Pub Date : 2024-06-23DOI: 10.3847/1538-4365/ad452e
Przemek Mróz, Andrzej Udalski, Michał K. Szymański, Mateusz Kapusta, Igor Soszyński, Łukasz Wyrzykowski, Paweł Pietrukowicz, Szymon Kozłowski, Radosław Poleski, Jan Skowron, Dorota Skowron, Krzysztof Ulaczyk, Mariusz Gromadzki, Krzysztof Rybicki, Patryk Iwanek, Marcin Wrona and Milena Ratajczak
Measurements of the microlensing optical depth and event rate toward the Large Magellanic Cloud (LMC) can be used to probe the distribution and mass function of compact objects in the direction toward that galaxy—in the Milky Way disk, the Milky Way dark matter halo, and the LMC itself. The previous measurements, based on small statistical samples of events, found that the optical depth is an order of magnitude smaller than that expected from the entire dark matter halo in the form of compact objects. However, these previous studies were not sensitive to long-duration events with Einstein timescales longer than 2.5–3 yr, which are expected from massive (10–100 M⊙) and intermediate-mass (102–105M⊙) black holes. Such events would have been missed by the previous studies and would not have been taken into account in calculations of the optical depth. Here, we present the analysis of nearly 20 yr long photometric monitoring of 78.7 million stars in the LMC by the Optical Gravitational Lensing Experiment (OGLE) from 2001 through 2020. We describe the observing setup, the construction of the 20 yr OGLE data set, the methods used for searching for microlensing events in the light-curve data, and the calculation of the event detection efficiency. In total, we find 16 microlensing events (13 using an automated pipeline and three with manual searches), all of which have timescales shorter than 1 yr. We use a sample of 13 events to measure the microlensing optical depth toward the LMC τ = (0.121 ± 0.037) × 10−7 and the event rate Γ = (0.74 ± 0.25) × 10−7 yr−1 star−1. These numbers are consistent with lensing by stars in the Milky Way disk and the LMC itself, and they demonstrate that massive and intermediate-mass black holes cannot comprise a significant fraction of the dark matter.
{"title":"Microlensing Optical Depth and Event Rate toward the Large Magellanic Cloud Based on 20 yr of OGLE Observations","authors":"Przemek Mróz, Andrzej Udalski, Michał K. Szymański, Mateusz Kapusta, Igor Soszyński, Łukasz Wyrzykowski, Paweł Pietrukowicz, Szymon Kozłowski, Radosław Poleski, Jan Skowron, Dorota Skowron, Krzysztof Ulaczyk, Mariusz Gromadzki, Krzysztof Rybicki, Patryk Iwanek, Marcin Wrona and Milena Ratajczak","doi":"10.3847/1538-4365/ad452e","DOIUrl":"https://doi.org/10.3847/1538-4365/ad452e","url":null,"abstract":"Measurements of the microlensing optical depth and event rate toward the Large Magellanic Cloud (LMC) can be used to probe the distribution and mass function of compact objects in the direction toward that galaxy—in the Milky Way disk, the Milky Way dark matter halo, and the LMC itself. The previous measurements, based on small statistical samples of events, found that the optical depth is an order of magnitude smaller than that expected from the entire dark matter halo in the form of compact objects. However, these previous studies were not sensitive to long-duration events with Einstein timescales longer than 2.5–3 yr, which are expected from massive (10–100 M⊙) and intermediate-mass (102–105M⊙) black holes. Such events would have been missed by the previous studies and would not have been taken into account in calculations of the optical depth. Here, we present the analysis of nearly 20 yr long photometric monitoring of 78.7 million stars in the LMC by the Optical Gravitational Lensing Experiment (OGLE) from 2001 through 2020. We describe the observing setup, the construction of the 20 yr OGLE data set, the methods used for searching for microlensing events in the light-curve data, and the calculation of the event detection efficiency. In total, we find 16 microlensing events (13 using an automated pipeline and three with manual searches), all of which have timescales shorter than 1 yr. We use a sample of 13 events to measure the microlensing optical depth toward the LMC τ = (0.121 ± 0.037) × 10−7 and the event rate Γ = (0.74 ± 0.25) × 10−7 yr−1 star−1. These numbers are consistent with lensing by stars in the Milky Way disk and the LMC itself, and they demonstrate that massive and intermediate-mass black holes cannot comprise a significant fraction of the dark matter.","PeriodicalId":22368,"journal":{"name":"The Astrophysical Journal Supplement Series","volume":"8 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141504885","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}
Pub Date : 2024-06-23DOI: 10.3847/1538-4365/ad435c
Leisa K. Townsley, Patrick S. Broos and Matthew S. Povich
The Tarantula Nebula (30 Doradus, 30 Dor) is the most important star-forming complex in the Local Group, offering a microscope on starburst astrophysics. At its heart lies the exceptionally rich young stellar cluster R136, containing the most massive stars known. Stellar winds and supernovae have carved 30 Dor into an amazing display of arcs, pillars, and bubbles. We present first results and advanced data-processing products from the 2 Ms Chandra X-ray Visionary Project, “The Tarantula—Revealed by X-rays” (T-ReX). The 3615 point sources in the T-ReX catalog include massive stars, compact objects, binaries, bright pre-main-sequence stars, and compact young stellar (sub)clusters in 30 Dor. After removing point sources and excluding the exceptionally bright supernova remnant N157B (30 Dor B), the global diffuse X-ray maps reveal hot plasma structures resolved at 1–10 pc scales, with an absorption-corrected total-band (0.5–7 keV) X-ray luminosity of 2.110 × 1037 erg s−1. Spatially resolved spectral modeling provides evidence for emission lines enhanced by charge-exchange processes at the interfaces. We identify a candidate for the oldest X-ray pulsar detected to date in 30 Dor, PSR J0538-6902, inside a newly resolved arcuate X-ray wind nebula, the Manta Ray. The long temporal baseline of T-ReX allowed monitoring of dozens of massive stars, several showing periodic variability tied to binary orbital periods, and captured strong flares from at least three low-mass Galactic foreground stars.
蜘蛛星云(30 Doradus,30 Dor)是本星系群中最重要的恒星形成复合体,是研究星爆天体物理学的显微镜。它的中心是异常丰富的年轻恒星群R136,其中包含了已知质量最大的恒星。恒星风和超新星将 30 Dor 星雕刻成令人惊叹的弧形、柱形和气泡。我们将介绍钱德拉 X 射线愿景项目 "X 射线揭示的狼蛛(T-Revealed by X-rays,T-ReX)"的首批成果和高级数据处理产品。T-ReX 星表中的 3615 个点源包括大质量恒星、紧凑天体、双星、明亮的前主序恒星和 30 Dor 中紧凑的年轻恒星(子)星团。剔除点源和异常明亮的超新星残余物 N157B(30 Dor B)后,全球漫射 X 射线图显示了 1-10 pc 尺度的热等离子体结构,吸收校正后的总波段(0.5-7 keV)X 射线光度为 2.110 × 1037 erg s-1。空间分辨光谱建模为界面上电荷交换过程增强的发射线提供了证据。我们在一个新解析的弧状X射线风星云--Manta Ray--内发现了一颗迄今为止在30 Dor探测到的最古老的X射线脉冲星的候选者--PSR J0538-6902。T-ReX 的长时间基线允许对数十颗大质量恒星进行监测,其中几颗恒星显示出与双星轨道周期相关的周期性变化,并捕捉到至少三颗低质量银河系前景恒星的强烈耀斑。
{"title":"T-ReX: The Tarantula—Revealed by X-Rays","authors":"Leisa K. Townsley, Patrick S. Broos and Matthew S. Povich","doi":"10.3847/1538-4365/ad435c","DOIUrl":"https://doi.org/10.3847/1538-4365/ad435c","url":null,"abstract":"The Tarantula Nebula (30 Doradus, 30 Dor) is the most important star-forming complex in the Local Group, offering a microscope on starburst astrophysics. At its heart lies the exceptionally rich young stellar cluster R136, containing the most massive stars known. Stellar winds and supernovae have carved 30 Dor into an amazing display of arcs, pillars, and bubbles. We present first results and advanced data-processing products from the 2 Ms Chandra X-ray Visionary Project, “The Tarantula—Revealed by X-rays” (T-ReX). The 3615 point sources in the T-ReX catalog include massive stars, compact objects, binaries, bright pre-main-sequence stars, and compact young stellar (sub)clusters in 30 Dor. After removing point sources and excluding the exceptionally bright supernova remnant N157B (30 Dor B), the global diffuse X-ray maps reveal hot plasma structures resolved at 1–10 pc scales, with an absorption-corrected total-band (0.5–7 keV) X-ray luminosity of 2.110 × 1037 erg s−1. Spatially resolved spectral modeling provides evidence for emission lines enhanced by charge-exchange processes at the interfaces. We identify a candidate for the oldest X-ray pulsar detected to date in 30 Dor, PSR J0538-6902, inside a newly resolved arcuate X-ray wind nebula, the Manta Ray. The long temporal baseline of T-ReX allowed monitoring of dozens of massive stars, several showing periodic variability tied to binary orbital periods, and captured strong flares from at least three low-mass Galactic foreground stars.","PeriodicalId":22368,"journal":{"name":"The Astrophysical Journal Supplement Series","volume":"26 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141527981","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}
Pub Date : 2024-06-19DOI: 10.3847/1538-4365/ad46f5
Amir Aghabiglou, Chung San Chu, Arwa Dabbech and Yves Wiaux
Radio-interferometric imaging entails solving high-resolution high-dynamic-range inverse problems from large data volumes. Recent image reconstruction techniques grounded in optimization theory have demonstrated remarkable capability for imaging precision, well beyond CLEAN’s capability. These range from advanced proximal algorithms propelled by handcrafted regularization operators, such as the SARA family, to hybrid plug-and-play (PnP) algorithms propelled by learned regularization denoisers, such as AIRI. Optimization and PnP structures are however highly iterative, which hinders their ability to handle the extreme data sizes expected from future instruments. To address this scalability challenge, we introduce a novel deep-learning approach, dubbed “Residual-to-Residual DNN series for high-Dynamic-range imaging” or in short R2D2. R2D2's reconstruction is formed as a series of residual images, iteratively estimated as outputs of deep neural networks (DNNs) taking the previous iteration’s image estimate and associated data residual as inputs. It thus takes a hybrid structure between a PnP algorithm and a learned version of the matching pursuit algorithm that underpins CLEAN. We present a comprehensive study of our approach, featuring its multiple incarnations distinguished by their DNN architectures. We provide a detailed description of its training process, targeting a telescope-specific approach. R2D2's capability to deliver high precision is demonstrated in simulation, across a variety of image and observation settings using the Very Large Array. Its reconstruction speed is also demonstrated: with only a few iterations required to clean data residuals at dynamic ranges up to 105, R2D2 opens the door to fast precision imaging. R2D2 codes are available in the BASPLib (https://basp-group.github.io/BASPLib/) library on GitHub.
{"title":"The R2D2 Deep Neural Network Series Paradigm for Fast Precision Imaging in Radio Astronomy","authors":"Amir Aghabiglou, Chung San Chu, Arwa Dabbech and Yves Wiaux","doi":"10.3847/1538-4365/ad46f5","DOIUrl":"https://doi.org/10.3847/1538-4365/ad46f5","url":null,"abstract":"Radio-interferometric imaging entails solving high-resolution high-dynamic-range inverse problems from large data volumes. Recent image reconstruction techniques grounded in optimization theory have demonstrated remarkable capability for imaging precision, well beyond CLEAN’s capability. These range from advanced proximal algorithms propelled by handcrafted regularization operators, such as the SARA family, to hybrid plug-and-play (PnP) algorithms propelled by learned regularization denoisers, such as AIRI. Optimization and PnP structures are however highly iterative, which hinders their ability to handle the extreme data sizes expected from future instruments. To address this scalability challenge, we introduce a novel deep-learning approach, dubbed “Residual-to-Residual DNN series for high-Dynamic-range imaging” or in short R2D2. R2D2's reconstruction is formed as a series of residual images, iteratively estimated as outputs of deep neural networks (DNNs) taking the previous iteration’s image estimate and associated data residual as inputs. It thus takes a hybrid structure between a PnP algorithm and a learned version of the matching pursuit algorithm that underpins CLEAN. We present a comprehensive study of our approach, featuring its multiple incarnations distinguished by their DNN architectures. We provide a detailed description of its training process, targeting a telescope-specific approach. R2D2's capability to deliver high precision is demonstrated in simulation, across a variety of image and observation settings using the Very Large Array. Its reconstruction speed is also demonstrated: with only a few iterations required to clean data residuals at dynamic ranges up to 105, R2D2 opens the door to fast precision imaging. R2D2 codes are available in the BASPLib (https://basp-group.github.io/BASPLib/) library on GitHub.","PeriodicalId":22368,"journal":{"name":"The Astrophysical Journal Supplement Series","volume":"3 8 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141529049","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 study of the molecular gas in early-mid stage major mergers, with a sample of 43 major-merger galaxy pairs selected from the Mapping Nearby Galaxies at Apache Point Observatory survey and a control sample of 195 isolated galaxies selected from the xCOLD GASS survey. Adopting kinematic asymmetry as a new effective indicator to describe the merger stage, we aim to study the role of molecular gas in the merger-induced star formation enhancement along the merger sequence of galaxy pairs. We obtain the molecular gas properties from CO observations with the James Clerk Maxwell Telescope, Institut de Radioastronomie Milimetrique 30 m telescope, and the MaNGA-ARO Survey of CO Targets survey. Using these data, we investigate the differences in molecular gas fraction ( ), star formation rate (SFR), star formation efficiency (SFE), molecular-to-atomic gas ratio ( /MH i), total gas fraction (fgas), and the SFE of total gas (SFEgas) between the pair and control samples. In the full pair sample, our results suggest the of paired galaxies is significantly enhanced, while the SFE is comparable to that of isolated galaxies. We detect significantly increased and /MH i in paired galaxies at the pericenter stage, indicating an accelerated transition from atomic gas to molecular gas due to interactions. Our results indicate that the elevation of plays a major role in the enhancement of global SFR in paired galaxies at the pericenter stage, while the contribution of enhanced SFE in specific regions requires further explorations through spatially resolved observations of a larger sample spanning a wide range of merger stages.
我们从阿帕奇角天文台(Apache Point Observatory)的近邻星系测绘调查(Mapping Nearby Galaxies at Apache Point Observatory survey)中选取了43对大合并星系,并从xCOLD GASS调查中选取了195个孤立星系作为对照样本,对早中期大合并星系中的分子气体进行了研究。采用运动不对称作为描述合并阶段的新的有效指标,我们的目的是研究分子气体在星系对合并序列中合并引起的恒星形成增强中的作用。我们通过詹姆斯-克拉克-麦克斯韦望远镜、Institut de Radioastronomie Milimetrique 30米望远镜和MaNGA-ARO CO目标巡天观测获得了分子气体的性质。利用这些数据,我们研究了分子气体分数( )、恒星形成率(SFR)、恒星形成效率(SFE)、分子与原子气体比( /MH i)、总气体分数(fgas)以及总气体的 SFE(SFEgas)在成对样本和对照样本之间的差异。在全配对样本中,我们的结果表明配对星系的化学气体与原子气体之比(/MH i)显著提高,而总气体的SFE则与孤立星系相当。我们探测到配对星系在近心阶段的/MH i和/MH i明显增加,这表明由于相互作用,原子气体向分子气体的过渡加速了。我们的研究结果表明,对偶星系在围中心阶段的全局 SFR 的增强主要是由于 /MH i 的升高,而特定区域 SFE 的增强则需要通过对跨越各种合并阶段的更大样本进行空间分辨观测来进一步探索。
{"title":"CO Observations of Early-mid Stage Major Mergers in the MaNGA Survey","authors":"Qingzheng Yu, 清正 余, Taotao Fang, 陶陶 方, Cong Kevin Xu, 聪 徐, Shuai Feng, 帅 冯, Siyi Feng, 思轶 冯, Yu Gao, 煜 高, Xue-Jian Jiang, 雪健 蒋 and Ute Lisenfeld","doi":"10.3847/1538-4365/ad4547","DOIUrl":"https://doi.org/10.3847/1538-4365/ad4547","url":null,"abstract":"We present a study of the molecular gas in early-mid stage major mergers, with a sample of 43 major-merger galaxy pairs selected from the Mapping Nearby Galaxies at Apache Point Observatory survey and a control sample of 195 isolated galaxies selected from the xCOLD GASS survey. Adopting kinematic asymmetry as a new effective indicator to describe the merger stage, we aim to study the role of molecular gas in the merger-induced star formation enhancement along the merger sequence of galaxy pairs. We obtain the molecular gas properties from CO observations with the James Clerk Maxwell Telescope, Institut de Radioastronomie Milimetrique 30 m telescope, and the MaNGA-ARO Survey of CO Targets survey. Using these data, we investigate the differences in molecular gas fraction ( ), star formation rate (SFR), star formation efficiency (SFE), molecular-to-atomic gas ratio ( /MH i), total gas fraction (fgas), and the SFE of total gas (SFEgas) between the pair and control samples. In the full pair sample, our results suggest the of paired galaxies is significantly enhanced, while the SFE is comparable to that of isolated galaxies. We detect significantly increased and /MH i in paired galaxies at the pericenter stage, indicating an accelerated transition from atomic gas to molecular gas due to interactions. Our results indicate that the elevation of plays a major role in the enhancement of global SFR in paired galaxies at the pericenter stage, while the contribution of enhanced SFE in specific regions requires further explorations through spatially resolved observations of a larger sample spanning a wide range of merger stages.","PeriodicalId":22368,"journal":{"name":"The Astrophysical Journal Supplement Series","volume":"20 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141527982","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}
Pub Date : 2024-06-16DOI: 10.3847/1538-4365/ad4642
Zahra Tajik, Nastaran Farhang, Hossein Safari and Michael S. Wheatland
Solar and stellar magnetic patches (i.e., magnetic fluxes that reach the surface from the interior) are believed to be the primary sources of a star’s atmospheric conditions. Here, we apply the complex network approach and investigate its efficacy in the identification of these features. For this purpose, we use the line-of-sight magnetograms provided by the Helioseismic and Magnetic Imager on board the Solar Dynamics Observatory. We construct the magnetic network following a specific visibility graph condition between pairs of pixels with opposite polarities and search for possible links between these regions. The complex network facilitates the construction of node degrees and PageRank images, and applying the downhill algorithm to node-degree images allows for the grouping of pixels into features corresponding to one-to-one matches with magnetogram patches. This approach promisingly serves to identify the nontrivial morphological structure of the magnetic patches for small and large sizes. We observe that the changes in the features of the node-degree images effectively correspond to the cospatial magnetic patches over time. Through visual assessment, we estimate an average false-negative error rate of approximately 1% in identifying small-scale features (one or two pixels in size).
太阳和恒星磁斑(即从内部到达表面的磁通量)被认为是恒星大气状况的主要来源。在此,我们应用复杂网络方法,研究其在识别这些特征方面的功效。为此,我们使用了太阳动力学天文台(Solar Dynamics Observatory)上的太阳地震和磁成像仪(Helioseismic and Magnetic Imager)提供的视线磁图。我们根据极性相反的像素对之间的特定可见度图条件构建磁网络,并搜索这些区域之间可能存在的联系。复杂的网络有助于构建节点度和 PageRank 图像,将下坡算法应用于节点度图像可将像素分组为与磁图斑块一一对应的特征。这种方法有望识别大小磁图斑块的非复杂形态结构。我们观察到,随着时间的推移,节点度图像特征的变化与空间磁斑块有效对应。通过目测评估,我们估计识别小尺度特征(一或两个像素大小)的平均误差率约为 1%。
{"title":"Complex Network View of the Sun’s Magnetic Patches. I. Identification","authors":"Zahra Tajik, Nastaran Farhang, Hossein Safari and Michael S. Wheatland","doi":"10.3847/1538-4365/ad4642","DOIUrl":"https://doi.org/10.3847/1538-4365/ad4642","url":null,"abstract":"Solar and stellar magnetic patches (i.e., magnetic fluxes that reach the surface from the interior) are believed to be the primary sources of a star’s atmospheric conditions. Here, we apply the complex network approach and investigate its efficacy in the identification of these features. For this purpose, we use the line-of-sight magnetograms provided by the Helioseismic and Magnetic Imager on board the Solar Dynamics Observatory. We construct the magnetic network following a specific visibility graph condition between pairs of pixels with opposite polarities and search for possible links between these regions. The complex network facilitates the construction of node degrees and PageRank images, and applying the downhill algorithm to node-degree images allows for the grouping of pixels into features corresponding to one-to-one matches with magnetogram patches. This approach promisingly serves to identify the nontrivial morphological structure of the magnetic patches for small and large sizes. We observe that the changes in the features of the node-degree images effectively correspond to the cospatial magnetic patches over time. Through visual assessment, we estimate an average false-negative error rate of approximately 1% in identifying small-scale features (one or two pixels in size).","PeriodicalId":22368,"journal":{"name":"The Astrophysical Journal Supplement Series","volume":"42 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141527885","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}
Pub Date : 2024-06-11DOI: 10.3847/1538-4365/ad434f
Jie Song, GuanWen Fang, Shuo Ba, Zesen Lin, Yizhou Gu, Chichun Zhou, Tao Wang, Cai-Na Hao, Guilin Liu, Hongxin Zhang, Yao Yao, Xu Kong
Morphological classification conveys abundant information on the formation, evolution, and environment of galaxies. In this work, we refine a two-step galaxy morphological classification framework (USmorph), which employs a combination of unsupervised machine-learning and supervised machine-learning techniques, along with a self-consistent and robust data-preprocessing step. The updated method is applied to galaxies with I