A. Z. Lugo-Aranda, S. F. S'anchez, C. Espinosa-Ponce, C. L'opez-Cob'a, L. Galbany, J. Barrera-Ballesteros, L. Sánchez-Menguiano, J. Anderson
We present a new code named pyhiiextractor, which detects and extracts the main features (positions and radii) of clumpy ionized regions, i.e. candidate H ii regions, using $rm {H},alpha$ emission line images. Our code is optimized to be used on the dataproducts provided by the pipe3d pipeline (or dataproducts with such a format), applied to high-spatial resolution integral field spectroscopy data (like that provided by the AMUSING++ compilation, using muse). The code provides the properties of both the underlying stellar population and the emission lines for each detected H ii candidate. Furthermore, the code delivers a novel estimation of the diffuse ionized gas (DIG) component, independent of its physical properties, which enables a decontamination of the properties of the H ii regions from the DIG. Using simulated data, mimicking the expected observations of spiral galaxies, we characterize pyhiiextractor and its ability to extract the main properties of the H ii regions (and the DIG), including the line fluxes, ratios, and equivalent widths. Finally, we compare our code with other such tools adopted in the literature, which have been developed or used for similar purposes: pyhiiexplorer, sourceextractor, hiiphot, and astrodendro. We conclude that pyhiiextractor exceeds the performance of previous tools in aspects such as the number of recovered regions and the distribution of sizes and fluxes (an improvement that is especially noticeable for the faintest and smallest regions). pyhiiextractor is therefore an optimal tool to detect candidate H ii regions, offering an accurate estimation of their properties and a good decontamination of the DIG component.
{"title":"pyhiiextractor: a tool to detect and extract physical properties of H ii regions from integral field spectroscopic data","authors":"A. Z. Lugo-Aranda, S. F. S'anchez, C. Espinosa-Ponce, C. L'opez-Cob'a, L. Galbany, J. Barrera-Ballesteros, L. Sánchez-Menguiano, J. Anderson","doi":"10.1093/rasti/rzac001","DOIUrl":"https://doi.org/10.1093/rasti/rzac001","url":null,"abstract":"\u0000 We present a new code named pyhiiextractor, which detects and extracts the main features (positions and radii) of clumpy ionized regions, i.e. candidate H ii regions, using $rm {H},alpha$ emission line images. Our code is optimized to be used on the dataproducts provided by the pipe3d pipeline (or dataproducts with such a format), applied to high-spatial resolution integral field spectroscopy data (like that provided by the AMUSING++ compilation, using muse). The code provides the properties of both the underlying stellar population and the emission lines for each detected H ii candidate. Furthermore, the code delivers a novel estimation of the diffuse ionized gas (DIG) component, independent of its physical properties, which enables a decontamination of the properties of the H ii regions from the DIG. Using simulated data, mimicking the expected observations of spiral galaxies, we characterize pyhiiextractor and its ability to extract the main properties of the H ii regions (and the DIG), including the line fluxes, ratios, and equivalent widths. Finally, we compare our code with other such tools adopted in the literature, which have been developed or used for similar purposes: pyhiiexplorer, sourceextractor, hiiphot, and astrodendro. We conclude that pyhiiextractor exceeds the performance of previous tools in aspects such as the number of recovered regions and the distribution of sizes and fluxes (an improvement that is especially noticeable for the faintest and smallest regions). pyhiiextractor is therefore an optimal tool to detect candidate H ii regions, offering an accurate estimation of their properties and a good decontamination of the DIG component.","PeriodicalId":367327,"journal":{"name":"RAS Techniques and Instruments","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125890256","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
B. Rackham, N. Espinoza, S. Berdyugina, H. Korhonen, R. MacDonald, B. Montet, B. Morris, M. Oshagh, A. Shapiro, Y. Unruh, E. Quintana, R. Zellem, D. Apai, T. Barclay, J. Barstow, G. Bruno, L. Carone, S. Casewell, Heather Cegla, S. Criscuoli, C. Fischer, D. Fournier, M. Giampapa, H. Giles, A. Iyer, G. Kopp, N. Kostogryz, N. Krivova, M. Mallonn, C. McGruder, K. Molaverdikhani, E. Newton, Mayukh Panja, S. Peacock, K. Reardon, R. Roettenbacher, G. Scandariato, S. Solanki, K. Stassun, O. Steiner, K. Stevenson, J. Tregloan-Reed, A. Valio, S. Wedemeyer, L. Welbanks, Jie Yu, M. Alam, J. Davenport, D. Deming, C. Dong, E. Ducrot, C. Fisher, E. Gilbert, V. Kostov, M. López-Morales, M. Line, T. Močnik, S. Mullally, R. Paudel, I. Ribas, J. Valenti
Study Analysis Group 21 (SAG21) of NASA’s Exoplanet Exploration Program Analysis Group (ExoPAG) was organized to study the effect of stellar contamination on space-based transmission spectroscopy, a method for studying exoplanetary atmospheres by measuring the wavelength-dependent radius of a planet as it transits its star. Transmission spectroscopy relies on a precise understanding of the spectrum of the star being occulted. However, stars are not homogeneous, constant light sources but have temporally evolving photospheres and chromospheres with inhomogeneities like spots, faculae, plages, granules, and flares. This SAG brought together an interdisciplinary team of more than 100 scientists, with observers and theorists from the heliophysics, stellar astrophysics, planetary science, and exoplanetary atmosphere research communities, to study the current research needs that can be addressed in this context to make the most of transit studies from current NASA facilities like HST and JWST. The analysis produced 14 findings, which fall into three Science Themes encompassing (1) how the Sun is used as our best laboratory to calibrate our understanding of stellar heterogeneities (‘The Sun as the Stellar Benchmark’), (2) how stars other than the Sun extend our knowledge of heterogeneities (‘Surface Heterogeneities of Other Stars’) and (3) how to incorporate information gathered for the Sun and other stars into transit studies (‘Mapping Stellar Knowledge to Transit Studies’). In this invited review, we largely reproduce the final report of SAG21 as a contribution to the peer-reviewed literature.
{"title":"The effect of stellar contamination on low-resolution transmission spectroscopy: Needs identified by NASA’s Exoplanet Exploration Program Study Analysis Group 21","authors":"B. Rackham, N. Espinoza, S. Berdyugina, H. Korhonen, R. MacDonald, B. Montet, B. Morris, M. Oshagh, A. Shapiro, Y. Unruh, E. Quintana, R. Zellem, D. Apai, T. Barclay, J. Barstow, G. Bruno, L. Carone, S. Casewell, Heather Cegla, S. Criscuoli, C. Fischer, D. Fournier, M. Giampapa, H. Giles, A. Iyer, G. Kopp, N. Kostogryz, N. Krivova, M. Mallonn, C. McGruder, K. Molaverdikhani, E. Newton, Mayukh Panja, S. Peacock, K. Reardon, R. Roettenbacher, G. Scandariato, S. Solanki, K. Stassun, O. Steiner, K. Stevenson, J. Tregloan-Reed, A. Valio, S. Wedemeyer, L. Welbanks, Jie Yu, M. Alam, J. Davenport, D. Deming, C. Dong, E. Ducrot, C. Fisher, E. Gilbert, V. Kostov, M. López-Morales, M. Line, T. Močnik, S. Mullally, R. Paudel, I. Ribas, J. Valenti","doi":"10.1093/rasti/rzad009","DOIUrl":"https://doi.org/10.1093/rasti/rzad009","url":null,"abstract":"\u0000 Study Analysis Group 21 (SAG21) of NASA’s Exoplanet Exploration Program Analysis Group (ExoPAG) was organized to study the effect of stellar contamination on space-based transmission spectroscopy, a method for studying exoplanetary atmospheres by measuring the wavelength-dependent radius of a planet as it transits its star. Transmission spectroscopy relies on a precise understanding of the spectrum of the star being occulted. However, stars are not homogeneous, constant light sources but have temporally evolving photospheres and chromospheres with inhomogeneities like spots, faculae, plages, granules, and flares. This SAG brought together an interdisciplinary team of more than 100 scientists, with observers and theorists from the heliophysics, stellar astrophysics, planetary science, and exoplanetary atmosphere research communities, to study the current research needs that can be addressed in this context to make the most of transit studies from current NASA facilities like HST and JWST. The analysis produced 14 findings, which fall into three Science Themes encompassing (1) how the Sun is used as our best laboratory to calibrate our understanding of stellar heterogeneities (‘The Sun as the Stellar Benchmark’), (2) how stars other than the Sun extend our knowledge of heterogeneities (‘Surface Heterogeneities of Other Stars’) and (3) how to incorporate information gathered for the Sun and other stars into transit studies (‘Mapping Stellar Knowledge to Transit Studies’). In this invited review, we largely reproduce the final report of SAG21 as a contribution to the peer-reviewed literature.","PeriodicalId":367327,"journal":{"name":"RAS Techniques and Instruments","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114556985","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
M. Druett, A. G. Pietrow, G. Vissers, C. Robustini, Flavio Calvo
Most modern solar observatories deliver data products formatted as 3D spatio-temporal data cubes, that contain additional, higher dimensions with spectral and/or polarimetric information. This multi-dimensional complexity presents a major challenge when browsing for features of interest in several dimensions simultaneously. We developed the COlor COllapsed PLOTting (COCOPLOT) software as a quick-look and context image software, to convey spectral profile or time evolution from all the spatial pixels (x, y) in a 3D [nx, ny, nλ] or [nx, ny, nt] data cube as a single image, using colour. This can avoid the need to scan through many wavelengths, creating difference and composite images when searching for signals satisfying multiple criteria. Filters are generated for the red, green, and blue channels by selecting values of interest to highlight in each channel, and their weightings. These filters are combined with the data cube over the third dimension axis to produce an nx × ny × 3 cube displayed as one true colour image. Some use cases are presented for data from the Swedish 1-m Solar Telescope and Interface Region Imaging Spectrograph (IRIS), including Hα solar flare data, a comparison with k-means clustering for identifying asymmetries in the Ca ii K line and off-limb coronal rain in IRIS C ii slit-jaw images. These illustrate identification by colour alone using COCOPLOT of locations including line wing or central enhancement, broadening, wing absorption, and sites with intermittent flows or time-persistent features. COCOPLOT is publicly available in both IDL and Python.
大多数现代太阳观测站提供的数据产品格式为三维时空数据立方体，其中包含附加的、更高维度的光谱和/或极化信息。当同时在多个维度上浏览感兴趣的特性时，这种多维复杂性提出了一个主要挑战。我们开发了颜色折叠绘图(COCOPLOT)软件作为快速查看和上下文图像软件，以3D [nx, ny, nλ]或[nx, ny, nt]数据立方体中的所有空间像素(x, y)作为单个图像，使用颜色来传达光谱轮廓或时间演变。这可以避免在搜索满足多个标准的信号时需要扫描多个波长，从而产生差异和合成图像。通过选择要在每个通道中突出显示的感兴趣的值及其权重，为红色、绿色和蓝色通道生成过滤器。这些过滤器与第三维轴上的数据立方体相结合，产生一个nx × ny × 3立方体，显示为一个真彩色图像。介绍了瑞典1米太阳望远镜和界面区域成像光谱仪(IRIS)数据的一些用例，包括Hα太阳耀斑数据，与K均值聚类的比较，以识别Ca ii K线的不对称性和IRIS ii裂隙颚图像中的离翼日冕雨。这些说明了使用COCOPLOT单独通过颜色识别的位置，包括线翼或中心增强，扩大，翼吸收，以及间歇性流动或时间持续特征的位置。COCOPLOT在IDL和Python中都是公开的。
{"title":"COCOPLOT: COlor COllapsed PLOTting software Using colour to view 3D data as a 2D image","authors":"M. Druett, A. G. Pietrow, G. Vissers, C. Robustini, Flavio Calvo","doi":"10.1093/rasti/rzac003","DOIUrl":"https://doi.org/10.1093/rasti/rzac003","url":null,"abstract":"\u0000 Most modern solar observatories deliver data products formatted as 3D spatio-temporal data cubes, that contain additional, higher dimensions with spectral and/or polarimetric information. This multi-dimensional complexity presents a major challenge when browsing for features of interest in several dimensions simultaneously. We developed the COlor COllapsed PLOTting (COCOPLOT) software as a quick-look and context image software, to convey spectral profile or time evolution from all the spatial pixels (x, y) in a 3D [nx, ny, nλ] or [nx, ny, nt] data cube as a single image, using colour. This can avoid the need to scan through many wavelengths, creating difference and composite images when searching for signals satisfying multiple criteria. Filters are generated for the red, green, and blue channels by selecting values of interest to highlight in each channel, and their weightings. These filters are combined with the data cube over the third dimension axis to produce an nx × ny × 3 cube displayed as one true colour image. Some use cases are presented for data from the Swedish 1-m Solar Telescope and Interface Region Imaging Spectrograph (IRIS), including Hα solar flare data, a comparison with k-means clustering for identifying asymmetries in the Ca ii K line and off-limb coronal rain in IRIS C ii slit-jaw images. These illustrate identification by colour alone using COCOPLOT of locations including line wing or central enhancement, broadening, wing absorption, and sites with intermittent flows or time-persistent features. COCOPLOT is publicly available in both IDL and Python.","PeriodicalId":367327,"journal":{"name":"RAS Techniques and Instruments","volume":"99 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131443377","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
C. Louis, C. Jackman, J. Grießmeier, O. Wucknitz, D. J. McKenna, P. Murphy, P. Gallagher, E. Carley, D. '. Fionnag'ain, A. Golden, J. McCauley, P. Callanan, M. Redman, C. Vocks
The Low Frequency Array (LOFAR) is an international radio telescope array, consisting of 38 stations in the Netherlands and 14 international stations spread over Europe. Here we present an observation method to study the jovian decametric radio emissions from several LOFAR stations (here Birr Castle in Ireland, Nançay in France and Postdam in Germany), at high temporal and spectral resolution. This method is based on prediction tools, such as radio emission simulations and probability maps, and data processing. We report an observation of Io-induced decametric emission from June 2021, and a first case study of the substructures that compose the macroscopic emissions (called millisecond bursts). The study of these bursts make it possible to determine the electron populations at the origin of these emissions. We then present several possible future avenues for study based on these observations. The methodology and study perspectives described in this paper can be applied to new observations of jovian radio emissions induced by Io, but also by Ganymede or Europa, or jovian auroral radio emissions.
{"title":"Method to observe Jupiter’s radio emissions at high resolution using multiple LOFAR stations: A first case study of the Io-decametric emission using the Irish IE613, French FR606 and German DE604 stations","authors":"C. Louis, C. Jackman, J. Grießmeier, O. Wucknitz, D. J. McKenna, P. Murphy, P. Gallagher, E. Carley, D. '. Fionnag'ain, A. Golden, J. McCauley, P. Callanan, M. Redman, C. Vocks","doi":"10.1093/rasti/rzac005","DOIUrl":"https://doi.org/10.1093/rasti/rzac005","url":null,"abstract":"\u0000 The Low Frequency Array (LOFAR) is an international radio telescope array, consisting of 38 stations in the Netherlands and 14 international stations spread over Europe. Here we present an observation method to study the jovian decametric radio emissions from several LOFAR stations (here Birr Castle in Ireland, Nançay in France and Postdam in Germany), at high temporal and spectral resolution. This method is based on prediction tools, such as radio emission simulations and probability maps, and data processing. We report an observation of Io-induced decametric emission from June 2021, and a first case study of the substructures that compose the macroscopic emissions (called millisecond bursts). The study of these bursts make it possible to determine the electron populations at the origin of these emissions. We then present several possible future avenues for study based on these observations. The methodology and study perspectives described in this paper can be applied to new observations of jovian radio emissions induced by Io, but also by Ganymede or Europa, or jovian auroral radio emissions.","PeriodicalId":367327,"journal":{"name":"RAS Techniques and Instruments","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125521536","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Augustin Marignier, J. McEwen, A. Ferreira, T. Kitching
In this work, we describe a framework for solving spherical inverse imaging problems using posterior sampling for full uncertainty quantification. Inverse imaging problems defined on the sphere arise in many fields, including seismology and cosmology where images are defined on the globe and the cosmic sphere, and are generally high-dimensional and computationally expensive. As a result, sampling the posterior distribution of spherical imaging problems is a challenging task. Our framework leverages a proximal Markov chain Monte Carlo (MCMC) algorithm to efficiently sample the high-dimensional space of spherical images with a sparsity-promoting wavelet prior. We detail the modifications needed for the algorithm to be applied to spherical problems, and give special consideration to the crucial forward modelling step which contains computationally expensive spherical harmonic transforms. By sampling the posterior, our framework allows for full and flexible uncertainty quantification, something which is not possible with other methods based on, for example, convex optimisation. We demonstrate our framework in practice on full-sky cosmological mass-mapping and to the construction of phase velocity maps in global seismic tomography. We find that our approach is potentially useful at moderate resolutions, such as those of interest in seismology. However at high resolutions, such as those required for astrophysical applications, the poor scaling of the complexity of spherical harmonic transforms severely limits our method, which may be resolved with future GPU implementations. A new Python package, pxmcmc, containing the proximal MCMC sampler, measurement operators, wavelet transforms and sparse priors is made publicly available.
{"title":"Posterior sampling for inverse imaging problems on the sphere in seismology and cosmology","authors":"Augustin Marignier, J. McEwen, A. Ferreira, T. Kitching","doi":"10.1093/rasti/rzac010","DOIUrl":"https://doi.org/10.1093/rasti/rzac010","url":null,"abstract":"\u0000 In this work, we describe a framework for solving spherical inverse imaging problems using posterior sampling for full uncertainty quantification. Inverse imaging problems defined on the sphere arise in many fields, including seismology and cosmology where images are defined on the globe and the cosmic sphere, and are generally high-dimensional and computationally expensive. As a result, sampling the posterior distribution of spherical imaging problems is a challenging task. Our framework leverages a proximal Markov chain Monte Carlo (MCMC) algorithm to efficiently sample the high-dimensional space of spherical images with a sparsity-promoting wavelet prior. We detail the modifications needed for the algorithm to be applied to spherical problems, and give special consideration to the crucial forward modelling step which contains computationally expensive spherical harmonic transforms. By sampling the posterior, our framework allows for full and flexible uncertainty quantification, something which is not possible with other methods based on, for example, convex optimisation. We demonstrate our framework in practice on full-sky cosmological mass-mapping and to the construction of phase velocity maps in global seismic tomography. We find that our approach is potentially useful at moderate resolutions, such as those of interest in seismology. However at high resolutions, such as those required for astrophysical applications, the poor scaling of the complexity of spherical harmonic transforms severely limits our method, which may be resolved with future GPU implementations. A new Python package, pxmcmc, containing the proximal MCMC sampler, measurement operators, wavelet transforms and sparse priors is made publicly available.","PeriodicalId":367327,"journal":{"name":"RAS Techniques and Instruments","volume":"75 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125916311","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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