We measure the high-mass stellar initial mass function (IMF) from resolved stars in M33 young stellar clusters. Leveraging \textit{Hubble Space Telescope's} high resolving power, we fully model the ...IMF probabilistically. We first model the optical CMD of each cluster to constrain its power-law slope \(\Gamma\), marginalized over other cluster parameters in the fit (e.g., cluster age, mass, and radius). We then probabilistically model the distribution of MF slopes for a highly strict cluster sample of 9 clusters more massive than log(Mass/M\(_{\odot}\))=3.6; above this mass, all clusters have well-populated main sequences of massive stars and should have accurate recovery of their MF slopes, based on extensive tests with artificial clusters. We find the ensemble IMF is best described by a mean high-mass slope of \(\overline{\Gamma} = 1.49\pm0.18\), with an intrinsic scatter of \(\sigma^{2}_{\Gamma} = 0.02^{+0.16}_{0.00}\), consistent with a universal IMF. We find no dependence of the IMF on environmental impacts such as the local star formation rate or galactocentric radius within M33, which serves as a proxy for metallicity. This \(\overline{\Gamma}\) measurement is consistent with similar measurements in M31, despite M33 having a much higher star formation rate intensity. While this measurement is formally consistent with the canonical Kroupa (\(\Gamma = 1.30\)) IMF, as well as the Salpeter (\(\Gamma = 1.35)\)) value, it is the second Local Group cluster sample to show evidence for a somewhat steeper high-mass IMF slope. We explore the impacts a steeper IMF slope has on a number of astronomical sub-fields.
We present analysis using a citizen science campaign to improve the cosmological measures from the Hobby-Eberly Telescope Dark Energy Experiment (HETDEX). The goal of HETDEX is to measure the Hubble ...expansion rate, \(H(z)\), and angular diameter distance, \(D_A(z)\), at \(z =\) 2.4, each to percent-level accuracy. This accuracy is determined primarily from the total number of detected Lyman-\(\alpha\) emitters (LAEs), the false positive rate due to noise, and the contamination due to O II emitting galaxies. This paper presents the citizen science project, Dark Energy Explorers, with the goal of increasing the number of LAEs, decreasing the number of false positives due to noise and the O II galaxies. Initial analysis shows that citizen science is an efficient and effective tool for classification most accurately done by the human eye, especially in combination with unsupervised machine learning. Three aspects from the citizen science campaign that have the most impact are 1) identifying individual problems with detections, 2) providing a clean sample with 100% visual identification above a signal-to-noise cut, and 3) providing labels for machine learning efforts. Since the end of 2022, Dark Energy Explorers has collected over three and a half million classifications by 11,000 volunteers in over 85 different countries around the world. By incorporating the results of the Dark Energy Explorers we expect to improve the accuracy on the \(D_A(z)\) and \(H(z)\) parameters at \(z =\) 2.4 by 10 - 30%. While the primary goal is to improve on HETDEX, Dark Energy Explorers has already proven to be a uniquely powerful tool for science advancement and increasing accessibility to science worldwide.
We use young clusters and giant molecular clouds (GMCs) in the galaxies M33 and M31 to constrain temporal and spatial scales in the star formation process. In M33, we compare the PHATTER catalogue of ...1214 clusters with ages measured via colour-magnitude diagram (CMD) fitting to 444 GMCs identified from a new 35 pc resolution ALMA \(^{12}\)CO(2-1) survey. In M31, we compare the PHAT catalogue of 1249 clusters to 251 GMCs measured from a CARMA \(^{12}\)CO(1-0) survey with 20 pc resolution. Through two-point correlation analysis, we find that young clusters have a high probability of being near other young clusters, but correlation between GMCs is suppressed by the cloud identification algorithm. By comparing the positions, we find that younger clusters are closer to GMCs than older clusters. Through cross-correlation analysis of the M33 cluster data, we find that clusters are statistically associated when they are \(\leq\)10 Myr old. Utilizing the high precision ages of the clusters, we find that clusters older than \(\approx 18\) Myr are uncorrelated with the molecular ISM. Using the spatial coincidence of the youngest clusters and GMCs in M33, we estimate that clusters spend \(\approx\)4-6 Myr inside their parent GMC. Through similar analysis, we find that the GMCs in M33 have a total lifetime of \(\approx 11\)-15 Myr. We also develop a drift model and show that the above correlations can be explained if the clusters in M33 have a 5-10 km s\(^{-1}\) velocity dispersion relative to the molecular ISM.
The Gravity Spy project aims to uncover the origins of glitches, transient bursts of noise that hamper analysis of gravitational-wave data. By using both the work of citizen-science volunteers and ...machine-learning algorithms, the Gravity Spy project enables reliable classification of glitches. Citizen science and machine learning are intrinsically coupled within the Gravity Spy framework, with machine-learning classifications providing a rapid first-pass classification of the dataset and enabling tiered volunteer training, and volunteer-based classifications verifying the machine classifications, bolstering the machine-learning training set and identifying new morphological classes of glitches. These classifications are now routinely used in studies characterizing the performance of the LIGO gravitational-wave detectors. Providing the volunteers with a training framework that teaches them to classify a wide range of glitches, as well as additional tools to aid their investigations of interesting glitches, empowers them to make discoveries of new classes of glitches. This demonstrates that, when giving suitable support, volunteers can go beyond simple classification tasks to identify new features in data at a level comparable to domain experts. The Gravity Spy project is now providing volunteers with more complicated data that includes auxiliary monitors of the detector to identify the root cause of glitches.
Young stellar objects (YSOs) are the gold standard for tracing star formation in galaxies but have been unobservable beyond the Milky Way and Magellanic Clouds. But that all changed when the James ...Webb Space Telescope was launched, which we use to identify YSOs in the Local Group galaxy M33, marking the first time that individual YSOs have been identified at these large distances. We present MIRI imaging mosaics at 5.6 and 21 microns that cover a significant portion of one of M33's spiral arms that has existing panchromatic imaging from the Hubble Space Telescope and deep ALMA CO measurements. Using these MIRI and Hubble Space Telescope images, we identify point sources using the new DOLPHOT MIRI module. We identify 793 candidate YSOs from cuts based on colour, proximity to giant molecular clouds (GMCs), and visual inspection. Similar to Milky Way GMCs, we find that higher mass GMCs contain more YSOs and YSO emission, which further shows YSOs identify star formation better than most tracers that cannot capture this relationship at cloud scales. We find evidence of enhanced star formation efficiency in the southern spiral arm by comparing the YSOs to the molecular gas mass.
Objective and background: The course of asthma may be altered during pregnancy with at least one‐third of women experiencing a worsening of asthma and 20% having an exacerbation during pregnancy. ...This study used the novel proteomic technique, surface‐enhanced laser desorption ionization‐time of flight mass spectrometry to determine if the presence of asthma during pregnancy was associated with alterations in plasma proteins.
Methods: Plasma collected from healthy (n = 23) and asthmatic (n = 27) pregnant women at 18 and 30 weeks gestation was applied to strong anion exchange (SAX2), weak cation exchange (WCX2) and immobilized metal affinity capture (IMAC‐Cu2+) chips. Mass analysis was conducted using Ciphergen Protein Biology System IIc and significant differences in individual peak intensities between groups determined.
Results: At 18 weeks gestation, 91 peaks were significantly different between pregnant women with and without asthma, representing 28% of the total peaks identified. At 30 weeks gestation, 51 peaks were significantly different. There were two peaks that were significantly different between groups at both 18 and 30 weeks gestation and expressed at a similar level at both time points. One was increased in asthmatics (MW = 6444 Da) whereas the other decreased in asthmatics compared with non‐asthmatic women (MW = 1846 Da).
Conclusions: This study demonstrated that there are differences in protein patterns between pregnant women with and without asthma. Other techniques are needed to define the molecular species and classify pathophysiological significance. Surface‐enhanced laser desorption ionization‐time of flight mass spectrometry has potential as a tool to monitor disease progression in situations such as pregnancy.
The Gravity Spy project aims to uncover the origins of glitches, transient bursts of noise that hamper analysis of gravitational-wave data. By using both the work of citizen-science volunteers and ...machine learning algorithms, the Gravity Spy project enables reliable classification of glitches. Citizen science and machine learning are intrinsically coupled within the Gravity Spy framework, with machine learning classifications providing a rapid first-pass classification of the dataset and enabling tiered volunteer training, and volunteer-based classifications verifying the machine classifications, bolstering the machine learning training set and identifying new morphological classes of glitches. These classifications are now routinely used in studies characterizing the performance of the LIGO gravitational-wave detectors. Providing the volunteers with a training framework that teaches them to classify a wide range of glitches, as well as additional tools to aid their investigations of interesting glitches, empowers them to make discoveries of new classes of glitches. This demonstrates that, when giving suitable support, volunteers can go beyond simple classification tasks to identify new features in data at a level comparable to domain experts. The Gravity Spy project is now providing volunteers with more complicated data that includes auxiliary monitors of the detector to identify the root cause of glitches.
We construct a catalog of star clusters from Hubble Space Telescope images of the inner disk of the Triangulum Galaxy (M33) using image classifications collected by the Local Group Cluster Search, a ...citizen science project hosted on the Zooniverse platform. We identify 1214 star clusters within the Hubble Space Telescope imaging footprint of the Panchromatic Hubble Andromeda Treasury: Triangulum Extended Region (PHATTER) survey. Comparing this catalog to existing compilations in the literature, 68% of the clusters are newly identified. The final catalog includes multi-band aperture photometry and fits for cluster properties via integrated light SED fitting. The cluster catalog's 50% completeness limit is ~1500 solar masses at an age of 100 Myr, as derived from comprehensive synthetic cluster tests.
We present the final legacy version of stellar photometry for the Panchromatic Hubble Andromeda Treasury (PHAT) survey. We have reprocessed all of the Hubble Space Telescope (HST) Wide Field Camera 3 ...(WFC3) and Advanced Camera for Surveys (ACS) near ultraviolet (F275W, F336W), optical (F475W, F814W), and near infrared (F110W, F160W) imaging from the PHAT survey using an improved method that optimized the survey depth and chip gap coverage by including all overlapping exposures in all bands in the photometry. An additional improvement was gained through the use of charge transfer efficiency (CTE) corrected input images, which provide more complete star finding as well as more reliable photometry for the NUV bands, which had no CTE correction in the previous version of the PHAT photometry. While this method requires significantly more computing resources and time than earlier versions where the photometry was performed on individual pointings, it results in smaller systematic instrumental completeness variations as demonstrated by cleaner maps in stellar density, and it results in optimal constraints on stellar fluxes in all bands from the survey data. Our resulting catalog has 138 million stars, 18% more than the previous catalog, with lower density regions gaining as much as 40% more stars. The new catalog produces nearly seamless population maps which show relatively well-mixed distributions for populations associated with ages older than 1-2 Gyr, and highly structured distributions for the younger populations.
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