arXiv subjects

B. Healy

Publications and source records attributed to B. Healy.

3 recordsLinked to original sources

arXiv2024

ZTF SN~Ia DR2: Cosmology-independent constraints on Type Ia supernova standardisation from supernova siblings

Understanding Type Ia supernovae (SNe~Ia) and the empirical standardisation relations that make them excellent distance indicators is vital to improving cosmological constraints. SN~Ia ``siblings", i.e. two or more SNe~Ia in the same host or parent galaxy offer a unique way to infer the standardisation relations and their diversity across the population. We analyse a sample of 25 SN~Ia pairs, observed homogeneously by the Zwicky Transient Factory (ZTF) to infer the SNe~Ia light curve width-luminosity and colour-luminosity parameters $\alpha$ and $\beta$. Using the pairwise constraints from siblings, allowing for a diversity in the standardisation relations, we find $\alpha = 0.218 \pm 0.055 $ and $\beta = 3.084 \pm 0.312$, respectively, with a dispersion in $\alpha$ and $\beta$ of $\leq 0.195$ and $\leq 0.923$, respectively, at 95$\%$ C.L. While the median dispersion is large, the values within $\sim 1 \sigma$ are consistent with no dispersion. Hence, fitting for a single global standardisation relation, we find $\alpha = 0.228 \pm 0.029 $ and $\beta = 3.160 \pm 0.191$. We find a very small intrinsic scatter of the siblings sample $\sigma_{\rm int} \leq 0.10$ at 95\% C.L. compared to $\sigma_{\rm int} = 0.22 \pm 0.04$ when computing the scatter using the Hubble residuals without comparing them as siblings. Splitting the sample based on host galaxy stellar mass, we find that SNe~Ia in both subsamples have consistent $\alpha$ and $\beta$. The $\beta$ value is consistent with the value for the cosmological sample. However, we find a higher $\alpha$ by $\sim 2.5 - 3.5 \sigma$. The high $\alpha$ is driven by low $x_1$ pairs, potentially suggesting that the slow and fast declining SN~Ia have different slopes of the width-luminosity relation. We can confirm or refute this with increased statistics from near future time-domain surveys. (abridged)

astro-ph.CO↗

arXiv2024

Spectroscopic observations of progenitor activity 100 days before a Type Ibn supernova

Obtaining spectroscopic observations of the progenitors of core-collapse supernovae is often unfeasible due to an inherent lack of knowledge as to which stars will go supernova and when they will explode. In this letter, we present photometric and spectroscopic observations of the progenitor activity of SN 2023fyq in the preceding 150 days before the He-rich progenitor exploded as a Type Ibn supernova. The progenitor of SN 2023fyq shows an exponential rise in flux prior to core-collapse. Complex He I emission line features are observed, with a P-Cygni like profile, as well as an evolving broad base with velocities on the order of 10,000 km/s, possibly due to electron scattering. The luminosity and evolution of SN 2023fyq are consistent with a faint Type Ibn, reaching a peak r-band magnitude of 18.1 mag, although there is some uncertainty in the distance to the host, NGC 4388, located in the Virgo cluster. We present additional evidence of asymmetric He-rich material being present prior to the explosion of SN 2023fyq, as well as after, suggesting this material has survived the ejecta-CSM interaction. Broad [O I] and the Ca II triplet lines are observed at late phases, confirming that SN 2023fyq was a genuine supernova rather than a non-terminal interacting transient. SN 2023fyq provides insight into the final moments of a massive star's life, highlighting that the progenitor is likely highly unstable before core-collapse.

astro-ph.HE↗

arXiv2023

An Optimal Control Strategy for Execution of Large Stock Orders Using LSTMs

In this paper, we simulate the execution of a large stock order with real data and general power law in the Almgren and Chriss model. The example that we consider is the liquidation of a large position executed over the course of a single trading day in a limit order book. Transaction costs are incurred because large orders walk the order book, that is, they consume order book liquidity beyond the best bid/ask. We model the order book with a power law that is proportional to trading volume, and thus transaction costs are inversely proportional to a power of trading volume. We obtain a policy approximation by training a long short term memory (LSTM) neural network to minimize transaction costs accumulated when execution is carried out as a sequence of smaller suborders. Using historical S&P100 price and volume data, we evaluate our LSTM strategy relative to strategies based on time-weighted average price (TWAP) and volume-weighted average price (VWAP). For execution of a single stock, the input to the LSTM is the cross section of data on all 100 stocks, including prices, volumes, TWAPs and VWAPs. By using this data cross section, the LSTM should be able to exploit inter-stock co-dependence in volume and price movements, thereby reducing transaction costs for the day. Our tests on S&P100 data demonstrate that in fact this is so, as our LSTM strategy consistently outperforms TWAP and VWAP-based strategies.

q-fin.CP↗