Growing evidence suggests that protoplanetary discs may be influenced by late stage infall from the interstellar medium (ISM). It remains unclear the degree to which infall shapes disc populations at ages $\gtrsim 1$~Myr. We explore possible spatial correlations between stellar accretion rates in the Lupus star forming region, which would support the hypothesis that infall can regulate stellar accretion. We consider both the `clustered' stars towards the center of Lupus 3, and the `distributed' stars that are more sparsely distributed across the Lupus complex. We take the observed accretion rates in the literature and explore spatial correlations. In particular, we test whether the clustered stars exhibit a radial gradient in normalised accretion rates, and whether the distributed stars have spatially correlated accretion rates. We find statistically significant correlations for both the clustered and distributed samples. The clustered sample exhibits higher accretion rates in the central region, consistent with the expected Bondi-Hoyle-Lyttleton accretion rate. Stars that are spatially closer among the distributed population also exhibit more similar accretion rates. These results cannot be explained by the stellar mass distribution for either sample. Age gradients are disfavoured, though not discounted, because normalised disc dust masses are not spatially correlated across the region. Spatially correlated stellar accretion rates within the Lupus star forming region argue in favour of an environmental influence on stellar accretion, possibly combined with internal processes in the inner disc. Refined age measurements and searches for evidence of infalling material is a potential way to further test this finding.
We present photometric and spectroscopic observations of SN2020xga and SN2022xgc, two hydrogen-poor superluminous supernovae (SLSNe-I) at $z = 0.4296$ and $z = 0.3103$ respectively, that show an additional set of broad Mg II absorption lines, blueshifted by a few thousand km s$^{-1}$ with respect to the host galaxy absorption system. Previous work interpreted this as due to resonance line scattering of the SLSN continuum by rapidly expanding CSM expelled shortly before the explosion. The peak rest-frame $g$-band magnitude of SN2020xga is $-22.30 \pm 0.04$ mag and of SN2022xgc is $-21.97 \pm 0.05$ mag, placing them among the brightest SLSNe-I. We use high-quality spectra from ultraviolet to near-infrared wavelengths to model the Mg II line profiles and infer the properties of the CSM shells. We find that the CSM shell of SN2020xga resides at $\sim 1.3 \times 10^{16} \rm cm$ moving with a maximum velocity of $4275~\rm km~s^{-1}$, and the shell of SN2022xgc is located at $\sim 0.8 \times 10^{16} \rm cm$ reaching up to $4400~\rm km~s^{-1}$. These shells were expelled $\sim 11$ and $\sim 5$ months before explosion for SN2020xga and SN2022xgc respectively, possibly as a result of Luminous Blue Variable-like eruptions or pulsational pair instability (PPI) mass loss. We also analyze optical photometric data and model the light curves considering powering from the magnetar spin-down mechanism. The results support very energetic magnetars, approaching the mass-shedding limit, powering these SNe with ejecta masses of $\sim 7-9 \rm~M_\odot$. The ejecta masses inferred from the magnetar modeling are not consistent with the PPI scenario pointing towards stars $> 50~\rm M_\odot$ He-core, hence alternative scenarios such as fallback accretion are discussed.
We report on the discovery and spectroscopic confirmation of TOI-2458 b, a transiting mini-Neptune around an F-type star leaving the main-sequence with a mass of $M_\star=1.05 \pm 0.03$ M$_{\odot}$, a radius of $R_\star=1.31 \pm 0.03$ R$_{\odot}$, an effective temperature of $T_{\rm eff}=6005\pm50$ K, and a metallicity of $-0.10\pm0.05$ dex. By combining TESS photometry with high-resolution spectra acquired with the HARPS spectrograph, we found that the transiting planet has an orbital period of $\sim$3.74 days, a mass of $M_p=13.31\pm0.99$ M$_{\oplus}$ and a radius of $R_p=2.83\pm0.20$ R$_{\oplus}$. The host star TOI-2458 shows a short activity cycle of $\sim$54 days revealed in the HARPS S-index time series. We took the opportunity to investigate other F stars showing activity cycle periods comparable to that of TOI-2458 and found that they have shorter rotation periods than would be expected based on the gyrochronology predictions. In addition, we determined TOI-2458's stellar inclination angle to be $i_\star\,=\,10.6_{-10.6}^{+13.3}$ degrees. We discuss that both phenomena (fast stellar rotation and planet orbit inclination) could be explained by in situ formation of a hot Jupiter interior to TOI-2458 b. It is plausible that this hot Jupiter was recently engulfed by the star. Analysis of HARPS spectra has identified the presence of another planet with a period of $P\,=\,16.55\pm0.06$ days and a minimum mass of $M_p \sin i=10.22\pm1.90$ M$_{\oplus}$.
Standard cosmological analysis, which relies on two-point statistics, fails to extract the full information of the data. This limits our ability to constrain with precision cosmological parameters. Thus, recent years have seen a paradigm shift from analytical likelihood-based to simulation-based inference. However, such methods require a large number of costly simulations. We focus on full-field inference, considered the optimal form of inference. Our objective is to benchmark several ways of conducting full-field inference to gain insight into the number of simulations required for each method. We make a distinction between explicit and implicit full-field inference. Moreover, as it is crucial for explicit full-field inference to use a differentiable forward model, we aim to discuss the advantages of having this property for the implicit approach. We use the sbi_lens package which provides a fast and differentiable log-normal forward model. This forward model enables us to compare explicit and implicit full-field inference with and without gradient. The former is achieved by sampling the forward model through the No U-Turns sampler. The latter starts by compressing the data into sufficient statistics and uses the Neural Likelihood Estimation algorithm and the one augmented with gradient. We perform a full-field analysis on LSST Y10 like weak lensing simulated mass maps. We show that explicit and implicit full-field inference yield consistent constraints. Explicit inference requires 630 000 simulations with our particular sampler corresponding to 400 independent samples. Implicit inference requires a maximum of 101 000 simulations split into 100 000 simulations to build sufficient statistics (this number is not fine tuned) and 1 000 simulations to perform inference. Additionally, we show that our way of exploiting the gradients does not significantly help implicit inference.