arXiv subjects

Chris Frohmaier

Publications and source records attributed to Chris Frohmaier.

4 recordsLinked to original sources

arXiv2025

A Young Supernova Selection Pipeline For The LSST Era

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 23 percent. The developed criteria were applied to LSST simulations, yielding a sample of 694 Deep Drilling Field survey SNe and 56,260 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.

astro-ph.HE↗

arXiv2021

PISCOLA: a data-driven transient light-curve fitter

Forthcoming time-domain surveys, such as the Rubin Observatory Legacy Survey of Space and Time, will vastly increase samples of supernovae (SNe) and other optical transients, requiring new data-driven techniques to analyse their photometric light curves. Here, we present the "Python for Intelligent Supernova-COsmology Light-curve Analysis" (PISCOLA), an open source data-driven light-curve fitter using Gaussian Processes that can estimate rest-frame light curves of transients without the need for an underlying light-curve template. We test PISCOLA on large-scale simulations of type Ia SNe (SNe Ia) to validate its performance, and show it successfully retrieves rest-frame peak magnitudes for average survey cadences of up to 7 days. We also compare to the existing SN Ia light-curve fitter SALT2 on real data, and find only small (but significant) disagreements for different light-curve parameters. As a proof-of-concept of an application of PISCOLA, we decomposed and analysed the PISCOLA rest-frame light-curves of SNe Ia from the Pantheon SN Ia sample with Non-Negative Matrix Factorization. Our new parametrization provides a similar performance to existing light-curve fitters such as SALT2. We further derived a SN Ia colour law from PISCOLA fits over $\sim$3500 to 7000Å, and find agreement with the SALT2 colour law and with reddening laws with total-to-selective extinction ratio $R_V \lesssim 3.1$.

astro-ph.HE↗

arXiv2021

Optimising a magnitude-limited spectroscopic training sample for photometric classification of supernovae

In preparation for photometric classification of transients from the Legacy Survey of Space and Time (LSST) we run tests with different training data sets. Using estimates of the depth to which the 4-metre Multi-Object Spectroscopic Telescope (4MOST) Time Domain Extragalactic Survey (TiDES) can classify transients, we simulate a magnitude-limited sample reaching $r_{\textrm{AB}} \approx$ 22.5 mag. We run our simulations with the software snmachine, a photometric classification pipeline using machine learning. The machine-learning algorithms struggle to classify supernovae when the training sample is magnitude-limited, in contrast to representative training samples. Classification performance noticeably improves when we combine the magnitude-limited training sample with a simulated realistic sample of faint, high-redshift supernovae observed from larger spectroscopic facilities; the algorithms' range of average area under ROC curve (AUC) scores over 10 runs increases from 0.547-0.628 to 0.946-0.969 and purity of the classified sample reaches 95 per cent in all runs for 2 of the 4 algorithms. By creating new, artificial light curves using the augmentation software avocado, we achieve a purity in our classified sample of 95 per cent in all 10 runs performed for all machine-learning algorithms considered. We also reach a highest average AUC score of 0.986 with the artificial neural network algorithm. Having `true' faint supernovae to complement our magnitude-limited sample is a crucial requirement in optimisation of a 4MOST spectroscopic sample. However, our results are a proof of concept that augmentation is also necessary to achieve the best classification results.

astro-ph.IM↗

arXiv2020

Observing the earliest moments of supernovae using strong gravitational lenses

We determine the viability of exploiting lensing time delays to observe strongly gravitationally lensed supernovae (gLSNe) from first light. Assuming a plausible discovery strategy, the Legacy Survey of Space and Time (LSST) and the Zwicky Transient Facility (ZTF) will discover $\sim$ 110 and $\sim$ 1 systems per year before the supernova (SN) explosion in the final image respectively. Systems will be identified $11.7^{+29.8}_{-9.3}$ days before the final explosion. We then explore the possibility of performing early-time observations for Type IIP and Type Ia SNe in LSST-discovered systems. Using a simulated Type IIP explosion, we predict that the shock breakout in one trailing image per year will peak at $\lesssim$ 24.1 mag ($\lesssim$ 23.3) in the $B$-band ($F218W$), however evolving over a timescale of $\sim$ 30 minutes. Using an analytic model of Type Ia companion interaction, we find that in the $B$-band we should observe at least one shock cooling emission event per year that peaks at $\lesssim$ 26.3 mag ($\lesssim$ 29.6) assuming all Type Ia gLSNe have a 1 M$_\odot$ red giant (main sequence) companion. We perform Bayesian analysis to investigate how well deep observations with 1 hour exposures on the European Extremely Large Telescope would discriminate between Type Ia progenitor populations. We find that if all Type Ia SNe evolved from the double-degenerate channel, then observations of the lack of early blue flux in 10 (50) trailing images would rule out more than 27% (19%) of the population having 1 M$_\odot$ main sequence companions at 95% confidence.

astro-ph.HE↗