Galaxy Zoo: kinematics of strongly and weakly barred galaxies

We study the bar pattern speeds and corotation radii of 225 barred galaxies, using integral field unit data from MaNGA and the Tremaine–Weinberg method. Our sample, which is divided between strongly and weakly barred galaxies identified via Galaxy Zoo, is the largest that this method has been applied to. We find lower pattern speeds for strongly barred galaxies than for weakly barred galaxies. As simulations show that the pattern speed decreases as the bar exchanges angular momentum with its host, these results suggest that strong bars are more evolved than weak bars. Interestingly, the corotation radius is not different between weakly and strongly barred galaxies, despite being proportional to bar length. We also find that the corotation radius is significantly different between quenching and star-forming galaxies. Additionally, we find that strongly barred galaxies have significantly lower values for R, the ratio between the corotation radius and the bar radius, than weakly barred galaxies, despite a big overlap in both distributions. This ratio classifies bars into ultrafast bars (R 1.4; 62 per cent). Simulations show that R is correlated with the bar formation mechanism, so our results suggest that strong bars are more likely to be formed by different mechanisms than weak bars. Finally, we find a lower fraction of ultrafast bars than most other studies, which decreases the recently claimed tension with Lambda cold dark matter. However, the median value of R is still lower than what is predicted by simulations

Galaxy Zoo: Kinematics of strongly and weakly barred galaxies

We study the bar pattern speeds and corotation radii of 225 barred galaxies, using IFU data from MaNGA and the Tremaine-Weinberg method. Our sample, which is divided between strongly and weakly barred galaxies identified via Galaxy Zoo, is the largest that this method has been applied to. We find lower pattern speeds for strongly barred galaxies than for weakly barred galaxies. As simulations show that the pattern speed decreases as the bar exchanges angular momentum with its host, these results suggest that strong bars are more evolved than weak bars. Interestingly, the corotation radius is not different between weakly and strongly barred galaxies, despite being proportional to bar length. We also find that the corotation radius is significantly different between quenching and star forming galaxies. Additionally, we find that strongly barred galaxies have significantly lower values for R, the ratio between the corotation radius and the bar radius, than weakly barred galaxies, despite a big overlap in both distributions. This ratio classifies bars into ultrafast bars (R < 1.0; 11% of our sample), fast bars (1.0 < R < 1.4; 27%) and slow bars (R > 1.4; 62%). Simulations show that R is correlated with the bar formation mechanism, so our results suggest that strong bars are more likely to be formed by different mechanisms than weak bars. Finally, we find a lower fraction of ultrafast bars than most other studies, which decreases the recently claimed tension with {\Lambda}CDM. However, the median value of R is still lower than what is predicted by simulations.Comment: 20 pages, 16 figure

From fat droplets to floating forests: cross-domain transfer learning using a PatchGAN-based segmentation model

Many scientific domains gather sufficient labels to train machine algorithms through human-in-the-loop techniques provided by the Zooniverse.org citizen science platform. As the range of projects, task types and data rates increase, acceleration of model training is of paramount concern to focus volunteer effort where most needed. The application of Transfer Learning (TL) between Zooniverse projects holds promise as a solution. However, understanding the effectiveness of TL approaches that pretrain on large-scale generic image sets vs. images with similar characteristics possibly from similar tasks is an open challenge. We apply a generative segmentation model on two Zooniverse project-based data sets: (1) to identify fat droplets in liver cells (FatChecker; FC) and (2) the identification of kelp beds in satellite images (Floating Forests; FF) through transfer learning from the first project. We compare and contrast its performance with a TL model based on the COCO image set, and subsequently with baseline counterparts. We find that both the FC and COCO TL models perform better than the baseline cases when using >75% of the original training sample size. The COCO-based TL model generally performs better than the FC-based one, likely due to its generalized features. Our investigations provide important insights into usage of TL approaches on multi-domain data hosted across different Zooniverse projects, enabling future projects to accelerate task completion.Comment: 5 pages, 4 figures, accepted for publication at the Proceedings of the ACM/CIKM 2022 (Human-in-the-loop Data Curation Workshop

Distinguishing Mergers and Disks in High Redshift Observations of Galaxy Kinematics

The majority of massive star-forming galaxies at $z\sim2$ have velocity gradients suggestive of rotation, in addition to large amounts of disordered motions. In this paper, we demonstrate that it is challenging to distinguish the regular rotation of a disk galaxy from the orbital motions of merging galaxies with seeing-limited data. However, the merger fractions at $z\sim2$ are likely too low for this to have a large effect on measurements of disk fractions. To determine how often mergers pass for disks, we look to galaxy formation simulations. We analyze $\sim$24000 synthetic images and kinematic maps of 31 high-resolution simulations of isolated galaxies and mergers at $z\sim2$. We determine if the synthetic observations pass criteria commonly used to identify disk galaxies, and whether the results are consistent with their intrinsic dynamical states. Galaxies that are intrinsically mergers pass the disk criteria for anywhere from 0 to 100$\%$ of sightlines. The exact percentage depends strongly on the specific disk criteria adopted, and weakly on the separation of the merging galaxies. Therefore, one cannot tell with certainty whether observations of an individual galaxy indicate a merger or a disk. To estimate the fraction of mergers passing as disks in current kinematics samples, we combine the probability that a merger will pass as a disk with theoretical merger fractions from a cosmological simulation. Taking the latter at face-value, the observed disk fractions are overestimated by small amounts: at most by $5\%$ at high stellar mass ($10^{10-11}$ M$_{\odot}$) and $15\%$ at low stellar mass ($10^{9-10}$ M$_{\odot}$).Comment: 15 pages, 10 figures, accepted for publication in Ap

Practical galaxy morphology tools from deep supervised representation learning

Astronomers have typically set out to solve supervised machine learning problems by creating their own representations from scratch. We show that deep learning models trained to answer every Galaxy Zoo DECaLS question learn meaningful semantic representations of galaxies that are useful for new tasks on which the models were never trained. We exploit these representations to outperform several recent approaches at practical tasks crucial for investigating large galaxy samples. The first task is identifying galaxies of similar morphology to a query galaxy. Given a single galaxy assigned a free text tag by humans (e.g. ‘#diffuse’), we can find galaxies matching that tag for most tags. The second task is identifying the most interesting anomalies to a particular researcher. Our approach is 100 per cent accurate at identifying the most interesting 100 anomalies (as judged by Galaxy Zoo 2 volunteers). The third task is adapting a model to solve a new task using only a small number of newly-labelled galaxies. Models fine-tuned from our representation are better able to identify ring galaxies than models fine-tuned from terrestrial images (ImageNet) or trained from scratch. We solve each task with very few new labels; either one (for the similarity search) or several hundred (for anomaly detection or fine-tuning). This challenges the longstanding view that deep supervised methods require new large labelled datasets for practical use in astronomy. To help the community benefit from our pretrained models, we release our fine-tuning code zoobot. Zoobot is accessible to researchers with no prior experience in deep learning

Galaxy zoo DESI: large-scale bars as a secular mechanism for triggering AGN

Despite the evidence that supermassive black holes (SMBHs) co-evolve with their host galaxy, and that most of the growth of these SMBHs occurs via merger-free processes, the underlying mechanisms which drive this secular co-evolution are poorly understood. We investigate the role that both strong and weak large-scale galactic bars play in mediating this relationship. Using 72,940 disc galaxies in a volume-limited sample from Galaxy Zoo DESI, we analyse the active galactic nucleus (AGN) fraction in strongly barred, weakly barred, and unbarred galaxies up to z = 0.1 over a range of stellar masses and colours. After controlling for stellar mass and colour, we find that the optically selected AGN fraction is 31.6 ± 0.9 per cent in strongly barred galaxies, 23.3 ± 0.8 per cent in weakly barred galaxies, and 14.2 ± 0.6 per cent in unbarred disc galaxies. These are highly statistically robust results, strengthening the tantalising results in earlier works. Strongly barred galaxies have a higher fraction of AGNs than weakly barred galaxies, which in turn have a higher fraction than unbarred galaxies. Thus, while bars are not required in order to grow a SMBH in a disc galaxy, large-scale galactic bars appear to facilitate AGN fuelling, and the presence of a strong bar makes a disc galaxy more than twice as likely to host an AGN than an unbarred galaxy at all galaxy stellar masses and colours

Optimized Photometric Redshifts for the Cosmic Assembly Near-infrared Deep Extragalactic Legacy Survey (CANDELS)

We present the first comprehensive release of photometric redshifts (photo- z's) from the Cosmic Assembly Near-Infrared Deep Extragalactic Legacy Survey (CANDELS) team. We use statistics based upon the Quantile-Quantile (Q-Q) plot to identify biases and signatures of underestimated or overestimated errors in photo- z probability density functions (PDFs) produced by six groups in the collaboration; correcting for these effects makes the resulting PDFs better match the statistical definition of a PDF. After correcting each group’s PDF, we explore three methods of combining the different groups’ PDFs for a given object into a consensus curve. Two of these methods are based on identifying the minimum f-divergence curve, i.e., the PDF that is closest in aggregate to the other PDFs in a set (analogous to the median of an array of numbers). We demonstrate that these techniques yield improved results using sets of spectroscopic redshifts independent of those used to optimize PDF modifications. The best photo- z PDFs and point estimates are achieved with the minimum f-divergence using the best four PDFs for each object (mFDa4) and the hierarchical Bayesian (HB4) methods, respectively. The HB4 photo- z point estimates produced σ NMAD = 0.0227/0.0189 and ∣Δz/(1 + z)∣ &gt; 0.15 outlier fraction = 0.067/0.019 for spectroscopic and 3D Hubble Space Telescope redshifts, respectively. Finally, we describe the structure and provide guidance for the use of the CANDELS photo- z catalogs, which are available at https://archive.stsci.edu/prepds/candels/.</p

Optimized Photometric Redshifts for the Cosmic Assembly Near-Infrared Deep Extragalactic Legacy Survey (CANDELS)

We present the first comprehensive release of photometric redshifts (photo-z's) from the Cosmic Assembly Near-Infrared Deep Extragalactic Legacy Survey (CANDELS) team. We use statistics based upon the Quantile-Quantile (Q--Q) plot to identify biases and signatures of underestimated or overestimated errors in photo-z probability density functions (PDFs) produced by six groups in the collaboration; correcting for these effects makes the resulting PDFs better match the statistical definition of a PDF. After correcting each group's PDF, we explore three methods of combining the different groups' PDFs for a given object into a consensus curve. Two of these methods are based on identifying the minimum f-divergence curve, i.e., the PDF that is closest in aggregate to the other PDFs in a set (analogous to the median of an array of numbers). We demonstrate that these techniques yield improved results using sets of spectroscopic redshifts independent of those used to optimize PDF modifications. The best photo-z PDFs and point estimates are achieved with the minimum f-divergence using the best 4 PDFs for each object (mFDa4) and the Hierarchical Bayesian (HB4) methods, respectively. The HB4 photo-z point estimates produced $\sigma_{\rm NMAD} = 0.0227/0.0189$ and $|\Delta z/(1+z)| > 0.15$ outlier fraction = 0.067/0.019 for spectroscopic and 3D-HST redshifts, respectively. Finally, we describe the structure and provide guidance for the use of the CANDELS photo-z catalogs, which are available at https://archive.stsci.edu/hlsp/candels.Comment: 35 pages, 19 figures, submitted to ApJ, data available at https://archive.stsci.edu/hlsp/candel

Investigating the Effect of Galaxy Interactions on Star Formation at 0.5<z<3.0

Observations and simulations of interacting galaxies and mergers in the local universe have shown that interactions can significantly enhance the star formation rates (SFR) and fueling of Active Galactic Nuclei (AGN). However, at higher redshift, some simulations suggest that the level of star formation enhancement induced by interactions is lower due to the higher gas fractions and already increased SFRs in these galaxies. To test this, we measure the SFR enhancement in a total of 2351 (1327) massive ($M_*>10^{10}M_\odot$) major ($1<M_1/M_2<4$) spectroscopic galaxy pairs at 0.5<z<3.0 with $\Delta V <5000$ km s$^{-1}$ (1000 km s$^{-1}$) and projected separation <150 kpc selected from the extensive spectroscopic coverage in the COSMOS and CANDELS fields. We find that the highest level of SFR enhancement is a factor of 1.23$^{+0.08}_{-0.09}$ in the closest projected separation bin (<25 kpc) relative to a stellar mass-, redshift-, and environment-matched control sample of isolated galaxies. We find that the level of SFR enhancement is a factor of $\sim1.5$ higher at 0.5<z<1 than at 1<z<3 in the closest projected separation bin. Among a sample of visually identified mergers, we find an enhancement of a factor of 1.86$^{+0.29}_{-0.18}$ for coalesced systems. For this visually identified sample, we see a clear trend of increased SFR enhancement with decreasing projected separation (2.40$^{+0.62}_{-0.37}$ vs.\ 1.58$^{+0.29}_{-0.20}$ for 0.5<z<1.6 and 1.6<z<3.0, respectively). The SFR enhancement seen in our interactions and mergers are all lower than the level seen in local samples at the same separation, suggesting that the level of interaction-induced star formation evolves significantly over this time period.Comment: 23 pages, 13 figures, Accepted for publication in Ap

Major merging history in CANDELS. I. Evolution of the incidence of massive galaxy–galaxy pairs from z = 3 to z ∼ 0

The rate of major galaxy–galaxy merging is theoretically predicted to steadily increase with redshift during the peak epoch of massive galaxy development (1 ≤ z ≤ 3). We use close-pair statistics to objectively study the incidence of massive galaxies (stellar M1 > 2 × 1010 M⊙) hosting major companions (1 ≤ M1/M2 ≤ 4; i.e. 4:1) companions at z > 1. We show that these evolutionary trends are statistically robust to changes in companion proximity. We find disagreements between published results are resolved when selection criteria are closely matched. If we compute merger rates using constant fraction-to-rate conversion factors (Cmerg,pair = 0.6 and Tobs,pair = 0.65 Gyr), we find that MR rates disagree with theoretical predictions at z > 1.5. Instead, if we use an evolving Tobs,pair(z) ∝ (1 + z)−2 from Snyder et al., our MR-based rates agree with theory at 0 < z < 3. Our analysis underscores the need for detailed calibration of Cmerg,pair and Tobs,pair as a function of redshift, mass, and companion selection criteria to better constrain the empirical major merger history