E. Gharib-Nezhad, N. Batalha, K. Chubb, Richard Freedman, Iouli E. Gordon, Robert R Gamache, Robert J Hargreaves, Nikole K Lewis, Jonathan Tennyson, S. Yurchenko
When computing cross-sections from a line list, the result depends not only on the line strength, but also the line shape, pressure-broadening parameters, and line wing cut-off (i.e. the maximum distance calculated from each line centre). Pressure-broadening can be described using the Lorentz lineshape, but it is known to not represent the true absorption in the far wings. Both theory and experiment have shown that far from the line centre, non-Lorentzian behaviour controls the shape of the wings and the Lorentz lineshape fails to accurately characterize the absorption, leading to an underestimation or overestimation of the opacity continuum depending on the molecular species involved. The line wing cut-off is an often overlooked parameter when calculating absorption cross sections, but can have a significant effect on the appearance of the spectrum since it dictates the extent of the line wing that contributes to the calculation either side of every line centre. Therefore, when used to analyse exoplanet and brown dwarf spectra, an inaccurate choice for the line wing cut-off can result in errors in the opacity continuum, which propagate into the modeled transit spectra, and ultimately impact/bias the interpretation of observational spectra, and the derived composition and thermal structure. Here, we examine the different methods commonly utilized to calculate the wing cut-off and propose a standard practice procedure (i.e. absolute value of 25 cm−1 for P ≤ 200 bar and 100 cm−1 for P > 200 bar) to generate molecular opacities which will be used by the open-access MAESTRO (Molecules and Atoms in Exoplanet Science: Tools and Resources for Opacities) database. The pressing need for new measurements and theoretical studies of the far-wings is highlighted.
{"title":"The impact of spectral line wing cut-off: Recommended standard method with application to MAESTRO opacity database","authors":"E. Gharib-Nezhad, N. Batalha, K. Chubb, Richard Freedman, Iouli E. Gordon, Robert R Gamache, Robert J Hargreaves, Nikole K Lewis, Jonathan Tennyson, S. Yurchenko","doi":"10.1093/rasti/rzad058","DOIUrl":"https://doi.org/10.1093/rasti/rzad058","url":null,"abstract":"When computing cross-sections from a line list, the result depends not only on the line strength, but also the line shape, pressure-broadening parameters, and line wing cut-off (i.e. the maximum distance calculated from each line centre). Pressure-broadening can be described using the Lorentz lineshape, but it is known to not represent the true absorption in the far wings. Both theory and experiment have shown that far from the line centre, non-Lorentzian behaviour controls the shape of the wings and the Lorentz lineshape fails to accurately characterize the absorption, leading to an underestimation or overestimation of the opacity continuum depending on the molecular species involved. The line wing cut-off is an often overlooked parameter when calculating absorption cross sections, but can have a significant effect on the appearance of the spectrum since it dictates the extent of the line wing that contributes to the calculation either side of every line centre. Therefore, when used to analyse exoplanet and brown dwarf spectra, an inaccurate choice for the line wing cut-off can result in errors in the opacity continuum, which propagate into the modeled transit spectra, and ultimately impact/bias the interpretation of observational spectra, and the derived composition and thermal structure. Here, we examine the different methods commonly utilized to calculate the wing cut-off and propose a standard practice procedure (i.e. absolute value of 25 cm−1 for P ≤ 200 bar and 100 cm−1 for P > 200 bar) to generate molecular opacities which will be used by the open-access MAESTRO (Molecules and Atoms in Exoplanet Science: Tools and Resources for Opacities) database. The pressing need for new measurements and theoretical studies of the far-wings is highlighted.","PeriodicalId":367327,"journal":{"name":"RAS Techniques and Instruments","volume":"58 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139162903","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}
James D. Cole, Simon Sheridan, Sungwoo Lim, H. Sargeant, M. Anand, Andrew D Morse
This study describes the development of an instrument known as the Dynamic Mass Instrument (DMI) for use in microwave heating experiments that will allow greater insight in to the efficacy of the technique. A commercially available load cell is used as the main mechanism of mass measurement with a load arm used to provide microwave isolation of the load cell. The DMI is capable of measuring changes in mass with a mass range of 100 g to 200 g with an accuracy of ± 0.1 g in an environment of 250 W, 2.45 GHz microwaves under a working pressure of 3 mbar. A series of calibrations and experiments have been performed to quantify and clarify the behaviour of the instrument in different environments and scenarios and to ensure the DMI meets preset requirements. The DMI will, in future work, be used in In Situ Resource Utilisation (ISRU) experiments to examine in greater detail the efficacy of using microwave heating as a water extraction technique.
{"title":"Development and characterisation of a Dynamic Mass Instrument (DMI) for use in microwave heating experiments","authors":"James D. Cole, Simon Sheridan, Sungwoo Lim, H. Sargeant, M. Anand, Andrew D Morse","doi":"10.1093/rasti/rzad057","DOIUrl":"https://doi.org/10.1093/rasti/rzad057","url":null,"abstract":"\u0000 This study describes the development of an instrument known as the Dynamic Mass Instrument (DMI) for use in microwave heating experiments that will allow greater insight in to the efficacy of the technique. A commercially available load cell is used as the main mechanism of mass measurement with a load arm used to provide microwave isolation of the load cell. The DMI is capable of measuring changes in mass with a mass range of 100 g to 200 g with an accuracy of ± 0.1 g in an environment of 250 W, 2.45 GHz microwaves under a working pressure of 3 mbar. A series of calibrations and experiments have been performed to quantify and clarify the behaviour of the instrument in different environments and scenarios and to ensure the DMI meets preset requirements. The DMI will, in future work, be used in In Situ Resource Utilisation (ISRU) experiments to examine in greater detail the efficacy of using microwave heating as a water extraction technique.","PeriodicalId":367327,"journal":{"name":"RAS Techniques and Instruments","volume":"42 12","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138950913","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}
Anomaly detection algorithms are typically applied to static, unchanging, data features hand-crafted by the user. But how does a user systematically craft good features for anomalies that have never been seen? Here we couple deep learning with active learning – in which an Oracle iteratively labels small amounts of data selected algorithmically over a series of rounds – to automatically and dynamically improve the data features for efficient outlier detection. This approach, AHUNT, shows excellent performance on MNIST, CIFAR10, and Galaxy-DECaLS data, significantly outperforming both standard anomaly detection and active learning algorithms with static feature spaces. Beyond improved performance, AHUNT also allows the number of anomaly classes to grow organically in response to the Oracle’s evaluations. Extensive ablation studies explore the impact of Oracle question selection strategy and loss function on performance. We illustrate how the dynamic anomaly class taxonomy represents another step towards fully personalized rankings of different anomaly classes that reflect a user’s interests, allowing the algorithm to learn to ignore statistically significant but uninteresting outliers (e.g. noise). This should prove useful in the era of massive astronomical datasets serving diverse sets of users who can only review a tiny subset of the incoming data.
{"title":"Personalized anomaly detection using deep active learning","authors":"A. V. Sadr, Bruce A Bassett, Emmanuel Sekyi","doi":"10.1093/rasti/rzad032","DOIUrl":"https://doi.org/10.1093/rasti/rzad032","url":null,"abstract":"\u0000 Anomaly detection algorithms are typically applied to static, unchanging, data features hand-crafted by the user. But how does a user systematically craft good features for anomalies that have never been seen? Here we couple deep learning with active learning – in which an Oracle iteratively labels small amounts of data selected algorithmically over a series of rounds – to automatically and dynamically improve the data features for efficient outlier detection. This approach, AHUNT, shows excellent performance on MNIST, CIFAR10, and Galaxy-DECaLS data, significantly outperforming both standard anomaly detection and active learning algorithms with static feature spaces. Beyond improved performance, AHUNT also allows the number of anomaly classes to grow organically in response to the Oracle’s evaluations. Extensive ablation studies explore the impact of Oracle question selection strategy and loss function on performance. We illustrate how the dynamic anomaly class taxonomy represents another step towards fully personalized rankings of different anomaly classes that reflect a user’s interests, allowing the algorithm to learn to ignore statistically significant but uninteresting outliers (e.g. noise). This should prove useful in the era of massive astronomical datasets serving diverse sets of users who can only review a tiny subset of the incoming data.","PeriodicalId":367327,"journal":{"name":"RAS Techniques and Instruments","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126675810","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}
We present a machine learning (ML) approach for classifying kinematic profiles of elliptical galaxies in the Mapping Nearby Galaxies at Apache Point Observatory (MaNGA) survey. Previous studies employing ML to classify spectral data of galaxies have provided valuable insights into morphological galaxy classification. This study aims to enhance the understanding of galaxy kinematics by leveraging ML. The kinematics of 2,624 MaNGA elliptical galaxies are investigated using integral field spectroscopy (IFS) by classifying their one-dimensional velocity dispersion (VD) profiles. We utilised a total of 1,266 MaNGA VD profiles and employed a combination of unsupervised and supervised learning techniques. The unsupervised K-means algorithm classifies VD profiles into four categories: flat, decline, ascend, and irregular. A bagged decision trees classifier (TreeBagger) supervised ensemble is trained using visual tags, achieving 100% accuracy on the training set and 88% accuracy on the test set. Our analysis identifies the majority (68%) of MaNGA elliptical galaxies presenting flat VD profiles, which requires further investigation into the implications of the Dark Matter problem.
我们提出了一种机器学习(ML)方法，用于在Apache Point Observatory (MaNGA)调查中对椭圆星系的运动剖面进行分类。以往利用机器学习对星系光谱数据进行分类的研究为星系形态学分类提供了有价值的见解。本文利用积分场光谱(IFS)对2624个MaNGA椭圆星系的一维速度色散(VD)分布进行了分类，研究了它们的运动学特性。我们总共使用了1266个MaNGA VD档案，并结合了无监督和有监督的学习技术。无监督K-means算法将VD轮廓分为四类:平坦、下降、上升和不规则。使用视觉标签训练袋装决策树分类器(TreeBagger)监督集成，在训练集上达到100%的准确率，在测试集上达到88%的准确率。我们的分析表明，大多数(68%)的日本椭圆星系呈现平坦的VD轮廓，这需要进一步研究暗物质问题的含义。
{"title":"Classifying MaNGA Velocity Dispersion Profiles by Machine Learning","authors":"Yi Duann, Yong Tian, Chung-Ming Ko","doi":"10.1093/rasti/rzad044","DOIUrl":"https://doi.org/10.1093/rasti/rzad044","url":null,"abstract":"\u0000 We present a machine learning (ML) approach for classifying kinematic profiles of elliptical galaxies in the Mapping Nearby Galaxies at Apache Point Observatory (MaNGA) survey. Previous studies employing ML to classify spectral data of galaxies have provided valuable insights into morphological galaxy classification. This study aims to enhance the understanding of galaxy kinematics by leveraging ML. The kinematics of 2,624 MaNGA elliptical galaxies are investigated using integral field spectroscopy (IFS) by classifying their one-dimensional velocity dispersion (VD) profiles. We utilised a total of 1,266 MaNGA VD profiles and employed a combination of unsupervised and supervised learning techniques. The unsupervised K-means algorithm classifies VD profiles into four categories: flat, decline, ascend, and irregular. A bagged decision trees classifier (TreeBagger) supervised ensemble is trained using visual tags, achieving 100% accuracy on the training set and 88% accuracy on the test set. Our analysis identifies the majority (68%) of MaNGA elliptical galaxies presenting flat VD profiles, which requires further investigation into the implications of the Dark Matter problem.","PeriodicalId":367327,"journal":{"name":"RAS Techniques and Instruments","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121523333","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}
A. Nunes, J. Gamper, S. Chapman, M. Friel, J. Gjerloev
The Newcomb-Benford Law (NBL) prescribes the probability distribution of the first digit of variables which explore a broad range under conditions including aggregation. Long-term space weather relevant observations and indices necessarily incorporate changes in the contributing number and types of observing instrumentation over time and we find that this can be detected solely by comparison with the NBL. It detects when upstream solar wind magnetic field OMNI High Resolution (HRO) Interplanetary Magnetic Field incorporated new data from the WIND and Advanced Composition Explorer (ACE) spacecraft after 1995. NBL comparison can detect underlying changes in the geomagnetic Auroral Electrojet (AE) index (activity dependent background subtraction) and the SuperMAG Electrojet (SME) index (different station types) that select individual stations showing the largest deflection, but not where station data are averaged, as in the SuperMAG Ring Current (SMR) index. As composite indices become more widespread across the geosciences, the NBL may provide a generic, data processing independent flag indicating changes in the constituent raw data, calibration or sampling method.
Newcomb-Benford定律(NBL)规定了变量的第一位数的概率分布，这些变量在包括聚集在内的条件下探索了一个很宽的范围。与空间天气相关的长期观测和指数必然包含观测仪器的贡献数量和类型随时间的变化，我们发现这可以仅通过与NBL的比较来检测。欧姆尼高分辨率行星际磁场(OMNI High Resolution Interplanetary magnetic，简称HRO)将wind和先进成分探测器(Advanced Composition Explorer，简称ACE) 1995年之后的新数据整合在一起，用于探测上游太阳风磁场。NBL对比可以检测地磁极光电喷(AE)指数(活动相关背景减法)和SuperMAG电喷(SME)指数(不同台站类型)的潜在变化，这些指数选择显示最大偏转的单个台站，而不是像SuperMAG环电流(SMR)指数那样平均台站数据。随着复合指数在地球科学领域的应用越来越广泛，NBL可能会提供一个通用的、独立于数据处理的标志，表明原始数据、校准或抽样方法的变化。
{"title":"Newcomb-Benford Law as a generic flag for changes in the derivation of long-term solar terrestrial physics timeseries","authors":"A. Nunes, J. Gamper, S. Chapman, M. Friel, J. Gjerloev","doi":"10.1093/rasti/rzad041","DOIUrl":"https://doi.org/10.1093/rasti/rzad041","url":null,"abstract":"\u0000 The Newcomb-Benford Law (NBL) prescribes the probability distribution of the first digit of variables which explore a broad range under conditions including aggregation. Long-term space weather relevant observations and indices necessarily incorporate changes in the contributing number and types of observing instrumentation over time and we find that this can be detected solely by comparison with the NBL. It detects when upstream solar wind magnetic field OMNI High Resolution (HRO) Interplanetary Magnetic Field incorporated new data from the WIND and Advanced Composition Explorer (ACE) spacecraft after 1995. NBL comparison can detect underlying changes in the geomagnetic Auroral Electrojet (AE) index (activity dependent background subtraction) and the SuperMAG Electrojet (SME) index (different station types) that select individual stations showing the largest deflection, but not where station data are averaged, as in the SuperMAG Ring Current (SMR) index. As composite indices become more widespread across the geosciences, the NBL may provide a generic, data processing independent flag indicating changes in the constituent raw data, calibration or sampling method.","PeriodicalId":367327,"journal":{"name":"RAS Techniques and Instruments","volume":"101 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128973923","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}
In this paper we consider the modelling of diffracted stray light in heliospheric imagers. The emphasis is on the imagers proposed by RAL Space as part of the phase A/B1 study for ESA’s Vigil (formerly called Lagrange) L5 monitoring mission. In order to handle the extreme diffraction angles, a one-dimensional version of the PROPER diffraction modelling library has been developed. This is used to compute patterns at the lens aperture, and the standard two-dimensional version is then used to continue propagation to the sensor plane. The effects of key instrument and modelling parameters are analysed with a view to optimizing accuracy of the modelling and the diffraction performance of the instrument.
{"title":"Simulating diffraction effects in heliospheric imagers","authors":"S. Tappin, J. Davies, C. Eyles","doi":"10.1093/rasti/rzad033","DOIUrl":"https://doi.org/10.1093/rasti/rzad033","url":null,"abstract":"\u0000 In this paper we consider the modelling of diffracted stray light in heliospheric imagers. The emphasis is on the imagers proposed by RAL Space as part of the phase A/B1 study for ESA’s Vigil (formerly called Lagrange) L5 monitoring mission. In order to handle the extreme diffraction angles, a one-dimensional version of the PROPER diffraction modelling library has been developed. This is used to compute patterns at the lens aperture, and the standard two-dimensional version is then used to continue propagation to the sensor plane. The effects of key instrument and modelling parameters are analysed with a view to optimizing accuracy of the modelling and the diffraction performance of the instrument.","PeriodicalId":367327,"journal":{"name":"RAS Techniques and Instruments","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123958156","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}
S. Killey, I. J. Rae, S. Chakraborty, A. W. Smith, S. Bentley, M. Bakrania, R. Wainwright, C. Watt, J. Sandhu
The behaviour of relativistic electrons in the radiation belt is difficult to diagnose as their dynamics are controlled by simultaneous physics processes, some of which may be still unknown. Signatures of these physical processes are difficult to identify in large amounts of data; therefore, a machine learning approach is developed to classify energetic electron distributions which have been driven by different mechanisms. A series of unsupervised machine learning tools have been applied to 7-years of Van Allen Probe Relativistic Electron Proton Telescope data to identify 6 different typical types of plasma conditions, each with a distinctly shaped energy-dependent pitch angle distribution (PAD). The PADs at lower energies have shapes as expected from previous studies - either butterfly, pancake or flattop, providing evidence that machine learning has been able to reliably classify the relativistic electrons in the radiation belts. Further applications of this technique could be applied to other space plasma regions, and datasets from inner heliospheric missions such as Parker Solar Probe and Solar Orbiter, to planetary magnetospheres and the JUICE mission. Understanding pitch angle distributions across the heliosphere enables researchers to determine the physical mechanisms that drive pitch angle evolution and investigate their spatial and temporal dependence and physical properties.
{"title":"Using Machine Learning to Diagnose Relativistic Electron Distributions in the Van Allen Radiation Belts","authors":"S. Killey, I. J. Rae, S. Chakraborty, A. W. Smith, S. Bentley, M. Bakrania, R. Wainwright, C. Watt, J. Sandhu","doi":"10.1093/rasti/rzad035","DOIUrl":"https://doi.org/10.1093/rasti/rzad035","url":null,"abstract":"\u0000 The behaviour of relativistic electrons in the radiation belt is difficult to diagnose as their dynamics are controlled by simultaneous physics processes, some of which may be still unknown. Signatures of these physical processes are difficult to identify in large amounts of data; therefore, a machine learning approach is developed to classify energetic electron distributions which have been driven by different mechanisms. A series of unsupervised machine learning tools have been applied to 7-years of Van Allen Probe Relativistic Electron Proton Telescope data to identify 6 different typical types of plasma conditions, each with a distinctly shaped energy-dependent pitch angle distribution (PAD). The PADs at lower energies have shapes as expected from previous studies - either butterfly, pancake or flattop, providing evidence that machine learning has been able to reliably classify the relativistic electrons in the radiation belts. Further applications of this technique could be applied to other space plasma regions, and datasets from inner heliospheric missions such as Parker Solar Probe and Solar Orbiter, to planetary magnetospheres and the JUICE mission. Understanding pitch angle distributions across the heliosphere enables researchers to determine the physical mechanisms that drive pitch angle evolution and investigate their spatial and temporal dependence and physical properties.","PeriodicalId":367327,"journal":{"name":"RAS Techniques and Instruments","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128537393","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}
Daniel Le Corre, D. Mary, N. Mason, J. Bernard-Salas, Nick Cox
Pits, or pit craters, are near-circular depressions found in planetary surfaces, which are generally formed through gravitational collapse. Pits will be primary targets for future space exploration and habitability for their presence on most rocky Solar System surfaces and their potential to be entrances to sub-surface cavities. This is particularly true on Mars, where caves have been simulated to harbour stable reserves of ice water across much of the surface. Caves can also provide natural shelter from the high radiation dosages experienced at the surface. Since pits are rarely found to have corresponding high-resolution elevation data, tools are required for approximating their depths in order to find those which are the ideal candidates for follow-up remote investigation and future exploration. The Pit Topography from Shadows (PITS) tool has been developed to automatically calculate the apparent depth of a pit (h) by measuring the width of its shadow as it appears in satellite imagery. The tool requires just one cropped single- or multi-band image of a pit to calculate a profile of h along the length of the shadow, thus allowing for depth calculation where altimetry or stereo image data is not available. We also present a method for correcting shadow width measurements made in non-nadir observations for all possible values of emission and solar/satellite azimuth angles. Shadows are extracted using image segmentation in the form of k-means clustering and silhouette analysis. Across 19 shadow-labelled Mars Reconnaissance Orbiter red-band HiRISE images of Atypical Pit Craters (APCs) from the Mars Global Cave Candidate Catalog (MGC3), PITS detected 99.6 per cent of all shadow pixels (with 94.8 per cent of all detections being true shadow pixels). Following this testing, PITS has been applied to 123 red-band HiRISE images containing 88 APCs, which revealed an improvement in the variation of the calculated h due to emission angle correction, and also found 10 APCs that could be good candidates for cave entrances on Mars due to their h profiles.
{"title":"Automatically calculating the apparent depths of pits using the Pit Topography from Shadows (PITS) tool","authors":"Daniel Le Corre, D. Mary, N. Mason, J. Bernard-Salas, Nick Cox","doi":"10.1093/rasti/rzad037","DOIUrl":"https://doi.org/10.1093/rasti/rzad037","url":null,"abstract":"\u0000 Pits, or pit craters, are near-circular depressions found in planetary surfaces, which are generally formed through gravitational collapse. Pits will be primary targets for future space exploration and habitability for their presence on most rocky Solar System surfaces and their potential to be entrances to sub-surface cavities. This is particularly true on Mars, where caves have been simulated to harbour stable reserves of ice water across much of the surface. Caves can also provide natural shelter from the high radiation dosages experienced at the surface. Since pits are rarely found to have corresponding high-resolution elevation data, tools are required for approximating their depths in order to find those which are the ideal candidates for follow-up remote investigation and future exploration. The Pit Topography from Shadows (PITS) tool has been developed to automatically calculate the apparent depth of a pit (h) by measuring the width of its shadow as it appears in satellite imagery. The tool requires just one cropped single- or multi-band image of a pit to calculate a profile of h along the length of the shadow, thus allowing for depth calculation where altimetry or stereo image data is not available. We also present a method for correcting shadow width measurements made in non-nadir observations for all possible values of emission and solar/satellite azimuth angles. Shadows are extracted using image segmentation in the form of k-means clustering and silhouette analysis. Across 19 shadow-labelled Mars Reconnaissance Orbiter red-band HiRISE images of Atypical Pit Craters (APCs) from the Mars Global Cave Candidate Catalog (MGC3), PITS detected 99.6 per cent of all shadow pixels (with 94.8 per cent of all detections being true shadow pixels). Following this testing, PITS has been applied to 123 red-band HiRISE images containing 88 APCs, which revealed an improvement in the variation of the calculated h due to emission angle correction, and also found 10 APCs that could be good candidates for cave entrances on Mars due to their h profiles.","PeriodicalId":367327,"journal":{"name":"RAS Techniques and Instruments","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126969015","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}
J. Dumayne, I. Hook, S. Williams, G. A. Lowes, D. Head, A. Fritz, O. Graur, B. Holwerda, A. Humphrey, A. Milligan, M. Nicholl, B. Roukema, P. Wiseman
The Rubin Observatory’s 10-year Legacy Survey of Space and Time will observe near to 20 billion galaxies. For each galaxy the properties can be inferred. Approximately 105 galaxies observed per year will contain Type Ia supernovae (SNe), allowing SN host-galaxy properties to be calculated on a large scale. Measuring the properties of SN host-galaxies serves two main purposes. The first is that there are known correlations between host-galaxy type and supernova type, which can be used to aid in the classification of SNe. Secondly, Type Ia SNe exhibit correlations between host-galaxy properties and the peak luminosities of the SNe, which has implications for their use as standardisable candles in cosmology. We have used simulations to quantify the improvement in host-galaxy stellar mass (M*) measurements when supplementing photometry from Rubin with spectroscopy from the 4-metre Multi-Object Spectroscopic Telescope (4MOST) instrument. We provide results in the form of expected uncertainties in M* for galaxies with 0.1 < z < 0.9 and 18 < rAB < 25. We show that for galaxies mag 22 and brighter, combining Rubin and 4MOST data reduces the uncertainty measurements of galaxy M* by more than a factor of 2 compared with Rubin data alone. This applies for elliptical and Sc type hosts. We demonstrate that the reduced uncertainties in M* lead to an improvement of 7 per cent in the precision of the ‘mass step’ correction. We expect our improved measurements of host-galaxy properties to aid in the photometric classification of SNe observed by Rubin.
鲁宾天文台为期10年的时空遗产调查将观测近200亿个星系。对于每一个星系，这些性质都可以推断出来。每年观测到的大约105个星系将包含Ia型超新星(SNe)，这使得大规模计算SN宿主星系的性质成为可能。测量SN宿主星系的性质有两个主要目的。首先，宿主星系类型和超新星类型之间存在已知的相关性，这可以用来帮助对SNe进行分类。其次，Ia型超新星表现出宿主星系特性与超新星峰值亮度之间的相关性，这对它们在宇宙学中作为标准烛光的使用具有重要意义。我们使用模拟来量化宿主星系恒星质量(M*)测量的改进，当用4米多目标光谱望远镜(4MOST)仪器补充鲁宾的光度测量时。对于0.1 < z < 0.9和18 < rAB < 25的星系，我们以M*的预期不确定度的形式提供了结果。我们表明，对于22等及更亮的星系，与单独的鲁宾数据相比，将鲁宾和4MOST数据结合起来，可以将星系M*的不确定性测量降低2倍以上。这适用于椭圆型和Sc型主机。我们证明M*中不确定性的减少导致“质量步进”校正精度提高7%。我们期望我们对宿主星系特性的改进测量有助于鲁宾观测到的SNe的光度分类。
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A. Longobardi, M. Fossati, M. Fumagalli, B. Agarwal, E. Lofthouse, Marta Galbiati, R. Dutta, Trystyn A. M. Berg, Louise A Welsh
We present two new tools for studying and modelling metal absorption lines in the circumgalactic medium. The first tool, dubbed “NMF Profile Maker” (NMF−PM), uses a non-negative matrix factorization (NMF) method and provides a robust means to generate large libraries of realistic metal absorption profiles. The method is trained and tested on 650 unsaturated metal absorbers in the redshift interval z = 0.9 − 4.2 with column densities between 11.2 ≤ log (N/cm−2) ≤ 16.3, obtained from high-resolution (R > 4000) and high signal-to-noise ratio (S/N ≥ 10) quasar spectroscopy. To avoid spurious features, we train on infinite S/N Voigt models of the observed line profiles derived using the code “Monte-Carlo Absorption Line Fitter” (MC−ALF), a novel automatic Bayesian fitting code that is the second tool we present in this work. MC−ALF is a Monte Carlo code based on nested sampling that, without the need for any prior guess or human intervention, can decompose metal lines into individual Voigt components. Both MC−ALF and NMF−PM are made publicly available to allow the community to produce large libraries of synthetic metal profiles and to reconstruct Voigt models of absorption lines in an automatic fashion. Both tools contribute to the scientific effort of simulating and analysing metal absorbers in very large spectroscopic surveys of quasars like the ongoing Dark Energy Spectroscopic Instrument (DESI), the 4-meter Multi-Object Spectroscopic Telescope (4MOST), and the WHT Enhanced Area Velocity Explorer (WEAVE) surveys.
{"title":"Towards an automatic approach to modelling the circumgalactic medium: new tools for mock making and fitting of metal profiles in large surveys","authors":"A. Longobardi, M. Fossati, M. Fumagalli, B. Agarwal, E. Lofthouse, Marta Galbiati, R. Dutta, Trystyn A. M. Berg, Louise A Welsh","doi":"10.1093/rasti/rzad031","DOIUrl":"https://doi.org/10.1093/rasti/rzad031","url":null,"abstract":"\u0000 We present two new tools for studying and modelling metal absorption lines in the circumgalactic medium. The first tool, dubbed “NMF Profile Maker” (NMF−PM), uses a non-negative matrix factorization (NMF) method and provides a robust means to generate large libraries of realistic metal absorption profiles. The method is trained and tested on 650 unsaturated metal absorbers in the redshift interval z = 0.9 − 4.2 with column densities between 11.2 ≤ log (N/cm−2) ≤ 16.3, obtained from high-resolution (R > 4000) and high signal-to-noise ratio (S/N ≥ 10) quasar spectroscopy. To avoid spurious features, we train on infinite S/N Voigt models of the observed line profiles derived using the code “Monte-Carlo Absorption Line Fitter” (MC−ALF), a novel automatic Bayesian fitting code that is the second tool we present in this work. MC−ALF is a Monte Carlo code based on nested sampling that, without the need for any prior guess or human intervention, can decompose metal lines into individual Voigt components. Both MC−ALF and NMF−PM are made publicly available to allow the community to produce large libraries of synthetic metal profiles and to reconstruct Voigt models of absorption lines in an automatic fashion. Both tools contribute to the scientific effort of simulating and analysing metal absorbers in very large spectroscopic surveys of quasars like the ongoing Dark Energy Spectroscopic Instrument (DESI), the 4-meter Multi-Object Spectroscopic Telescope (4MOST), and the WHT Enhanced Area Velocity Explorer (WEAVE) surveys.","PeriodicalId":367327,"journal":{"name":"RAS Techniques and Instruments","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114068582","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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