arXiv subjects

Jürgen J. Popp

Publications and source records attributed to Jürgen J. Popp.

6 recordsLinked to original sources

arXiv2026

Physical properties of star-forming clumps in nearby galaxies from CLAUDS and HSC-SSP

Giant Star-forming Clumps (GSFCs) are kpc-scale regions of enhanced star-formation with stellar masses of $10^6$ to $10^{10}\,M_\odot$ that are commonly observed in high-redshift ($z \gtrsim 1$) galaxies but their formation and role in galaxy evolution remain unclear. We applied a Faster R-CNN object detection framework (FRCNN) to identify star-forming clumps in astrophysical imaging data from a mass-complete sample of $\sim$210,000 low-redshift galaxies ($z\lesssim0.32$), located in the HSC-SSP Wide and Deep fields observed by the Hyper Suprime-Cam Subaru Strategic Survey (HSC-SSP) and CFHT Large Area U-band Deep Survey (CLAUDS). We present a population of $\sim$710,000 detected clumps for which we measured the (u)grizy-photometry and derived the physical properties (stellar mass, age, dust extinction and star-formation rate) for each clump by fitting the photometric data to stellar population models. We estimate that the clump Stellar Mass Function (cSMF) for young clumps ($<100\,\mathrm{Myr}$) follows a power law with an exponent of $α=1.98\pm0.02$ down to a completeness limit of $\sim10^{8}\,M_\odot$ which is consistent with an in situ formation through violent disk instabilities (VDI). The clumps show elevated star-formation rates and negative gradients in their ages and stellar masses as a function of galactocentric distance. These gradients are steeper than those measured for the intra-clump regions of the host galaxies and are robust against uncertainties introduced by different modelling assumptions. For nearby galaxies we argue that the observed low-redshift clumps are likely analogues of high-redshift GSFCs and that the inward migration of star-forming clumps directly contributes to the central bulge formation of the host galaxy.

astro-ph.GA↗

arXiv2026

The fraction of clumpy star-forming galaxies in nearby galaxies from CLAUDS and HSC-SSP

Massive, star-forming clumps are regions of intensive star-formation that are commonly observed in high-redshift ($z > 1$) galaxies. Observations of low-redshift clumpy galaxy analogues are rare but the availability of wide-field galaxy survey data makes the detection of large clumpy galaxy samples much more feasible. We present a population of 12,790 star-forming clumps detected in a mass-complete sample of 5,395 star-forming galaxies (SFGs) at redshifts $z\leq0.32$, located in the XMM-LSS, E-COSMOS and DEEP2-3 fields observed by the Hyper Suprime-Cam Subaru Strategic Survey (HSC-SSP) and CFHT Large Area U-band Deep Survey (CLAUDS). The clumps were detected using an improved version of our Deep Learning (DL)-based object detection framework which uses the ZOOBOT foundation DL-model as a 'backbone' feature extractor. We determined the fraction of star-forming galaxies hosting at least one off-centre clump ($f_{\mathrm{clumpy}}$) based on a clump definition that requires a clump-galaxy flux ratio in the CLAUDS u-band of $\geq8\%$. We estimate $f_{\mathrm{clumpy}}$ to decrease from $\sim$31\% at $z\sim0.3$ to $\sim$23\% at $z \sim 0.1$, which aligns well with a low-redshift extrapolation of the clumpy fraction that is measured using high-redshift observations. At fixed redshift, $f_{\mathrm{clumpy}}$ is negatively correlated with the stellar mass and positively correlated with the specific star-formation rate (sSFR) of the host galaxies. When the clump definition is changed to include only clumps with a stellar mass of $M_{\mathrm{cl}} \geq 10^7 M_\odot$, we observe a highly increased clumpy fraction of $\sim$60\% that tends to increase with the stellar mass of the host galaxies but does not show a dependence on the sSFR of the host galaxies.

astro-ph.GA↗

arXiv2026

Star-forming clump detection in nearby galaxies using Faster R-CNN and $ugrizy$ imaging data from CLAUDS and HSC-SSP

Giant Star-forming Clumps (GSFCs) are kpc-scale regions of enhanced star-formation with stellar masses of $10^7$ to $10^9\,M_\odot$ that are commonly observed in high-redshift galaxies but are rarely detected in low-redshift ($z\lesssim0.5$) galaxy analogues. However, the availability of wide-field galaxy survey data makes it possible to identify potential star-forming clumps in large samples of low-redshift galaxies using object detection models that are based on Deep Learning (DL) techniques. We apply a novel DL-based object detection model to galaxies observed by the Hyper Suprime-Cam Subaru Strategic Survey (HSC-SSP) and CFHT Large Area U-band Deep Survey (CLAUDS). Our model is based on the the Faster Region-Based Convolutional Neural Network (Faster R-CNN or FRCNN) object detection framework but expanded to process the six $ugrizy$ filter band images simultaneously and identify not only clumps and their locations in the host galaxy but also additional contaminants. By adopting the \textsc{Zoobot} foundation DL-model as a feature extraction backbone, we also demonstrate one of the first applications of \textsc{Zoobot} in a downstream task for object detection. Our model achieves a detection completeness of $\gtrsim 0.9$ and purity of $\gtrsim 0.8$ which were validated on a large set of real galaxies into which simulated clumps were injected.

astro-ph.IM↗

arXiv2026

Deep Learning Segmentation of Spiral Arms and Bars for 600,000 Galaxies in DESI

We present a catalogue of segmentation maps identifying the extent of spiral arms and bars of 639,636 galaxies in the DESI Legacy Survey. To produce these maps, we have trained a deep U-net-style neural network using the pixel masks from the Galaxy Zoo: 3D citizen science project. The resulting data products are "soft" segmentation maps, which show the confidence of the model that a pixel lies within the spiral arms or bars of a galaxy. In this paper we detail the sample selection from DESI-LS using the machine classifications from Galaxy Zoo: DESI, the architecture of the U-net model--dubbed ZooBot:3D. We demonstrate the ability of the model to identify spiral arms and bars in a wide range of face-on disks, and identify an emergent ability to identify rings--despite only a small number of ring-type galaxies being present in the training data. Finally, we discuss the practical application of these data products to photometric imaging and IFU spectroscopy. The ZooBot:3D dataset is available for use publicly, and contains the full catalogue presented in this paper, along with cross-matched subsamples for the MaNGA and SAMI IFU surveys.

astro-ph.GA↗

arXiv2024

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.

astro-ph.GA↗

arXiv2024

Scaling Laws for Galaxy Images

We present the first systematic investigation of supervised scaling laws outside of an ImageNet-like context - on images of galaxies. We use 840k galaxy images and over 100M annotations by Galaxy Zoo volunteers, comparable in scale to Imagenet-1K. We find that adding annotated galaxy images provides a power law improvement in performance across all architectures and all tasks, while adding trainable parameters is effective only for some (typically more subjectively challenging) tasks. We then compare the downstream performance of finetuned models pretrained on either ImageNet-12k alone vs. additionally pretrained on our galaxy images. We achieve an average relative error rate reduction of 31% across 5 downstream tasks of scientific interest. Our finetuned models are more label-efficient and, unlike their ImageNet-12k-pretrained equivalents, often achieve linear transfer performance equal to that of end-to-end finetuning. We find relatively modest additional downstream benefits from scaling model size, implying that scaling alone is not sufficient to address our domain gap, and suggest that practitioners with qualitatively different images might benefit more from in-domain adaption followed by targeted downstream labelling.

cs.CV↗