Hintze Centre for Astrophysical Surveys

Abstract:

Modern astronomical surveys deliver immense volumes of transient detections, yet distinguishing real astrophysical signals (for example, explosive events) from bogus imaging artefacts remains a challenge. Convolutional neural networks are effectively used for real versus bogus classification; however, their reliance on opaque latent representations hinders interpretability. Here we show that large language models (LLMs) can approach the performance level of a convolutional neural network on three optical transient survey datasets (Pan-STARRS, MeerLICHT and ATLAS) while simultaneously producing direct, human-readable descriptions for every candidate. Using only 15 examples and concise instructions, Google’s LLM, Gemini, achieves a 93% average accuracy across datasets that span a range of resolution and pixel scales. We also show that a second LLM can assess the coherence of the output of the first model, enabling iterative refinement by identifying problematic cases. This framework allows users to define the desired classification behaviour through natural language and examples, bypassing traditional training pipelines. Furthermore, by generating textual descriptions of observed features, LLMs enable users to query classifications as if navigating an annotated catalogue, rather than deciphering abstract latent spaces. As next-generation telescopes and surveys further increase the amount of data available, LLM-based classification could help bridge the gap between automated detection and transparent, human-level understanding.

Abstract:

Abstract We investigate the clustering of Lyman-break galaxies at redshifts of 3 ≲ z ≲ 5 within the COSMOS field by measuring the angular two-point correlation function. Our robust sample of ~60,000 bright (mUV ≲ 27) Lyman-break galaxies was selected based on spectral energy distribution fitting across 14 photometric bands spanning optical and near-infrared wavelengths. We constrained both the 1- and 2-halo terms at separations up to 300 arcsec, finding an excess in the correlation function at scales corresponding to <20 kpc, consistent with enhancement due to clumps in the same galaxy or interactions on this scale. We then performed Bayesian model fits on the correlation functions to infer the Halo Occupation Distribution parameters, star formation duty cycle, and galaxy bias in three redshift bins. We examined several cases where different combinations of parameters were varied, showing that our data can constrain the slope of the satellite occupation function, which previous studies have fixed. For an MUV-limited sub-sample, we found galaxy bias values of $b_g=3.18^{+0.14}_{-0.14}$ at z ≃ 3, $b_g=3.58^{+0.27}_{-0.29}$ at z ≃ 4, $b_g=4.27^{+0.25}_{-0.26}$ at z ≃ 5. The duty cycle values are $0.62^{+0.25}_{-0.26}$, $0.40^{+0.34}_{-0.22}$, and $0.39^{+0.31}_{-0.20}$,respectively. These results suggest that, as the redshift increases, there is a slight decrease in the host halo masses and a shorter timescale for star formation in bright galaxies, at a fixed rest-frame UV luminosity threshold.

Abstract:

We present multiband ATLAS and ZTF photometry for SN 2019tsf, a Type Ib stripped-envelope supernova (SESN). The slow spectral evolution could be associated with an uncommon explosion mechanism specific to this SN. Possible explanations include fallback accretion onto a compact remnant or a long-lived central engine, both of which could provide extended energy injection responsible for the late-time rebrightening and unusual spectral features. The rebrightening observations represent the latest photometric measurements of a multipeaked Type Ib SN. As late-time photometry and spectroscopy suggest no hydrogen, the potential circumstellar material (CSM) must be H-poor. The absence of a nebular phase and the lack of narrow emission lines in the late-time spectra (>142 days) of the SNe suggest that any CSM interaction is likely asymmetric and enveloped by the SN ejecta. However, an extended CSM structure is evident through a follow-up radio campaign with the Karl G. Jansky Very Large Array (VLA), indicating a source of bright optically thick radio emission at late times, which is highly unusual among H-poor SESNe. We attribute this phenomenology to an interaction of the supernova ejecta with asymmetric CSM, potentially disk-like, and we present several models that may explain the origin of this rare Type Ib supernova. We propose a warped disk model in which a tertiary companion—commonly present around massive stars—perturbs the progenitor’s CSM, producing density enhancements that may explain the observed multipeaked SN 2019tsf light curve. This SN 2019tsf is a unique SN Type Ib among the recently discovered class of SNe that undergo mass transfer at the moment of explosion.

Abstract:

Abstract Using deep observations across three of the LOFAR Two-metre Sky Survey Deep Fields, this work measures the angular clustering of star forming galaxies (SFGs) and low-excitation radio galaxies (LERGs) to z ≲1.5 for faint sources, S144 MHz ≥200 μJy. We measure the angular auto-correlation of LOFAR sources in redshift bins and their cross-correlation with multi-wavelength sources to measure the evolving galaxy bias for SFGs and LERGs. Our work shows the bias of the radio-selected SFGs increases from $b=0.90^{+0.11}_{-0.10}$ at z ∼0.2 to $b=2.94^{+0.36}_{-0.36}$ at z ∼1.2; faster than the assumed b($z$)∝1/D($z$) models adopted in previous LOFAR cosmology studies (at sensitivities where AGN dominate), but in broad agreement with previous work. We further study the luminosity dependence of bias for SFGs and find little evidence for any luminosity dependence at fixed redshift, although uncertainties remain large for the sample sizes available. The LERG population instead shows a weaker redshift evolution with $b=2.33^{+0.28}_{-0.27}$ at z ∼0.7 to $b=2.65^{+0.57}_{-0.55}$ at z ∼1.2, though it is also consistent with the assumed bias evolution model (b($z$)∝1/D($z$)) within the measured uncertainties. For those LERGs which reside in quiescent galaxies (QLERGs), there is weak evidence that they are more biased than the general LERG population and evolve from b=$2.62^{+0.33}_{-0.33}$ at z ∼0.7 to $b=3.08^{+0.85}_{-0.84}$ at z ∼1.2. This suggests the halo environment of radio sources may be related to their properties. These measurements can help constrain models for the bias evolution of these source populations, and can help inform multi-tracer analyses.