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We present multi-epoch optical spectropolarimetric and imaging polarimetric observations of the nearby Type II supernova (SN) 2023ixf discovered in M101 at a distance of 6.85 Mpc. The first imaging polarimetric observations were taken +2.33 days (60085.08 MJD) after the explosion, while the last imaging polarimetric data points (+73.19 and +76.19 days) were acquired after the fall from the light curve plateau. At +2.33 days there is strong evidence of circumstellar material (CSM) interaction in the spectra and the light curve. A significant level of polarization $P_r = 0.88\pm 0.06 \% $ seen during this phase indicates that this CSM is aspherical. We find that the polarization evolves with time toward the interstellar polarization level ($0.35\%$) during the photospheric phase, which suggests that the recombination photosphere is spherically symmetric. There is a jump in polarization ($P_r =0.65 \pm 0.08 \% $) at +73.19 days when the light curve falls from the plateau. This is a phase where polarimetric data is sensitive to non-spherical inner ejecta or a decrease in optical depth into the single scattering regime. We also present spectropolarimetric data that reveal line (de)polarization during most of the observed epochs. In addition, at +14.50 days we see an "inverse P Cygn" profile in the H and He line polarization, which clearly indicates the presence of asymmetrically distributed material overlying the photosphere. The overall temporal evolution of polarization is typical for Type II SNe, but the high level of polarization during the rising phase has only been observed in SN 2023ixf., Comment: 14 pages, 7 figures, submitted to ApJL, comments welcome

We present and analyze the extensive optical broadband photometry of the Type II SN 2023ixf up to one year after explosion. We find that, when compared to two pre-existing model grids, the pseudo-bolometric light curve is consistent with drastically different combinations of progenitor and explosion properties. This may be an effect of known degeneracies in Type IIP light-curve models. We independently compute a large grid of ${\tt MESA+STELLA}$ single-star progenitor and light-curve models with various zero-age main-sequence masses, mass-loss efficiencies, and convective efficiencies. Using the observed progenitor variability as an additional constraint, we select stellar models consistent with the pulsation period and explode them according to previously established scaling laws to match plateau properties. Our hydrodynamic modeling indicates that SN 2023ixf is most consistent with a moderate-energy ($E_{\rm exp}\approx7\times10^{50}$ erg) explosion of an initially high-mass red supergiant progenitor ($\gtrsim 17\ M_{\odot}$) that lost a significant amount of mass in its prior evolution, leaving a low-mass hydrogen envelope ($\lesssim 3\ M_{\odot}$) at the time of explosion, with a radius $\gtrsim 950\ R_{\odot}$ and a synthesized $^{56}$Ni mass of $0.07\ M_{\odot}$. We posit that previous mass transfer in a binary system may have stripped the envelope of SN 2023ixf's progenitor. The analysis method with pulsation period presented in this work offers a way to break degeneracies in light-curve modeling in the future, particularly with the upcoming Vera C.~Rubin Observatory Legacy Survey of Space and Time, when a record of progenitor variability will be more common., Comment: 18 pages, 7 figures, submitted to ApJ. Comments welcome

We present the most comprehensive catalog to date of Type I Superluminous Supernovae (SLSNe), a class of stripped envelope supernovae (SNe) characterized by exceptionally high luminosities. We have compiled a sample of 262 SLSNe reported through 2022 December 31. We verified the spectroscopic classification of each SLSN and collated an exhaustive data set of UV, optical and IR photometry from both publicly available data and our own FLEET observational follow-up program, totaling over 30,000 photometric detections. Using these data we derive observational parameters such as the peak absolute magnitudes, rise and decline timescales, as well as bolometric luminosities, temperature and photospheric radius evolution for all SLSNe. Additionally, we model all light curves using a hybrid model that includes contributions from both a magnetar central engine and the radioactive decay of $^{56}$Ni. We explore correlations among various physical and observational parameters, and recover the previously found relation between ejecta mass and magnetar spin, as well as the overall progenitor pre-explosion mass distribution with a peak at $\approx 6.5$ M$_\odot$. We find no significant redshift dependence for any parameter, and no evidence for distinct sub-types of SLSNe. We find that $< 3$\% of SLSNe are best fit with a significant contribution from radioactive decay $\gtrsim 50$\%, representing a set of relatively dim and slowly declining SNe. We provide several analytical tools designed to simulate typical SLSN light curves across a broad range of wavelengths and phases, enabling accurate K-corrections, bolometric scaling calculations, and inclusion of SLSNe in survey simulations or future comparison works. The complete catalog, including all of the photometry, models, and derived parameters, is made available as an open-source resource on GitHub., Comment: 59 pages, 22 Figures, Submitted to MNRAS

We present high-cadence photometric and spectroscopic observations of supernova (SN) 2024ggi, a Type II SN with flash spectroscopy features which exploded in the nearby galaxy NGC 3621 at $\sim$7 Mpc. The light-curve evolution over the first 30 hours can be fit by two power law indices with a break after 22 hours, rising from $M_V \approx -12.95$ mag at +0.66 days to $M_V \approx -17.91$ mag after 7 days. In addition, the densely sampled color curve shows a strong blueward evolution over the first few days and then behaves as a normal SN II with a redward evolution as the ejecta cool. Such deviations could be due to interaction with circumstellar material (CSM). Early high- and low-resolution spectra clearly show high-ionization flash features from the first spectrum to +3.42 days after the explosion. From the high-resolution spectra, we calculate the CSM velocity to be 37 $\pm~4~\mathrm{km\,s^{-1}} $. We also see the line strength evolve rapidly from 1.22 to 1.49 days in the earliest high-resolution spectra. Comparison of the low-resolution spectra with CMFGEN models suggests that the pre-explosion mass-loss rate of SN 2024ggi falls in a range of $10^{-3}$ to $10^{-2}$ M$_{\odot}$ yr$^{-1}$, which is similar to that derived for SN 2023ixf. However, the rapid temporal evolution of the narrow lines in the spectra of SN 2024ggi ($R_\mathrm{CSM} \sim 2.7 \times 10^{14} \mathrm{cm}$) could indicate a smaller spatial extent of the CSM than in SN 2023ixf ($R_\mathrm{CSM} \sim 5.4 \times 10^{14} \mathrm{cm}$) which in turn implies lower total CSM mass for SN 2024ggi., Comment: 23 pages, 15 figures, 4 tables, accepted for publication in ApJL

Historically, astronomy has prioritized visuals to present information, with scientists and communicators overlooking the critical need to communicate astrophysics with blind or low-vision audiences and provide novel channels for sighted audiences to process scientific information. This study sonified NASA data of three astronomical objects presented as aural visualizations, then surveyed blind or low-vision and sighted individuals to elicit feedback on the experience of these pieces as it relates to enjoyment, education, and trust of the scientific data. Data analyses from 3,184 sighted or blind or low-vision survey participants yielded significant self-reported learning gains and positive experiential responses. Results showed that astrophysical data engaging multiple senses could establish additional avenues of trust, increase access, and promote awareness of accessibility in sighted and blind or low-vision communities., Comment: Published in Frontiers in Communication on March 13th 2024. 15 pages, 9 figures, 2 tables. Supplemental data available through Frontiers publication. Accessible screen-reader version: https://docs.google.com/document/d/1IdroqbdtULo2OQFaqGw20NRVD-sLYEKBUPWnmxZ9gH0/edit?usp=sharing Press release: https://www.frontiersin.org/news/2024/03/25/communication-nasa-scientists-space-data-sounds

We present an extensive $\textit{Hubble Space Telescope}$ ($\textit{HST}$) rest-frame ultraviolet (UV) imaging study of the locations of Type I superluminous supernovae (SLSNe) within their host galaxies. The sample includes 65 SLSNe with detected host galaxies in the redshift range $z\approx 0.05-2$. Using precise astrometric matching with SN images, we determine the distributions of physical and host-normalized offsets relative to the host centers, as well as the fractional flux distribution relative to the underlying UV light distribution. We find that the host-normalized offsets of SLSNe roughly track an exponential disk profile, but exhibit an overabundance of sources with large offsets of $1.5-4$ times their host half-light radius. The SLSNe normalized offsets are systematically larger than those of long gamma-ray bursts (LGRBs), and even Type Ib/c and II SNe. Furthermore, we find that about 40\% of all SLSNe occur in the dimmest regions of their host galaxies (fractional flux of 0), in stark contrast to LGRBs and Type Ib/c and II SNe. We do not detect any significant trends in the locations of SLSNe as a function of redshift, or as a function of explosion and magnetar engine parameters inferred from modeling of their optical lights curves. The significant difference in SLSN locations compared to LGRBs (and normal core-collapse SNe) suggests that at least some of their progenitors follow a different evolutionary path. We speculate that SLSNe arise from massive runaway stars from disrupted binary systems, with velocities of $\sim 10^2$ km s$^{-1}$., Comment: 31 pages, 14 figures, 5 tables. Submitted to ApJ. Comments welcomed

Operationalizing AI fairness at LinkedIn's scale is challenging not only because there are multiple mutually incompatible definitions of fairness but also because determining what is fair depends on the specifics and context of the product where AI is deployed. Moreover, AI practitioners need clarity on what fairness expectations need to be addressed at the AI level. In this paper, we present the evolving AI fairness framework used at LinkedIn to address these three challenges. The framework disentangles AI fairness by separating out equal treatment and equitable product expectations. Rather than imposing a trade-off between these two commonly opposing interpretations of fairness, the framework provides clear guidelines for operationalizing equal AI treatment complemented with a product equity strategy. This paper focuses on the equal AI treatment component of LinkedIn's AI fairness framework, shares the principles that support it, and illustrates their application through a case study. We hope this paper will encourage other big tech companies to join us in sharing their approach to operationalizing AI fairness at scale, so that together we can keep advancing this constantly evolving field.

As AI-based decision systems proliferate, their successful operationalization requires balancing multiple desiderata: predictive performance, disparity across groups, safeguarding sensitive group attributes (e.g., race), and engineering cost. We present a holistic framework for evaluating and contextualizing fairness interventions with respect to the above desiderata. The two key points of practical consideration are \emph{where} (pre-, in-, post-processing) and \emph{how} (in what way the sensitive group data is used) the intervention is introduced. We demonstrate our framework with a case study on predictive parity. In it, we first propose a novel method for achieving predictive parity fairness without using group data at inference time via distibutionally robust optimization. Then, we showcase the effectiveness of these methods in a benchmarking study of close to 400 variations across two major model types (XGBoost vs. Neural Net), ten datasets, and over twenty unique methodologies. Methodological insights derived from our empirical study inform the practical design of ML workflow with fairness as a central concern. We find predictive parity is difficult to achieve without using group data, and despite requiring group data during model training (but not inference), distributionally robust methods we develop provide significant Pareto improvement. Moreover, a plain XGBoost model often Pareto-dominates neural networks with fairness interventions, highlighting the importance of model inductive bias.

The impossibility theorem of fairness is a foundational result in the algorithmic fairness literature. It states that outside of special cases, one cannot exactly and simultaneously satisfy all three common and intuitive definitions of fairness - demographic parity, equalized odds, and predictive rate parity. This result has driven most works to focus on solutions for one or two of the metrics. Rather than follow suit, in this paper we present a framework that pushes the limits of the impossibility theorem in order to satisfy all three metrics to the best extent possible. We develop an integer-programming based approach that can yield a certifiably optimal post-processing method for simultaneously satisfying multiple fairness criteria under small violations. We show experiments demonstrating that our post-processor can improve fairness across the different definitions simultaneously with minimal model performance reduction. We also discuss applications of our framework for model selection and fairness explainability, thereby attempting to answer the question: who's the fairest of them all?

With the upcoming Vera C.~Rubin Observatory Legacy Survey of Space and Time (LSST), it is expected that only $\sim 0.1\%$ of all transients will be classified spectroscopically. To conduct studies of rare transients, such as Type I superluminous supernovae (SLSNe), we must instead rely on photometric classification. In this vein, here we carry out a pilot study of SLSNe from the Pan-STARRS1 Medium-Deep Survey (PS1-MDS) classified photometrically with our SuperRAENN and Superphot algorithms. We first construct a sub-sample of the photometric sample using a list of simple selection metrics designed to minimize contamination and ensure sufficient data quality for modeling. We then fit the multi-band light curves with a magnetar spin-down model using the Modular Open-Source Fitter for Transients (MOSFiT). Comparing the magnetar engine and ejecta parameter distributions of the photometric sample to those of the PS1-MDS spectroscopic sample and a larger literature spectroscopic sample, we find that these samples are overall consistent, but that the photometric sample extends to slower spins and lower ejecta masses, which correspond to lower luminosity events, as expected for photometric selection. While our PS1-MDS photometric sample is still smaller than the overall SLSN spectroscopic sample, our methodology paves the way to an orders-of-magnitude increase in the SLSN sample in the LSST era through photometric selection and study., Comment: 13 pages, 6 figures, submitted to ApJ

Predictive parity (PP), also known as sufficiency, is a core definition of algorithmic fairness essentially stating that model outputs must have the same interpretation of expected outcomes regardless of group. Testing and satisfying PP is especially important in many settings where model scores are interpreted by humans or directly provide access to opportunity, such as healthcare or banking. Solutions for PP violations have primarily been studied through the lens of model calibration. However, we find that existing calibration-based tests and mitigation methods are designed for independent data, which is often not assumable in large-scale applications such as social media or medical testing. In this work, we address this issue by developing a statistically rigorous non-parametric regression based test for PP with dependent observations. We then apply our test to illustrate that PP testing can significantly vary under the two assumptions. Lastly, we provide a mitigation solution to provide a minimally-biased post-processing transformation function to achieve PP.