Rubin-LSST

Abstract:

Abstract Early-time spectroscopy of supernovae (SNe), acquired within days of explosion, yields crucial insights into their outermost ejecta layers, facilitating the study of their environments, progenitor systems, and explosion mechanisms. Recent efforts in early discovery and follow-up of SNe have shown the potential insights that can be gained from early-time spectra. Surveys such as the Time-Domain Extragalactic Survey (TiDES), conducted with the 4-meter Multi-Object Spectroscopic Telescope (4MOST), will provide spectroscopic follow-up of transients discovered by the Legacy Survey of Space and Time (LSST). Current simulations indicate that early-time spectroscopic studies conducted with TiDES data will be limited by the current SN selection criteria. To enhance early-time SN spectroscopic studies from TiDES-like surveys, we propose a set of selection criteria focusing on young SNe (YSNe), which we define as SNe prior to −10 days before peak brightness. Utilising the Zwicky Transient Facility transient alerts, we developed criteria to select YSNe while minimising the sample’s contamination rate to 23percnt. The developed criteria were applied to LSST simulations, yielding a sample of 694 Deep Drilling Field survey SNe and 56260 Wide Fast Deep survey SNe for follow-up. We demonstrate that our criteria enables the selection of SNe at early-times, enhancing future early-time spectroscopic SN studies from TiDES-like surveys. Finally, we investigated 4MOST-like observing strategies to increase the sample of spectroscopically observed YSNe. We propose that a 4MOST-like observing strategy that follows LSST with a delay of 3 days is optimal for a TiDES-like SN survey in terms of the number of classifiable spectra obtained, while a 1 day delay is most optimal for enhancing the early-time science in conjunction with our YSN selection criteria.

Abstract:

Abstract We present morphological classifications of over 41 000 galaxies out to zphot ∼ 2.5 across six square degrees of the Euclid Deep Field North (EDFN) from the Hawaii Twenty Square Degree (H20) survey, a part of the wider Cosmic Dawn survey. Galaxy Zoo citizen scientists play a crucial role in the examination of large astronomical data sets through crowdsourced data mining of extragalactic imaging. This iteration, Galaxy Zoo: Cosmic Dawn (GZCD), saw tens of thousands of volunteers and the deep learning foundation model Zoobot collectively classify objects in ultra-deep multiband Hyper Suprime-Cam (HSC) imaging down to a depth of mHSC − i = 21.5. Here, we present the details and general analysis of this iteration, including the use of Zoobot in an active learning cycle to improve both model performance and volunteer experience, as well as the discovery of 51 new gravitational lenses in the EDFN. We also announce the public data release of the classifications for over 45 000 subjects, including more than 41 000 galaxies (median zphot of 0.42 ± 0.23), along with their associated image cutouts. This data set provides a valuable opportunity for follow-up imaging of objects in the EDFN as well as acting as a truth set for training deep learning models for application to ground-based surveys like that of the Ultraviolet Near-Infrared Optical Northern Survey (UNIONS) collaboration and the newly operational Vera C. Rubin Observatory.

Abstract:

Type Ia supernovae (SNe Ia) are among the most precise cosmological distance indicators used to study the expansion history of the Universe. The vast increase in SN Ia data due to large-scale astrophysical surveys has led to the discovery of a wide variety of SN Ia sub-classes, such as transitional and fast-declining SNe Ia. However, their distinct photometric and spectroscopic properties differentiate them from the population of normal SNe Ia such that their use as cosmological tools remains challenged. Here, we present a high-cadenced photometric and spectroscopic dataset of two SNe Ia, SNe 2020ue and 2020nlb, which were discovered in the nearby Virgo cluster of galaxies. Our study shows that SN 2020nlb is a normal SN Ia whose unusually red colour is intrinsic, arising from a lower photospheric temperature rather than interstellar reddening, providing clear evidence that colour diversity among normal SNe Ia can have a physical origin. In contrast, SN 2020ue has photometric properties, such as colour evolution and light curve decay rate, similar to those of transitional SNe. It is hence more spectroscopically aligned with normal SNe Ia. This is evident from spectroscopic indicators such as the pseudo-equivalent width of lines. Thus, such SNe Ia, which lie photometrically at the edge of the standard normal SNe Ia range, may be missed in cosmological SNe Ia samples. Our results highlight that a spectroscopic analysis of SNe Ia around peak brightness is crucial for identifying intrinsic colour variations and constructing a more complete and physically homogeneous SN Ia sample for precision cosmology. Si II

Abstract:

Stellar bars are elongated structures in disk galaxies that can torque and funnel gas inward, influencing galaxy evolution. While strong bars are known to induce rapid inflow, the impact of weaker bars remains less certain. We collected spectroscopic data using the Isaac Newton Telescope to analyze 18 nearby galaxies (strongly barred, weakly barred, and unbarred) drawn from Galaxy Zoo DESI. We obtained spatial profiles of equivalent width (EW) and ionized gas velocity dispersion by fitting Gaussian profiles to the Hα emission line. Strongly barred galaxies exhibit a distinctive three-peaked EW[Hα] structure, consistent with inward funneling of gas. Weakly barred systems lack this pattern, which suggests limited inflow. Velocity dispersion distributions further distinguish the bar types, with strongly barred galaxies showing significantly higher values than weakly barred and unbarred systems. These results suggest that strong bars drive gas inflow, while weak bars exert a limited dynamical influence.