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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 JWST 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 Mid-Infrared Instrument (MIRI) imaging mosaics at 5.6 and 21 $\mu$m that cover a significant portion of one of M33’s spiral arms that has existing panchromatic imaging from the Hubble Space Telescope and deep Atacama Large Millimeter/submillimeter Array 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 show 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.

 

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 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 Panchromatic Hubble Andromeda Treasury: Triangulum Extended Region (PHATTER) catalogue of 1214 clusters with ages measured via colour–magnitude diagram (CMD) fitting to 444 GMCs identified from a new 35 pc resolution Atacama Large Millimeter/submillimeter Array (ALMA) 12CO(2–1) survey. In M31, we compare the Panchromatic Hubble Andromeda Treasury (PHAT) catalogue of 1249 clusters to 251 GMCs measured from a Combined Array for Research in Millimeter-wave Astronomy (CARMA) 12CO(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 ≤10 Myr old. Utilizing the high precision ages of the clusters, we find that clusters older than ≈18 Myr are uncorrelated with the molecular interstellar medium (ISM). Using the spatial coincidence of the youngest clusters and GMCs in M33, we estimate that clusters spend ≈4–6 Myr inside their parent GMC. Through similar analysis, we find that the GMCs in M33 have a total lifetime of ≈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.

 

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), atz= 2.4, each to percent-level accuracy. This accuracy is determined primarily from the total number of detected Lyαemitters (LAEs), the false positive rate due to noise, and the contamination due to [Oii] emitting galaxies. This paper presents the citizen science project, Dark Energy Explorers (https://www.zooniverse.org/projects/erinmc/dark-energy-explorers), with the goal of increasing the number of LAEs and decreasing the number of false positives due to noise and the [Oii] 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 theD_A(z) andH(z) parameters atz= 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.

 

Abstract 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 multiband aperture photometry and fits for cluster properties via integrated light spectral energy distribution fitting. The cluster catalog’s 50% completeness limit is ∼1500 M ☉ at an age of 100 Myr, as derived from comprehensive synthetic cluster tests. 

We measure the star cluster mass function (CMF) for the Local Group galaxy M33. We use the catalog of stellar clusters selected from the Panchromatic Hubble Andromeda Treasury: Triangulum Extended Region survey. We analyze 711 clusters in M33 with$7.0<\log (\mathrm{Age}/\mathrm{yr})<8.5$, and log(M/M_⊙) > 3.0 as determined from color–magnitude diagram fits to individual stars. The M33 CMF is best described by a Schechter function with power-law slopeα= −${2.06}_{-0.13}^{+0.14}$, and truncation mass log(M_c/M_⊙)$={4.24}_{-0.13}^{+0.16}$. The data show strong evidence for a high-mass truncation, thus strongly favoring a Schechter function fit over a pure power law. M33's truncation mass is consistent with the previously identified linear trend betweenM_c, and star formation rate surface density, Σ_SFR. We also explore the effect that individual cluster mass uncertainties have on derived mass function parameters, and find evidence to suggest that large cluster mass uncertainties have the potential to bias the truncation mass of fitted mass functions at the 1σlevel.

 

Abstract The CO-to-H 2 conversion factor ( α CO ) is critical to studying molecular gas and star formation in galaxies. The value of α CO has been found to vary within and between galaxies, but the specific environmental conditions that cause these variations are not fully understood. Previous observations on ~kiloparsec scales revealed low values of α CO in the centers of some barred spiral galaxies, including NGC 3351. We present new Atacama Large Millimeter/submillimeter Array Band 3, 6, and 7 observations of 12 CO, 13 CO, and C 18 O lines on 100 pc scales in the inner ∼2 kpc of NGC 3351. Using multiline radiative transfer modeling and a Bayesian likelihood analysis, we infer the H 2 density, kinetic temperature, CO column density per line width, and CO isotopologue abundances on a pixel-by-pixel basis. Our modeling implies the existence of a dominant gas component with a density of 2–3 × 10 3 cm −3 in the central ∼1 kpc and a high temperature of 30–60 K near the nucleus and near the contact points that connect to the bar-driven inflows. Assuming a CO/H 2 abundance of 3 × 10 −4 , our analysis yields α CO ∼ 0.5–2.0 M ⊙ (K km s −1 pc 2 ) −1 with a decreasing trend with galactocentric radius in the central ∼1 kpc. The inflows show a substantially lower α CO ≲ 0.1 M ⊙ (K km s −1 pc 2 ) −1 , likely due to lower optical depths caused by turbulence or shear in the inflows. Over the whole region, this gives an intensity-weighted α CO of ∼1.5 M ⊙ (K km s −1 pc 2 ) −1 , which is similar to previous dust-modeling-based results at kiloparsec scales. This suggests that low α CO on kiloparsec scales in the centers of some barred galaxies may be due to the contribution of low-optical-depth CO emission in bar-driven inflows. 

Abstract Measurements of the star formation efficiency (SFE) of giant molecular clouds (GMCs) in the Milky Way generally show a large scatter, which could be intrinsic or observational. We use magnetohydrodynamic simulations of GMCs (including feedback) to forward-model the relationship between the true GMC SFE and observational proxies. We show that individual GMCs trace broad ranges of observed SFE throughout collapse, star formation, and disruption. Low measured SFEs (${\ll} 1\hbox{ per cent}$) are ‘real’ but correspond to early stages; the true ‘per-freefall’ SFE where most stars actually form can be much larger. Very high (${\gg} 10\hbox{ per cent}$) values are often artificially enhanced by rapid gas dispersal. Simulations including stellar feedback reproduce observed GMC-scale SFEs, but simulations without feedback produce 20× larger SFEs. Radiative feedback dominates among mechanisms simulated. An anticorrelation of SFE with cloud mass is shown to be an observational artefact. We also explore individual dense ‘clumps’ within GMCs and show that (with feedback) their bulk properties agree well with observations. Predicted SFEs within the dense clumps are ∼2× larger than observed, possibly indicating physics other than feedback from massive (main-sequence) stars is needed to regulate their collapse.