RAS Techniques and Instruments最新文献

Classifying LEO satellite platforms with boosted decision trees 利用增强决策树对低地轨道卫星平台进行分类

RAS Techniques and Instruments Pub Date : 2024-05-14 DOI: 10.1093/rasti/rzae018

Billy Shrive, Pollacco Don, Paul Chote, James A. Blake, B. Cooke, James McCormac, R. West, Robert Airey, Alex MacManus, Phineas Allen

{"title":"Classifying LEO satellite platforms with boosted decision trees","authors":"Billy Shrive, Pollacco Don, Paul Chote, James A. Blake, B. Cooke, James McCormac, R. West, Robert Airey, Alex MacManus, Phineas Allen","doi":"10.1093/rasti/rzae018","DOIUrl":"https://doi.org/10.1093/rasti/rzae018","url":null,"abstract":"\u0000 As the cost of reaching LEO has diminished we expect, over the next decade, an almost exponential increase in the numbers of LEO spacecraft from established and potentially new agents. Remote characterisation of these and the increasing numbers of decommissioned/debris satellites is thus becoming more important, along with identifying unannounced changes in megaconstellations. In this paper we examine the light curves of known LEO platforms with a boosted tree algorithm in order to determine whether spacecraft properties were discernible. A-priori we expected little correlation as we expected the large variations in sight-line geometries would mask signs from the spacecraft. Using large numbers of lightcurves from the MMT-9 database, we find that this is not the case and most platforms are statistically identifiable in most sight-lines and tentatively associate this correlation with the differences and similarities between downward facing instruments. Pairs of satellite platforms can be distinguished 86.13 per cent (N = 15 600) of the time using this method. Evolutionary changes to the Starlink satellite platform are also distinguished.","PeriodicalId":367327,"journal":{"name":"RAS Techniques and Instruments","volume":"16 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140981550","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}

引用次数: 0

PyExoCross: a Python program for generating spectra and cross sections from molecular line lists PyExoCross：从分子线列表生成光谱和横截面的 Python 程序

RAS Techniques and Instruments Pub Date : 2024-04-24 DOI: 10.1093/rasti/rzae016

Jingxin Zhang, J. Tennyson, S. Yurchenko

引用次数: 1

The verification of periodicity with the use of recurrent neural networks 利用递归神经网络验证周期性

RAS Techniques and Instruments Pub Date : 2024-04-23 DOI: 10.1093/rasti/rzae015

N. Miller, P. W. Lucas, Y. Sun, Z. Guo, W. J. Cooper, C. Morris

引用次数: 0

REPUBLIC: A variability-preserving systematic-correction algorithm for PLATO’s multi-camera light curves REPUBLIC: PLATO 多相机光曲线的可变性保护系统校正算法

RAS Techniques and Instruments Pub Date : 2024-04-09 DOI: 10.1093/rasti/rzae014

Oscar Barrag'an, S. Aigrain, J. McCormac

{"title":"REPUBLIC: A variability-preserving systematic-correction algorithm for PLATO’s multi-camera light curves","authors":"Oscar Barrag'an, S. Aigrain, J. McCormac","doi":"10.1093/rasti/rzae014","DOIUrl":"https://doi.org/10.1093/rasti/rzae014","url":null,"abstract":"\u0000 Space-based photometry missions produce exquisite light curves that contain a wealth of stellar variability on a wide range of timescales. Light curves also typically contain significant instrumental systematics – spurious, non-astrophysical trends that are common, in varying degrees, to many light curves. Empirical systematics-correction approaches using the information in the light curves themselves have been very successful, but tend to suppress astrophysical signals, particularly on longer timescales. Unlike its predecessors, the PLATO mission will use multiple cameras to monitor the same stars. We present REPUBLIC, a novel systematics-correction algorithm which exploits this multi-camera configuration to correct systematics that differ between cameras, while preserving the component of each star’s signal that is common to all cameras, regardless of timescale. Through simulations with astrophysical signals (star spots and planetary transits), Kepler-like errors, and white noise, we demonstrate REPUBLIC’s ability to preserve long-term astrophysical signals usually lost in standard correction techniques. We also explore REPUBLIC’s performance with different number of cameras and systematic properties. We conclude that REPUBLIC should be considered a potential complement to existing strategies for systematic correction in multi-camera surveys, with its utility contingent upon further validation and adaptation to the specific characteristics of the PLATO mission data.","PeriodicalId":367327,"journal":{"name":"RAS Techniques and Instruments","volume":"91 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140726110","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}

引用次数: 0

A simple spacecraft – vector intersection methodology and applications 简单的航天器--矢量交叉方法和应用

RAS Techniques and Instruments Pub Date : 2024-03-23 DOI: 10.1093/rasti/rzae012

Georgios Xystouris, Oleg Shebanits, C. Arridge

引用次数: 0

Leveraging open science machine learning challenges for data constrained planetary mission instruments 利用开放科学机器学习挑战数据受限的行星任务仪器

RAS Techniques and Instruments Pub Date : 2024-03-15 DOI: 10.1093/rasti/rzae009

Victoria Da Poian, E. I. Lyness, J. Y. Qi, I. Shah, G. Lipstein, P. D. Archer, L. Chou, C. Freissinet, C. Malespin, A. McAdam, C. A. Knudson, B. P. Theiling, S. M. H”orst

{"title":"Leveraging open science machine learning challenges for data constrained planetary mission instruments","authors":"Victoria Da Poian, E. I. Lyness, J. Y. Qi, I. Shah, G. Lipstein, P. D. Archer, L. Chou, C. Freissinet, C. Malespin, A. McAdam, C. A. Knudson, B. P. Theiling, S. M. H”orst","doi":"10.1093/rasti/rzae009","DOIUrl":"https://doi.org/10.1093/rasti/rzae009","url":null,"abstract":"\u0000 We set up two open-science machine learning (ML) challenges focusing on building models to automatically analyze mass spectrometry (MS) data for Mars exploration. ML challenges provide an excellent way to engage a diverse set of experts with benchmark training data, explore a wide range of ML and data science approaches, and identify promising models based on empirical results, as well as to get independent external analyses to compare to those of the internal team. These two challenges were proof-of-concept projects to analyze the feasibility of combining data collected from different instruments in a single ML application. We selected mass spectrometry data from 1) commercial instruments and 2) the Sample Analysis at Mars (SAM, an instrument suite that includes a mass spectrometer subsystem onboard the Curiosity rover) testbed. These challenges, organized with DrivenData, gathered more than 1,150 unique participants from all over the world, and obtained more than 600 solutions contributing powerful models to the analysis of rock and soil samples relevant to planetary science using various mass spectrometry datasets. These two challenges demonstrated the suitability and value of multiple ML approaches to classifying planetary analog datasets from both commercial and flight-like instruments.\u0000 We present the processes from the problem identification, challenge setups, and challenge results that gathered creative and diverse solutions from worldwide participants, in some cases with no backgrounds in mass spectrometry. We also present the potential and limitations of these solutions for ML application in future planetary missions. Our longer-term goal is to deploy these powerful methods onboard the spacecraft to autonomously guide space operations and reduce ground-in-the-loop reliance.","PeriodicalId":367327,"journal":{"name":"RAS Techniques and Instruments","volume":"62 S1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140238226","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}

引用次数: 0

A forward-modelling approach to overcome PSF smearing and fit flexible models to the chemical structure of galaxies 克服 PSF 遮挡并根据星系化学结构拟合灵活模型的前向建模方法

RAS Techniques and Instruments Pub Date : 2024-03-13 DOI: 10.1093/rasti/rzae010

Benjamin Metha, S. Birrer, T. Treu, M. Trenti, Xuheng Ding, Xin Wang

{"title":"A forward-modelling approach to overcome PSF smearing and fit flexible models to the chemical structure of galaxies","authors":"Benjamin Metha, S. Birrer, T. Treu, M. Trenti, Xuheng Ding, Xin Wang","doi":"10.1093/rasti/rzae010","DOIUrl":"https://doi.org/10.1093/rasti/rzae010","url":null,"abstract":"\u0000 https://github.com/astrobenji/lenstronomy-metals-notebooks Historically, metallicity profiles of galaxies have been modelled using a radially symmetric, two-parameter linear model, which reveals that most galaxies are more metal-rich in their central regions than their outskirts. However, this model is known to yield inaccurate results when the point-spread function (PSF) of a telescope is large. Furthermore, a radially symmetric model cannot capture asymmetric structures within a galaxy. In this work, we present an extension of the popular forward-modelling python package lenstronomy, which allows the user to overcome both of these obstacles. We demonstrate the new features of this code base through two illustrative examples on simulated data. First, we show that through forward modelling, lenstronomy is able to recover accurately the metallicity gradients of galaxies, even when the PSF is comparable to the size of a galaxy, as long as the data is observed with a sufficient number of pixels. Additionally, we demonstrate how lenstronomy is able to fit irregular metallicity profiles to galaxies that are not well-described by a simple surface brightness profile. This opens up pathways for detailed investigations into the connections between morphology and chemical structure for galaxies at cosmological distances using the transformative capabilities of JWST. Our code is publicly available and open source, and can also be used to model spatial distributions of other galaxy properties that are traced by its surface brightness profile","PeriodicalId":367327,"journal":{"name":"RAS Techniques and Instruments","volume":"15 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140245873","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}

引用次数: 0

PySSED: An automated method of collating and fitting stellar spectral energy distributions PySSED：整理和拟合恒星光谱能量分布的自动方法

RAS Techniques and Instruments Pub Date : 2024-02-19 DOI: 10.1093/rasti/rzae005

I. McDonald, Albert Zijlstra, Nick L. J. Cox, Emma L. Alexander, Alexander Csukai, Ria Ramkumar, Alexander Hollings

引用次数: 1

A promising method for breaking the logjam of time-frequency analysis in astronomy 打破天文学时频分析困境的可行方法

RAS Techniques and Instruments Pub Date : 2024-01-25 DOI: 10.1093/rasti/rzae001

Shu-Ping Yan, Li Ji, Ping Zhang, Siming Liu, Lei Lu, Min Long

{"title":"A promising method for breaking the logjam of time-frequency analysis in astronomy","authors":"Shu-Ping Yan, Li Ji, Ping Zhang, Siming Liu, Lei Lu, Min Long","doi":"10.1093/rasti/rzae001","DOIUrl":"https://doi.org/10.1093/rasti/rzae001","url":null,"abstract":"\u0000 Time-frequency analysis could provide detailed dynamic information of celestial bodies and is critical for comprehension of astronomical phenomena. However, it is far from being well-developed in astronomy. Hilbert-Huang transform (HHT) is an advanced time-frequency method but has two problems in analyzing astronomical signals. One is that many astronomical signals may be composed of multiple components with various amplitudes and frequencies, while HHT uses assisted noises with the same amplitude to extract all components. The other is that HHT is an empirical method requiring tunable parameters to be optimized using experimental results or known facts, which are challenging to obtain in astronomy and it is therefore hard to determine whether the signal decomposition is right or not. In this study, we adjust the noise amplitude to optimize the decomposition based on the orthogonality of the obtained components and discard the decompositions with non-physical results. Three experiments show that this new extension of HHT is an effective method suitable for high-resolution time-frequency analysis in astronomy. It can be used to dig out valuable information which are inaccessible with other methods, and thus has the potential to open up new avenues for astronomy research.","PeriodicalId":367327,"journal":{"name":"RAS Techniques and Instruments","volume":"40 22","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139598023","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}

引用次数: 0

Correction to: Personalized anomaly detection using deep active learning 更正为利用深度主动学习进行个性化异常检测

RAS Techniques and Instruments Pub Date : 2024-01-01 DOI: 10.1093/rasti/rzae008

引用次数: 0