machine learning techniques to yield a 20 per cent improvement in overall performance – underscores one of the main Challenge outcomes: that of method complementarity. It is likely that the combination of methods in a so-called ensemble approach will be key to exploiting very large astronomical data sets.
Publisher: Oxford University Press (OUP)
Date: 05-10-2020
Abstract: As the largest radio telescope in the world, the Square Kilometre Array (SKA) will lead the next generation of radio astronomy. The feats of engineering required to construct the telescope array will be matched only by the techniques developed to exploit the rich scientific value of the data. To drive forward the development of efficient and accurate analysis methods, we are designing a series of data challenges that will provide the scientific community with high-quality data sets for testing and evaluating new techniques. In this paper, we present a description and results from the first such Science Data Challenge 1 (SDC1). Based on SKA MID continuum simulated observations and covering three frequencies (560, 1400, and 9200 MHz) at three depths (8, 100, and 1000 h), SDC1 asked participants to apply source detection, characterization, and classification methods to simulated data. The challenge opened in 2018 November, with nine teams submitting results by the deadline of 2019 April. In this work, we analyse the results for eight of those teams, showcasing the variety of approaches that can be successfully used to find, characterize, and classify sources in a deep, crowded field. The results also demonstrate the importance of building domain knowledge and expertise on this kind of analysis to obtain the best performance. As high-resolution observations begin revealing the true complexity of the sky, one of the outstanding challenges emerging from this analysis is the ability to deal with highly resolved and complex sources as effectively as the unresolved source population.
Location: United Kingdom of Great Britain and Northern Ireland
Location: United Kingdom of Great Britain and Northern Ireland
Location: United Kingdom of Great Britain and Northern Ireland
No related grants have been discovered for Philippa Hartley.