Zooniverse

Abstract:

This research note presents a new planet candidate orbiting the K-dwarf TIC 392664683, which was discovered by citizen scientists as part of the Planet Hunter TESS (PHT) project. Three transit-like events were observed in Sectors 38, 65 and 102; the available data imply that the orbital period must be either 23 or 46 days. The transit depth of 1964−212+433 ppm and duration of 5.4−0.2+0.4 hr are consistent across the three sectors spanning almost five years of TESS data. Using a stellar radius of 0.7740 ± 0.0168R⊙ we derive a planetary radius between 3.5 and 4.2 R⊕, approximately the same as Neptune. Comprehensive vetting mitigates the risk that this is a false positive and supports the candidate’s likely planetary nature.

Abstract:

Abstract We analyse black hole scaling relations for disk-dominated galaxies, and present evidence for secularly driven galaxy-black hole co-evolution. We do this observationally by looking at “bulgeless” galaxies, where the lack of a stellar bulge suggests a quiet merger history. We first compose a sample of 2,435 disk-dominated galaxies hosting optical broad line active galactic nuclei (AGN) from DESI data, and find a $M_{\mathrm{BH}^{}}$–$M_{*}^{}$ scaling relation of log (MBH) $= - 3.7^{+0.3}_{-0.3} + 1.00^{+0.03}_{-0.03} \log (M_*)$. From these, we identify 546 bulgeless galaxies hosting AGN, a factor of ≳ 5 larger than pre-existing samples, and find they follow the same $M_{\mathrm{BH}^{}}$-$M_{*}^{}$ relation as the general disk galaxy sample. Comparing the bulgeless galaxies to those with some bulge component, we find good agreement in the $M_{\mathrm{BH}^{}}$–$M_{*}^{}$ relation (within 3σ), but disagreement in the $M_{\mathrm{BH}^{}}$–$M_{\mathrm{*,bulge}}^{}$ relation (at >7σ). This is the opposite of what is expected from merger-driven co-evolution, suggesting secular growth is significant in establishing observed $M_{\mathrm{BH}^{}}$–$M_{*}^{}$ scaling relations. Furthermore, in a control sample of early-type galaxies we find tentative evidence of increased AGN luminosity compared to the bulgeless sample, but no statistical difference in black hole mass. This suggests the assumed merger-dominated evolutionary history of early-type galaxies does not lead to significantly increased long-term BH growth. These results support numerous recent observational and theoretical studies which suggest secular processes fuel significant supermassive black hole growth and galaxy-black hole co-evolution.

Abstract:

Abstract Strong gravitational lenses (SGLs) are rare systems whose discovery currently relies primarily on supervised machine learning methods trained on large simulated datasets. We present the first application of Astronomaly:PROTEGE to SGL discovery, demonstrating that a human-in-the-loop active learning framework can efficiently identify lenses in large imaging surveys without the need for simulated training data. We consider a sample of 3.7 million bright galaxies from the Kilo-Degree Survey (KiDS) DR4. Feature representations are extracted using a convolutional neural network pre-trained on the ImageNet dataset and subsequently fine-tuned on KiDS data using the self-supervised Bootstrap Your Own Latent (BYOL) framework. Within the embedding of these representations, the active learning loop of Astronomaly iteratively selects the most informative systems for expert inspection. A total of 3,000 objects are inspected across multiple rounds, yielding 34 high-quality (grade A/B) SGL candidates. On the basis that these systems occupy similar regions in the learned feature space, we expand this sample through nearest-neighbour similarity analysis. Including the active learning discoveries, we identify a total of 140 grade A/B candidates and more than 1,000 additional lower-confidence systems (grade C). Among the A/B candidates, 81 are newly identified, while ∼22% of previously known grade A/B KiDS lenses are recovered. These results demonstrate strong potential for next-generation surveys such as Euclid, Roman, and Rubin’s Legacy Survey of Space and Time. With approximately 60% of the high-quality candidates newly reported, this approach complements supervised methods by reducing reliance on simulations and enabling the discovery of a diverse population of SGLs.