N. Gupta, Zeeshan Hayder, R. Norris, M. Huynh, L. Petersson, X. R. Wang, H. Andernach, B. Koribalski, M. Yew, E. Crawford
Abstract The present work discusses the use of a weakly-supervised deep learning algorithm that reduces the cost of labelling pixel-level masks for complex radio galaxies with multiple components. The algorithm is trained on weak class-level labels of radio galaxies to get class activation maps (CAMs). The CAMs are further refined using an inter-pixel relations network (IRNet) to get instance segmentation masks over radio galaxies and the positions of their infrared hosts. We use data from the Australian Square Kilometre Array Pathfinder (ASKAP) telescope, specifically the Evolutionary Map of the Universe (EMU) Pilot Survey, which covered a sky area of 270 square degrees with an RMS sensitivity of 25–35 $mu$ Jy beam $^{-1}$ . We demonstrate that weakly-supervised deep learning algorithms can achieve high accuracy in predicting pixel-level information, including masks for the extended radio emission encapsulating all galaxy components and the positions of the infrared host galaxies. We evaluate the performance of our method using mean Average Precision (mAP) across multiple classes at a standard intersection over union (IoU) threshold of 0.5. We show that the model achieves a mAP $_{50}$ of 67.5% and 76.8% for radio masks and infrared host positions, respectively. The network architecture can be found at the following link: https://github.com/Nikhel1/Gal-CAM
{"title":"Deep learning for morphological identification of extended radio galaxies using weak labels","authors":"N. Gupta, Zeeshan Hayder, R. Norris, M. Huynh, L. Petersson, X. R. Wang, H. Andernach, B. Koribalski, M. Yew, E. Crawford","doi":"10.1017/pasa.2023.46","DOIUrl":"https://doi.org/10.1017/pasa.2023.46","url":null,"abstract":"Abstract The present work discusses the use of a weakly-supervised deep learning algorithm that reduces the cost of labelling pixel-level masks for complex radio galaxies with multiple components. The algorithm is trained on weak class-level labels of radio galaxies to get class activation maps (CAMs). The CAMs are further refined using an inter-pixel relations network (IRNet) to get instance segmentation masks over radio galaxies and the positions of their infrared hosts. We use data from the Australian Square Kilometre Array Pathfinder (ASKAP) telescope, specifically the Evolutionary Map of the Universe (EMU) Pilot Survey, which covered a sky area of 270 square degrees with an RMS sensitivity of 25–35 \u0000$mu$\u0000 Jy beam \u0000$^{-1}$\u0000 . We demonstrate that weakly-supervised deep learning algorithms can achieve high accuracy in predicting pixel-level information, including masks for the extended radio emission encapsulating all galaxy components and the positions of the infrared host galaxies. We evaluate the performance of our method using mean Average Precision (mAP) across multiple classes at a standard intersection over union (IoU) threshold of 0.5. We show that the model achieves a mAP \u0000$_{50}$\u0000 of 67.5% and 76.8% for radio masks and infrared host positions, respectively. The network architecture can be found at the following link: https://github.com/Nikhel1/Gal-CAM","PeriodicalId":20753,"journal":{"name":"Publications of the Astronomical Society of Australia","volume":"9 1","pages":""},"PeriodicalIF":6.3,"publicationDate":"2023-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74401057","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract We study the correlation between the non-thermal velocity dispersion ( $sigma_{nth}$ ) and the length scale (L) in the neutral interstellar medium (ISM) using a large number of Hi gas components taken from various published Hi surveys and previous Hi studies. We notice that above the length-scale (L) of 0.40 pc, there is a power-law relationship between $sigma_{nth}$ and L. However, below 0.40 pc, there is a break in the power law, where $sigma_{nth}$ is not significantly correlated with L. It has been observed from the Markov chain Monte Carlo (MCMC) method that for the dataset of L $gt$ 0.40 pc, the most probable values of intensity (A) and power-law index (p) are 1.14 and 0.55, respectively. Result of p suggests that the power law is steeper than the standard Kolmogorov law of turbulence. This is due to the dominance of clouds in the cold neutral medium. This is even more clear when we separate the clouds into two categories: one for L is $gt$ 0.40 pc and the kinetic temperature ( $T_{k}$ ) is $lt$ 250 K, which are in the cold neutral medium (CNM) and for other one where L is $gt$ 0.40 pc and $T_{k}$ is between 250 and 5 000 K, which are in the thermally unstable phase (UNM). Most probable values of A and p are 1.14 and 0.67, respectively, in the CNM phase and 1.01 and 0.52, respectively, in the UNM phase. A greater number of data points is effective for the UNM phase in constructing a more accurate estimate of A and p, since most of the clouds in the UNM phase lie below 500 K. However, from the value of p in the CNM phase, it appears that there is a significant difference from the Kolmogorov scaling, which can be attributed to a shock-dominated medium.
{"title":"Turbulence measurements in the neutral ISM from Hi-21 cm emission–absorption spectra","authors":"Atanu Koley","doi":"10.1017/pasa.2023.43","DOIUrl":"https://doi.org/10.1017/pasa.2023.43","url":null,"abstract":"Abstract We study the correlation between the non-thermal velocity dispersion ( \u0000$sigma_{nth}$\u0000 ) and the length scale (L) in the neutral interstellar medium (ISM) using a large number of Hi gas components taken from various published Hi surveys and previous Hi studies. We notice that above the length-scale (L) of 0.40 pc, there is a power-law relationship between \u0000$sigma_{nth}$\u0000 and L. However, below 0.40 pc, there is a break in the power law, where \u0000$sigma_{nth}$\u0000 is not significantly correlated with L. It has been observed from the Markov chain Monte Carlo (MCMC) method that for the dataset of L \u0000$gt$\u0000 0.40 pc, the most probable values of intensity (A) and power-law index (p) are 1.14 and 0.55, respectively. Result of p suggests that the power law is steeper than the standard Kolmogorov law of turbulence. This is due to the dominance of clouds in the cold neutral medium. This is even more clear when we separate the clouds into two categories: one for L is \u0000$gt$\u0000 0.40 pc and the kinetic temperature ( \u0000$T_{k}$\u0000 ) is \u0000$lt$\u0000 250 K, which are in the cold neutral medium (CNM) and for other one where L is \u0000$gt$\u0000 0.40 pc and \u0000$T_{k}$\u0000 is between 250 and 5 000 K, which are in the thermally unstable phase (UNM). Most probable values of A and p are 1.14 and 0.67, respectively, in the CNM phase and 1.01 and 0.52, respectively, in the UNM phase. A greater number of data points is effective for the UNM phase in constructing a more accurate estimate of A and p, since most of the clouds in the UNM phase lie below 500 K. However, from the value of p in the CNM phase, it appears that there is a significant difference from the Kolmogorov scaling, which can be attributed to a shock-dominated medium.","PeriodicalId":20753,"journal":{"name":"Publications of the Astronomical Society of Australia","volume":"23 1","pages":""},"PeriodicalIF":6.3,"publicationDate":"2023-08-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86071519","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
P. Armstrong, H. Qu, D. Brout, T. Davis, R. K. A. G. Kim, C. Lidman, M. Sako, B. Observatory, T. D. O. Astronomy, Astrophysics, A. N. University, Act 2601, Australia, D. Physics, Astronomy, U. Pennsylvania, Philadelphia, PA 19104, USA., D. Astronomy, Boston University, 725 Commonwealth Ave., Boston, MA 02215, S. O. Mathematics, Physics, The University of Queensland, Brisbane, Qld 4072, Kavli Institute for Cosmological Physics, U. Chicago, Chicago, IL 60637, P. Division, L. B. N. Laboratory, Berkeley, CA 94720, Centre for Gravitational Astrophysics, College of Materials Science, The Australian National University, Australia. School of Science, Canberra, The Arc Centre of Excellence for All-Sky Astrophysics Dimensions
Abstract As the scale of cosmological surveys increases, so does the complexity in the analyses. This complexity can often make it difficult to derive the underlying principles, necessitating statistically rigorous testing to ensure the results of an analysis are consistent and reasonable. This is particularly important in multi-probe cosmological analyses like those used in the Dark Energy Survey (DES) and the upcoming Legacy Survey of Space and Time, where accurate uncertainties are vital. In this paper, we present a statistically rigorous method to test the consistency of contours produced in these analyses and apply this method to the Pippin cosmological pipeline used for type Ia supernova cosmology with the DES. We make use of the Neyman construction, a frequentist methodology that leverages extensive simulations to calculate confidence intervals, to perform this consistency check. A true Neyman construction is too computationally expensive for supernova cosmology, so we develop a method for approximating a Neyman construction with far fewer simulations. We find that for a simulated dataset, the 68% contour reported by the Pippin pipeline and the 68% confidence region produced by our approximate Neyman construction differ by less than a percent near the input cosmology; however, they show more significant differences far from the input cosmology, with a maximal difference of 0.05 in $Omega_{M}$ and 0.07 in w. This divergence is most impactful for analyses of cosmological tensions, but its impact is mitigated when combining supernovae with other cross-cutting cosmological probes, such as the cosmic microwave background.
{"title":"Probing the consistency of cosmological contours for supernova cosmology","authors":"P. Armstrong, H. Qu, D. Brout, T. Davis, R. K. A. G. Kim, C. Lidman, M. Sako, B. Observatory, T. D. O. Astronomy, Astrophysics, A. N. University, Act 2601, Australia, D. Physics, Astronomy, U. Pennsylvania, Philadelphia, PA 19104, USA., D. Astronomy, Boston University, 725 Commonwealth Ave., Boston, MA 02215, S. O. Mathematics, Physics, The University of Queensland, Brisbane, Qld 4072, Kavli Institute for Cosmological Physics, U. Chicago, Chicago, IL 60637, P. Division, L. B. N. Laboratory, Berkeley, CA 94720, Centre for Gravitational Astrophysics, College of Materials Science, The Australian National University, Australia. School of Science, Canberra, The Arc Centre of Excellence for All-Sky Astrophysics Dimensions","doi":"10.1017/pasa.2023.40","DOIUrl":"https://doi.org/10.1017/pasa.2023.40","url":null,"abstract":"Abstract As the scale of cosmological surveys increases, so does the complexity in the analyses. This complexity can often make it difficult to derive the underlying principles, necessitating statistically rigorous testing to ensure the results of an analysis are consistent and reasonable. This is particularly important in multi-probe cosmological analyses like those used in the Dark Energy Survey (DES) and the upcoming Legacy Survey of Space and Time, where accurate uncertainties are vital. In this paper, we present a statistically rigorous method to test the consistency of contours produced in these analyses and apply this method to the Pippin cosmological pipeline used for type Ia supernova cosmology with the DES. We make use of the Neyman construction, a frequentist methodology that leverages extensive simulations to calculate confidence intervals, to perform this consistency check. A true Neyman construction is too computationally expensive for supernova cosmology, so we develop a method for approximating a Neyman construction with far fewer simulations. We find that for a simulated dataset, the 68% contour reported by the Pippin pipeline and the 68% confidence region produced by our approximate Neyman construction differ by less than a percent near the input cosmology; however, they show more significant differences far from the input cosmology, with a maximal difference of 0.05 in \u0000$Omega_{M}$\u0000 and 0.07 in w. This divergence is most impactful for analyses of cosmological tensions, but its impact is mitigated when combining supernovae with other cross-cutting cosmological probes, such as the cosmic microwave background.","PeriodicalId":20753,"journal":{"name":"Publications of the Astronomical Society of Australia","volume":"96 1","pages":""},"PeriodicalIF":6.3,"publicationDate":"2023-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80937353","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
N. Bhat, N. A. Swainston, S. McSweeney, M. Xue, B. W. Meyers, S. Kudale, S. Dai, S. Tremblay, W. van Straten, R. Shannon, K. R. Smith, M. Sokolowski, S. Ord, G. Sleap, A. Williams, P. Hancock, R. Lange, J. Tocknell, M. Johnston-Hollitt, D. Kaplan, S. Tingay, M. Walker
{"title":"The Southern-sky MWA Rapid Two-metre (SMART) pulsar survey—II. Survey status, pulsar census, and first pulsar discoveries – ADDENDUM","authors":"N. Bhat, N. A. Swainston, S. McSweeney, M. Xue, B. W. Meyers, S. Kudale, S. Dai, S. Tremblay, W. van Straten, R. Shannon, K. R. Smith, M. Sokolowski, S. Ord, G. Sleap, A. Williams, P. Hancock, R. Lange, J. Tocknell, M. Johnston-Hollitt, D. Kaplan, S. Tingay, M. Walker","doi":"10.1017/pasa.2023.30","DOIUrl":"https://doi.org/10.1017/pasa.2023.30","url":null,"abstract":"","PeriodicalId":20753,"journal":{"name":"Publications of the Astronomical Society of Australia","volume":"21 1","pages":""},"PeriodicalIF":6.3,"publicationDate":"2023-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90899204","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
A. Thomson, D. McConnell, E. Lenc, T. Galvin, L. Rudnick, G. Heald, C. Hale, S. Duchesne, C. Anderson, E. Carretti, C. Federrath, B. Gaensler, L. Harvey-Smith, M. Haverkorn, A. Hotan, Y. Ma, T. Murphy, N. M. McClure-Griffith, V. Moss, S. O’Sullivan, W. Raja, A. Seta, Cameron Van Eck, J. West, M. Whiting, M. Wieringa
Abstract The Australian SKA Pathfinder (ASKAP) radio telescope has carried out a survey of the entire Southern Sky at 887.5 MHz. The wide area, high angular resolution, and broad bandwidth provided by the low-band Rapid ASKAP Continuum Survey (RACS-low) allow the production of a next-generation rotation measure (RM) grid across the entire Southern Sky. Here we introduce this project as Spectral and Polarisation in Cutouts of Extragalactic sources from RACS (SPICE-RACS). In our first data release, we image 30 RACS-low fields in Stokes I, Q, U at 25 $^{primeprime}$ angular resolution, across 744–1032 MHz with 1 MHz spectral resolution. Using a bespoke, highly parallelised, software pipeline we are able to rapidly process wide-area spectro-polarimetric ASKAP observations. Notably, we use ‘postage stamp’ cutouts to assess the polarisation properties of 105912 radio components detected in total intensity. We find that our Stokes Q and U images have an rms noise of $sim$ 80 $unicode{x03BC}$ Jy PSF $^{-1}$ , and our correction for instrumental polarisation leakage allows us to characterise components with $gtrsim$ 1% polarisation fraction over most of the field of view. We produce a broadband polarised radio component catalogue that contains 5818 RM measurements over an area of $sim$ 1300 deg $^{2}$ with an average error in RM of $1.6^{+1.1}_{-1.0}$ rad m $^{-2}$ , and an average linear polarisation fraction $3.4^{+3.0}_{-1.6}$ %. We determine this subset of components using the conditions that the polarised signal-to-noise ratio is $>$ 8, the polarisation fraction is above our estimated polarised leakage, and the Stokes I spectrum has a reliable model. Our catalogue provides an areal density of $4pm2$ RMs deg $^{-2}$ ; an increase of $sim$ 4 times over the previous state-of-the-art (Taylor, Stil, Sunstrum 2009, ApJ, 702, 1230). Meaning that, having used just 3% of the RACS-low sky area, we have produced the 3rd largest RM catalogue to date. This catalogue has broad applications for studying astrophysical magnetic fields; notably revealing remarkable structure in the Galactic RM sky. We will explore this Galactic structure in a follow-up paper. We will also apply the techniques described here to produce an all-Southern-sky RM catalogue from RACS observations. Finally, we make our catalogue, spectra, images, and processing pipeline publicly available.
澳大利亚SKA探路者(ASKAP)射电望远镜在887.5 MHz波段对整个南方天空进行了一次巡天。低波段快速ASKAP连续测量(RACS-low)提供的广域、高角度分辨率和宽带宽允许在整个南方天空中生产下一代旋转测量(RM)网格。在这里，我们介绍这个项目作为光谱和偏振从RACS (SPICE-RACS)的河外源切割。在我们的第一个数据发布中，我们以25 $^{primeprime}$角分辨率在744-1032 MHz范围内对Stokes I, Q, U中的30个RACS-low场进行了成像，频谱分辨率为1 MHz。使用定制的、高度并行化的软件管道，我们能够快速处理广域光谱偏振ASKAP观测。值得注意的是，我们使用“邮票”切割来评估在总强度中检测到的105912无线电组件的极化特性。我们发现我们的Stokes Q和U图像的rms噪声为$sim$ 80 $unicode{x03BC}$ Jy PSF $^{-1}$，并且我们对仪器偏振泄漏的校正使我们能够在大部分视场中表征具有$gtrsim$ 1%偏振分数的组件。我们制作了一个宽带极化无线电组件目录，其中包含5818个RM测量值，面积为$sim$ 1300°$^{2}$，RM的平均误差为$1.6^{+1.1}_{-1.0}$ rad m$ ^{-2}$，平均线性极化分数为$3.4^{+3.0}_{-1.6}$ %。我们使用极化信噪比为$>$ 8，极化分数高于我们估计的极化泄漏，并且Stokes I谱具有可靠的模型的条件来确定该组件子集。我们的目录提供的面密度为$4pm2$ RMs度$^{-2}$;比以前的最先进技术增加了4倍(Taylor, Stil, Sunstrum 2009, ApJ, 702, 1230)。这意味着，仅使用了3%的rac低天空区域，我们就制作了迄今为止第三大的RM目录。该目录在研究天体物理磁场方面有广泛的应用;特别地揭示了银河系RM天空中非凡的结构。我们将在后续论文中探讨这个银河系结构。我们还将应用这里描述的技术，根据RACS的观测结果制作一个全南方天空的RM目录。最后，我们公开了我们的目录、光谱、图像和处理管道。
{"title":"The Rapid ASKAP Continuum Survey III: Spectra and Polarisation In Cutouts of Extragalactic Sources (SPICE-RACS) first data release","authors":"A. Thomson, D. McConnell, E. Lenc, T. Galvin, L. Rudnick, G. Heald, C. Hale, S. Duchesne, C. Anderson, E. Carretti, C. Federrath, B. Gaensler, L. Harvey-Smith, M. Haverkorn, A. Hotan, Y. Ma, T. Murphy, N. M. McClure-Griffith, V. Moss, S. O’Sullivan, W. Raja, A. Seta, Cameron Van Eck, J. West, M. Whiting, M. Wieringa","doi":"10.1017/pasa.2023.38","DOIUrl":"https://doi.org/10.1017/pasa.2023.38","url":null,"abstract":"Abstract The Australian SKA Pathfinder (ASKAP) radio telescope has carried out a survey of the entire Southern Sky at 887.5 MHz. The wide area, high angular resolution, and broad bandwidth provided by the low-band Rapid ASKAP Continuum Survey (RACS-low) allow the production of a next-generation rotation measure (RM) grid across the entire Southern Sky. Here we introduce this project as Spectral and Polarisation in Cutouts of Extragalactic sources from RACS (SPICE-RACS). In our first data release, we image 30 RACS-low fields in Stokes I, Q, U at 25 \u0000$^{primeprime}$\u0000 angular resolution, across 744–1032 MHz with 1 MHz spectral resolution. Using a bespoke, highly parallelised, software pipeline we are able to rapidly process wide-area spectro-polarimetric ASKAP observations. Notably, we use ‘postage stamp’ cutouts to assess the polarisation properties of 105912 radio components detected in total intensity. We find that our Stokes Q and U images have an rms noise of \u0000$sim$\u0000 80 \u0000$unicode{x03BC}$\u0000 Jy PSF \u0000$^{-1}$\u0000 , and our correction for instrumental polarisation leakage allows us to characterise components with \u0000$gtrsim$\u0000 1% polarisation fraction over most of the field of view. We produce a broadband polarised radio component catalogue that contains 5818 RM measurements over an area of \u0000$sim$\u0000 1300 deg \u0000$^{2}$\u0000 with an average error in RM of \u0000$1.6^{+1.1}_{-1.0}$\u0000 rad m \u0000$^{-2}$\u0000 , and an average linear polarisation fraction \u0000$3.4^{+3.0}_{-1.6}$\u0000 %. We determine this subset of components using the conditions that the polarised signal-to-noise ratio is \u0000$>$\u0000 8, the polarisation fraction is above our estimated polarised leakage, and the Stokes I spectrum has a reliable model. Our catalogue provides an areal density of \u0000$4pm2$\u0000 RMs deg \u0000$^{-2}$\u0000 ; an increase of \u0000$sim$\u0000 4 times over the previous state-of-the-art (Taylor, Stil, Sunstrum 2009, ApJ, 702, 1230). Meaning that, having used just 3% of the RACS-low sky area, we have produced the 3rd largest RM catalogue to date. This catalogue has broad applications for studying astrophysical magnetic fields; notably revealing remarkable structure in the Galactic RM sky. We will explore this Galactic structure in a follow-up paper. We will also apply the techniques described here to produce an all-Southern-sky RM catalogue from RACS observations. Finally, we make our catalogue, spectra, images, and processing pipeline publicly available.","PeriodicalId":20753,"journal":{"name":"Publications of the Astronomical Society of Australia","volume":"6 1","pages":""},"PeriodicalIF":6.3,"publicationDate":"2023-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79965556","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
K. Luken, R. Norris, X. R. Wang, L. Park, Ying Guo, M. Filipović
Abstract With the advent of deep, all-sky radio surveys, the need for ancillary data to make the most of the new, high-quality radio data from surveys like the Evolutionary Map of the Universe (EMU), GaLactic and Extragalactic All-sky Murchison Widefield Array survey eXtended, Very Large Array Sky Survey, and LOFAR Two-metre Sky Survey is growing rapidly. Radio surveys produce significant numbers of Active Galactic Nuclei (AGNs) and have a significantly higher average redshift when compared with optical and infrared all-sky surveys. Thus, traditional methods of estimating redshift are challenged, with spectroscopic surveys not reaching the redshift depth of radio surveys, and AGNs making it difficult for template fitting methods to accurately model the source. Machine Learning (ML) methods have been used, but efforts have typically been directed towards optically selected samples, or samples at significantly lower redshift than expected from upcoming radio surveys. This work compiles and homogenises a radio-selected dataset from both the northern hemisphere (making use of Sloan Digital Sky Survey optical photometry) and southern hemisphere (making use of Dark Energy Survey optical photometry). We then test commonly used ML algorithms such as k-Nearest Neighbours (kNN), Random Forest, ANNz, and GPz on this monolithic radio-selected sample. We show that kNN has the lowest percentage of catastrophic outliers, providing the best match for the majority of science cases in the EMU survey. We note that the wider redshift range of the combined dataset used allows for estimation of sources up to $z = 3$ before random scatter begins to dominate. When binning the data into redshift bins and treating the problem as a classification problem, we are able to correctly identify $approx$ 76% of the highest redshift sources—sources at redshift $z > 2.51$ —as being in either the highest bin ( $z > 2.51$ ) or second highest ( $z = 2.25$ ).
{"title":"Measuring photometric redshifts for high-redshift radio source surveys","authors":"K. Luken, R. Norris, X. R. Wang, L. Park, Ying Guo, M. Filipović","doi":"10.1017/pasa.2023.39","DOIUrl":"https://doi.org/10.1017/pasa.2023.39","url":null,"abstract":"Abstract With the advent of deep, all-sky radio surveys, the need for ancillary data to make the most of the new, high-quality radio data from surveys like the Evolutionary Map of the Universe (EMU), GaLactic and Extragalactic All-sky Murchison Widefield Array survey eXtended, Very Large Array Sky Survey, and LOFAR Two-metre Sky Survey is growing rapidly. Radio surveys produce significant numbers of Active Galactic Nuclei (AGNs) and have a significantly higher average redshift when compared with optical and infrared all-sky surveys. Thus, traditional methods of estimating redshift are challenged, with spectroscopic surveys not reaching the redshift depth of radio surveys, and AGNs making it difficult for template fitting methods to accurately model the source. Machine Learning (ML) methods have been used, but efforts have typically been directed towards optically selected samples, or samples at significantly lower redshift than expected from upcoming radio surveys. This work compiles and homogenises a radio-selected dataset from both the northern hemisphere (making use of Sloan Digital Sky Survey optical photometry) and southern hemisphere (making use of Dark Energy Survey optical photometry). We then test commonly used ML algorithms such as k-Nearest Neighbours (kNN), Random Forest, ANNz, and GPz on this monolithic radio-selected sample. We show that kNN has the lowest percentage of catastrophic outliers, providing the best match for the majority of science cases in the EMU survey. We note that the wider redshift range of the combined dataset used allows for estimation of sources up to \u0000$z = 3$\u0000 before random scatter begins to dominate. When binning the data into redshift bins and treating the problem as a classification problem, we are able to correctly identify \u0000$approx$\u0000 76% of the highest redshift sources—sources at redshift \u0000$z > 2.51$\u0000 —as being in either the highest bin ( \u0000$z > 2.51$\u0000 ) or second highest ( \u0000$z = 2.25$\u0000 ).","PeriodicalId":20753,"journal":{"name":"Publications of the Astronomical Society of Australia","volume":"10 1","pages":""},"PeriodicalIF":6.3,"publicationDate":"2023-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87288434","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract Next-generation astronomical surveys naturally pose challenges for human-centred visualisation and analysis workflows that currently rely on the use of standard desktop display environments. While a significant fraction of the data preparation and analysis will be taken care of by automated pipelines, crucial steps of knowledge discovery can still only be achieved through various level of human interpretation. As the number of sources in a survey grows, there is need to both modify and simplify repetitive visualisation processes that need to be completed for each source. As tasks such as per-source quality control, candidate rejection, and morphological classification all share a single instruction, multiple data (SIMD) work pattern, they are amenable to a parallel solution. Selecting extragalactic neutral hydrogen (Hi) surveys as a representative example, we use system performance benchmarking and the visual data and reasoning methodology from the field of information visualisation to evaluate a bespoke comparative visualisation environment: the encube visual analytics framework deployed on the 83 Megapixel Swinburne Discovery Wall. Through benchmarking using spectral cube data from existing Hi surveys, we are able to perform interactive comparative visualisation via texture-based volume rendering of 180 three-dimensional (3D) data cubes at a time. The time to load a configuration of spectral cubes scale linearly with the number of voxels, with independent samples of 180 cubes (8.4 Gigavoxels or 34 Gigabytes) each loading in under 5 min. We show that parallel comparative inspection is a productive and time-saving technique which can reduce the time taken to complete SIMD-style visual tasks currently performed at the desktop by at least two orders of magnitude, potentially rendering some labour-intensive desktop-based workflows obsolete.
{"title":"Survey-scale discovery-based research processes: Evaluating a bespoke visualisation environment for astronomical survey data","authors":"C. Fluke, D. Vohl, V. Kilborn, C. Murugeshan","doi":"10.1017/pasa.2023.37","DOIUrl":"https://doi.org/10.1017/pasa.2023.37","url":null,"abstract":"Abstract Next-generation astronomical surveys naturally pose challenges for human-centred visualisation and analysis workflows that currently rely on the use of standard desktop display environments. While a significant fraction of the data preparation and analysis will be taken care of by automated pipelines, crucial steps of knowledge discovery can still only be achieved through various level of human interpretation. As the number of sources in a survey grows, there is need to both modify and simplify repetitive visualisation processes that need to be completed for each source. As tasks such as per-source quality control, candidate rejection, and morphological classification all share a single instruction, multiple data (SIMD) work pattern, they are amenable to a parallel solution. Selecting extragalactic neutral hydrogen (Hi) surveys as a representative example, we use system performance benchmarking and the visual data and reasoning methodology from the field of information visualisation to evaluate a bespoke comparative visualisation environment: the encube visual analytics framework deployed on the 83 Megapixel Swinburne Discovery Wall. Through benchmarking using spectral cube data from existing Hi surveys, we are able to perform interactive comparative visualisation via texture-based volume rendering of 180 three-dimensional (3D) data cubes at a time. The time to load a configuration of spectral cubes scale linearly with the number of voxels, with independent samples of 180 cubes (8.4 Gigavoxels or 34 Gigabytes) each loading in under 5 min. We show that parallel comparative inspection is a productive and time-saving technique which can reduce the time taken to complete SIMD-style visual tasks currently performed at the desktop by at least two orders of magnitude, potentially rendering some labour-intensive desktop-based workflows obsolete.","PeriodicalId":20753,"journal":{"name":"Publications of the Astronomical Society of Australia","volume":"1 1","pages":""},"PeriodicalIF":6.3,"publicationDate":"2023-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82965670","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract Investigating rare and new objects have always been an important direction in astronomy. Cataclysmic variables (CVs) are ideal and natural celestial bodies for studying the accretion process of semi-detached binaries with accretion processes. However, the sample size of CVs must increase because a lager gap exists between the observational and the theoretical expanding CVs. Astronomy has entered the big data era and can provide massive images containing CV candidates. CVs as a type of faint celestial objects, are highly challenging to be identified directly from images using automatic manners. Deep learning has rapidly developed in intelligent image processing and has been widely applied in some astronomical fields with excellent detection results. YOLOX, as the latest YOLO framework, is advantageous in detecting small and dark targets. This work proposes an improved YOLOX-based framework according to the characteristics of CVs and Sloan Digital Sky Survey (SDSS) photometric images to train and verify the model to realise CV detection. We use the Convolutional Block Attention Module to increase the number of output features with the feature extraction network and adjust the feature fusion network to obtain fused features. Accordingly, the loss function is modified. Experimental results demonstrate that the improved model produces satisfactory results, with average accuracy (mean average Precision at 0.5) of 92.0%, Precision of 92.9%, Recall of 94.3%, and $F1-score$ of 93.6% on the test set. The proposed method can efficiently achieve the identification of CVs in test samples and search for CV candidates in unlabeled images. The image data vastly outnumber the spectra in the SDSS-released data. With supplementary follow-up observations or spectra, the proposed model can help astronomers in seeking and detecting CVs in a new manner to ensure that a more extensive CV catalog can be built. The proposed model may also be applied to the detection of other kinds of celestial objects.
{"title":"Automatic detection of cataclysmic variables from SDSS images","authors":"Junfeng Huang, Meixia Qu, Bin Jiang, Yanxia Zhang","doi":"10.1017/pasa.2023.34","DOIUrl":"https://doi.org/10.1017/pasa.2023.34","url":null,"abstract":"Abstract Investigating rare and new objects have always been an important direction in astronomy. Cataclysmic variables (CVs) are ideal and natural celestial bodies for studying the accretion process of semi-detached binaries with accretion processes. However, the sample size of CVs must increase because a lager gap exists between the observational and the theoretical expanding CVs. Astronomy has entered the big data era and can provide massive images containing CV candidates. CVs as a type of faint celestial objects, are highly challenging to be identified directly from images using automatic manners. Deep learning has rapidly developed in intelligent image processing and has been widely applied in some astronomical fields with excellent detection results. YOLOX, as the latest YOLO framework, is advantageous in detecting small and dark targets. This work proposes an improved YOLOX-based framework according to the characteristics of CVs and Sloan Digital Sky Survey (SDSS) photometric images to train and verify the model to realise CV detection. We use the Convolutional Block Attention Module to increase the number of output features with the feature extraction network and adjust the feature fusion network to obtain fused features. Accordingly, the loss function is modified. Experimental results demonstrate that the improved model produces satisfactory results, with average accuracy (mean average Precision at 0.5) of 92.0%, Precision of 92.9%, Recall of 94.3%, and \u0000$F1-score$\u0000 of 93.6% on the test set. The proposed method can efficiently achieve the identification of CVs in test samples and search for CV candidates in unlabeled images. The image data vastly outnumber the spectra in the SDSS-released data. With supplementary follow-up observations or spectra, the proposed model can help astronomers in seeking and detecting CVs in a new manner to ensure that a more extensive CV catalog can be built. The proposed model may also be applied to the detection of other kinds of celestial objects.","PeriodicalId":20753,"journal":{"name":"Publications of the Astronomical Society of Australia","volume":"18 1","pages":""},"PeriodicalIF":6.3,"publicationDate":"2023-06-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86680271","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
A. Zic, D. Reardon, Agastya Kapur, G. Hobbs, R. Mandow, M. Curyło, R. Shannon, Jacob Askew, M. Bailes, N. D. R. Bhat, A. Cameron, Zu-Cheng Chen, Shi Dai, Valentina Di Marco, Yi Feng, M. Kerr, Atharva Kulkarni, M. Lower, Rui Luo, R. Manchester, M. T. Miles, R. Nathan, S. Osłowski, Axl F. Rogers, C. Russell, J. Sarkissian, Mohsen Shamohammadi, R. Spiewak, N. Thyagarajan, L. Toomey, Shuangqiang Wang, Lei Zhang, Songbo Zhang, Xingjiang Zhu
Abstract We present the third data release from the Parkes Pulsar Timing Array (PPTA) project. The release contains observations of 32 pulsars obtained using the 64-m Parkes ‘Murriyang’ radio telescope. The data span is up to 18 yr with a typical cadence of 3 weeks. This data release is formed by combining an updated version of our second data release with $sim$ 3 yr of more recent data primarily obtained using an ultra-wide-bandwidth receiver system that operates between 704 and 4032 MHz. We provide calibrated pulse profiles, flux density dynamic spectra, pulse times of arrival, and initial pulsar timing models. We describe methods for processing such wide-bandwidth observations and compare this data release with our previous release.
{"title":"The Parkes Pulsar Timing Array third data release","authors":"A. Zic, D. Reardon, Agastya Kapur, G. Hobbs, R. Mandow, M. Curyło, R. Shannon, Jacob Askew, M. Bailes, N. D. R. Bhat, A. Cameron, Zu-Cheng Chen, Shi Dai, Valentina Di Marco, Yi Feng, M. Kerr, Atharva Kulkarni, M. Lower, Rui Luo, R. Manchester, M. T. Miles, R. Nathan, S. Osłowski, Axl F. Rogers, C. Russell, J. Sarkissian, Mohsen Shamohammadi, R. Spiewak, N. Thyagarajan, L. Toomey, Shuangqiang Wang, Lei Zhang, Songbo Zhang, Xingjiang Zhu","doi":"10.1017/pasa.2023.36","DOIUrl":"https://doi.org/10.1017/pasa.2023.36","url":null,"abstract":"Abstract We present the third data release from the Parkes Pulsar Timing Array (PPTA) project. The release contains observations of 32 pulsars obtained using the 64-m Parkes ‘Murriyang’ radio telescope. The data span is up to 18 yr with a typical cadence of 3 weeks. This data release is formed by combining an updated version of our second data release with \u0000$sim$\u0000 3 yr of more recent data primarily obtained using an ultra-wide-bandwidth receiver system that operates between 704 and 4032 MHz. We provide calibrated pulse profiles, flux density dynamic spectra, pulse times of arrival, and initial pulsar timing models. We describe methods for processing such wide-bandwidth observations and compare this data release with our previous release.","PeriodicalId":20753,"journal":{"name":"Publications of the Astronomical Society of Australia","volume":"34 1","pages":""},"PeriodicalIF":6.3,"publicationDate":"2023-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82941612","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
L. Driessen, G. Heald, S. Duchesne, T. Murphy, E. Lenc, J. Leung, V. Moss
Abstract We present a method for identifying radio stellar sources using their proper-motion. We demonstrate this method using the FIRST, VLASS, RACS-low and RACS-mid radio surveys, and astrometric information from Gaia Data Release 3. We find eight stellar radio sources using this method, two of which have not previously been identified in the literature as radio stars. We determine that this method probes distances of $sim$ 90pc when we use FIRST and RACS-mid, and $sim$ 250pc when we use FIRST and VLASS. We investigate the time baselines required by current and future radio sky surveys to detect the eight sources we found, with the SKA (6.7 GHz) requiring $<$ 3 yr between observations to find all eight sources. We also identify nine previously known and 43 candidate variable radio stellar sources that are detected in FIRST (1.4 GHz) but are not detected in RACS-mid (1.37 GHz). This shows that many stellar radio sources are variable, and that surveys with multiple epochs can detect a more complete sample of stellar radio sources.
{"title":"Detection of radio emission from stars via proper-motion searches","authors":"L. Driessen, G. Heald, S. Duchesne, T. Murphy, E. Lenc, J. Leung, V. Moss","doi":"10.1017/pasa.2023.26","DOIUrl":"https://doi.org/10.1017/pasa.2023.26","url":null,"abstract":"Abstract We present a method for identifying radio stellar sources using their proper-motion. We demonstrate this method using the FIRST, VLASS, RACS-low and RACS-mid radio surveys, and astrometric information from Gaia Data Release 3. We find eight stellar radio sources using this method, two of which have not previously been identified in the literature as radio stars. We determine that this method probes distances of \u0000$sim$\u0000 90pc when we use FIRST and RACS-mid, and \u0000$sim$\u0000 250pc when we use FIRST and VLASS. We investigate the time baselines required by current and future radio sky surveys to detect the eight sources we found, with the SKA (6.7 GHz) requiring \u0000$<$\u0000 3 yr between observations to find all eight sources. We also identify nine previously known and 43 candidate variable radio stellar sources that are detected in FIRST (1.4 GHz) but are not detected in RACS-mid (1.37 GHz). This shows that many stellar radio sources are variable, and that surveys with multiple epochs can detect a more complete sample of stellar radio sources.","PeriodicalId":20753,"journal":{"name":"Publications of the Astronomical Society of Australia","volume":"11 1","pages":""},"PeriodicalIF":6.3,"publicationDate":"2023-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85525618","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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