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ABSTRACT 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. 

ABSTRACT 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. 

This data set contains the individual classifications that the Gravity Spy citizen science volunteers made for glitches through 20 July 2024. Classifications made by science team members or in testing workflows have been removed as have classifications of glitches lacking a Gravity Spy identifier. See Zevin et al. (2017) for an explanation of the citizen science task and classification interface. Data about glitches with machine-learning labels are provided in an earlier data release (Glanzer et al., 2021). Final classifications combining ML and volunteer classifications are provided in Zevin et al. (2022).  22 of the classification labels match the labels used in the earlier data release, namely 1080Lines, 1400Ripples, Air_Compressor, Blip, Chirp, Extremely_Loud, Helix, Koi_Fish, Light_Modulation, Low_Frequency_Burst, Low_Frequency_Lines, No_Glitch, None_of_the_Above, Paired_Doves, Power_Line, Repeating_Blips, Scattered_Light, Scratchy, Tomte, Violin_Mode, Wandering_Line and Whistle. One glitch class that was added to the machine-learning classification has not been added to the Zooniverse project and so does not appear in this file, namely Blip_Low_Frequency. Four classes were added to the citizen science platform but not to the machine learning model and so have only volunteer labels, namely 70HZLINE, HIGHFREQUENCYBURST, LOWFREQUENCYBLIP and PIZZICATO. The glitch class Fast_Scattering added to the machine-learning classification has an equivalent volunteer label CROWN, which is used here (Soni et al. 2021). Glitches are presented to volunteers in a succession of workflows. Workflows include glitches classified by a machine learning classifier as being likely to be in a subset of classes and offer the option to classify only those classes plus None_of_the_Above. Each level includes the classes available in lower levels. The top level does not add new classification options but includes all glitches, including those for which the machine learning model is uncertain of the class. As the classes available to the volunteers change depending on the workflow, a glitch might be classified as None_of_the_Above in a lower workflow and subsequently as a different class in a higher workflow. Workflows and available classes are shown in the table below.  Workflow ID Name Number of glitch classes Glitches added 1610  Level 1 3 Blip, Whistle, None_of_the_Above 1934 Level 2 6 Koi_Fish, Power_Line, Violin_Mode 1935 Level 3 10 Chirp, Low_Frequency_Burst, No_Glitch, Scattered_Light 2360 Original level 4 22 1080Lines, 1400Ripples, Air_Compressor, Extremely_Loud, Helix, Light_Modulation, Low_Frequency_Lines, Paired_Doves, Repeating_Blips, Scratchy, Tomte, Wandering_Line 7765 New level 4 15 1080Lines, Extremely_Loud, Low_Frequency_Lines, Repeating_Blips, Scratchy 2117 Original level 5 22 No new glitch classes 7766 New level 5 27 1400Ripples, Air_Compressor, Paired_Doves, Tomte, Wandering_Line, 70HZLINE, CROWN, HIGHFREQUENCYBURST, LOWFREQUENCYBLIP, PIZZICATO 7767 Level 6 27 No new glitch classes Description of data fields Classification_id: a unique identifier for the classification. A volunteer may choose multiple classes for a glitch when classifying, in which case there will be multiple rows with the same classification_id. Subject_id: a unique identifier for the glitch being classified. This field can be used to join the classification to data about the glitch from the prior data release.  User_hash: an anonymized identifier for the user making the classification or for anonymous users an identifier that can be used to track the user within a session but which may not persist across sessions.  Anonymous_user: True if the classification was made by a non-logged in user.  Workflow: The Gravity Spy workflow in which the classification was made.  Workflow_version: The version of the workflow. Timestamp: Timestamp for the classification.  Classification: Glitch class selected by the volunteer.  Related datasets For machine learning classifications on all glitches in O1, O2, O3a, and O3b, please see Gravity Spy Machine Learning Classifications on Zenodo For classifications of glitches combining machine learning and volunteer classifications, please see Gravity Spy Volunteer Classifications of LIGO Glitches from Observing Runs O1, O2, O3a, and O3b. For the training set used in Gravity Spy machine learning algorithms, please see Gravity Spy Training Set on Zenodo. For detailed information on the training set used for the original Gravity Spy machine learning paper, please see Machine learning for Gravity Spy: Glitch classification and dataset on Zenodo. 

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. 

Abstract 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 present NIRCam and NIRISS modules for DOLPHOT, a widely used crowded-field stellar photometry package. We describe details of the modules including pixel masking, astrometric alignment, star finding, photometry, catalog creation, and artificial star tests. We tested these modules using NIRCam and NIRISS images of M92 (a Milky Way globular cluster), Draco II (an ultrafaint dwarf galaxy), and Wolf–Lundmark–Mellote (a star-forming dwarf galaxy). DOLPHOT’s photometry is highly precise, and the color–magnitude diagrams are deeper and have better definition than anticipated during original program design in 2017. The primary systematic uncertainties in DOLPHOT’s photometry arise from mismatches in the model and observed point-spread functions (PSFs) and aperture corrections, each contributing ≲0.01 mag to the photometric error budget. Version 1.2 of WebbPSF models, which include charge diffusion and interpixel capacitance effects, significantly reduced PSF-related uncertainties. We also observed minor (≲0.05 mag) chip-to-chip variations in NIRCam’s zero-points, which will be addressed by the JWST flux calibration program. Globular cluster observations are crucial for photometric calibration. Temporal variations in the photometry are generally ≲0.01 mag, although rare large misalignment events can introduce errors up to 0.08 mag. We provide recommended DOLPHOT parameters, guidelines for photometric reduction, and advice for improved observing strategies. Our Early Release Science DOLPHOT data products are available on MAST, complemented by comprehensive online documentation and tutorials for using DOLPHOT with JWST imaging data. 

Abstract 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 $7.0<\log \left(\mathrm{Age}/\mathrm{yr}\right)<8.5$, 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 We present the JWST Resolved Stellar Populations Early Release Science (ERS) program. We obtained 27.5 hr of NIRCam and NIRISS imaging of three targets in the Local Group (Milky Way globular cluster M92, ultrafaint dwarf galaxy DracoII, and star-forming dwarf galaxy WLM), which span factors of ∼10⁵in luminosity, ∼10⁴in distance, and ∼10⁵in surface brightness. We describe the survey strategy, scientific and technical goals, implementation details, present select NIRCam color–magnitude diagrams (CMDs), and validate the NIRCam exposure time calculator (ETC). Our CMDs are among the deepest in existence for each class of target. They touch the theoretical hydrogen-burning limit in M92 (<0.08M_⊙;M_F090W∼ +13.6), include the lowest-mass stars observed outside the Milky Way in Draco II (0.09M_⊙;M_F090W∼ +12.1), and reach ∼1.5 mag below the oldest main-sequence turnoff in WLM (M_F090W∼ +4.6). The PARSEC stellar models provide a good qualitative match to the NIRCam CMDs, though they are ∼0.05 mag too blue compared to M92 F090W − F150W data. Our CMDs show detector-dependent color offsets ranging from ∼0.02 mag in F090W – F150W to ∼0.1 mag in F277W – F444W; these appear to be due to differences in the zero-point calibrations among the detectors. The NIRCam ETC (v2.0) matches the signal-to-noise ratios based on photon noise in uncrowded fields, but the ETC may not be accurate in more crowded fields, similar to what is known for the Hubble Space Telescope. We release the point-source photometry package DOLPHOT, optimized for NIRCam and NIRISS, for the community. 

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.