Adler Planetarium

Context. Core-collapse supernovae (CCSNe) may have contributed a significant amount of dust in the early Universe. Freshly formed coolant molecules (e.g., CO) and warm dust can be found in CCSNe as early as ∼100 d after the SN explosion, allowing the study of their evolution with time series observations. Aims. Through study of the Type II SN 2023ixf, we aim to investigate the temporal evolution of the temperature, velocity, and mass of CO and compare them with other CCSNe, exploring their implications for the dust formation in CCSNe. From observations of velocity profiles of lines of other species (e.g., H and He), we also aim to characterize and understand the interaction of the SN ejecta with preexisting circumstellar material (CSM). Methods. We present a time series of 16 near-infrared spectra of SN 2023ixf from 9 to 307 d, taken with multiple instruments: Gemini/GNIRS, Keck/NIRES, IRTF/SpeX, and MMT/MMIRS. Results. The early ( t ≲ 70 d) spectra indicate interaction between the expanding ejecta and nearby CSM. At t ≲ 20 d, intermediate-width line profiles corresponding to the ejecta-wind interaction are superposed on evolving broad P Cygni profiles. We find intermediate-width and narrow lines in the spectra until t ≲ 70 d, which suggest continued CSM interaction. We also observe and discuss high-velocity absorption features in H α and H β line profiles formed by CSM interaction. The spectra contain CO first overtone emission between 199 and 307 d after the explosion. We modeled the CO emission and found the CO to have a higher velocity (3000–3500 km s −1 ) than that in Type II-pec SN 1987A (1800–2000 km s −1 ) during similar phases ( t = 199 − 307 d) and a comparable CO temperature to SN 1987A. A flattened continuum at wavelengths greater than 1.5 μm accompanies the CO emission, suggesting that the warm dust is likely formed in the ejecta. The warm dust masses are estimated to be on the order of ∼10 −5 M ⊙ .

Abstract Whether considering rare astrophysical events on cosmological scales or unresolved stellar populations, accurate models must account for the integrated contribution from the entire history of star formation upon which that population is built. Here, we describe the second version of POSYDON, an open-source binary population synthesis code based on extensive grids of detailed binary evolution models computed using the MESA code, which follows both stars’ structures as a binary system evolves through its complete evolution from the zero-age main sequence, through multiple phases of mass transfer and supernovae, to their death as compact objects. To generate synthetic binary populations, POSYDON uses advanced methods to interpolate between our large, densely spaced grids of simulated binaries. In our updated version of POSYDON, we account for the evolution of stellar binaries across a cosmological range of metallicities, extending from 10−4 to 2 Z ⊙, including grids specifically focused on the Small and Large Magellanic Clouds (0.2 and 0.45 Z ⊙). In addition to describing our model grids and detailing our methodology, we outline several improvements to POSYDON. These include the incorporation of single stars in stellar populations, a treatment for stellar mergers, and a careful modeling of “reverse-mass transferring” binaries in which a once-accreting star later becomes a donor star. Our simulations are focused on binaries with at least one high-mass component, such as those that host neutron stars and black holes, and we provide postprocessing methods to account for the cosmological evolution of metallicity and star formation as well as rate calculations for transient events.

Abstract We present multiband observations and analysis of EP240801a, a low-energy, extremely soft gamma-ray burst (GRB) discovered on 2024 August 1 by the Einstein Probe (EP) satellite with a weak contemporaneous signal also detected by the Fermi Gamma-ray Burst Monitor (GBM). Optical spectroscopy of the afterglow, obtained by Gran Telescopio Canarias and Keck, identified the redshift of z = 1.6734. EP240801a exhibits a burst duration of 148 s in X-rays and 22.3 s in gamma rays, with X-rays leading by 80.61 s. Spectral lag analysis indicates that the gamma-ray signal arrived 8.3 s earlier than the X-rays. Joint spectral fitting of EP Wide-field X-ray Telescope and Fermi/GBM data yields an isotropic energy E γ , iso = ( 5.5 7 − 0.50 + 0.54 ) × 1 0 51 erg , a peak energy E peak = 14.9 0 − 4.71 + 7.08 keV , and a fluence ratio S(25–50 keV)/S ( 50 – 100 keV ) = 1.6 7 − 0.46 + 0.74 , classifying EP240801a as an X-ray flash (XRF). The host-galaxy continuum spectrum, inferred using Prospector, was used to correct its contribution for the observed outburst optical data. Unusual early R-band behavior and EP Follow-up X-ray Telescope observations suggest multiple components in the afterglow. Three models are considered: a two-component jet model, a forward-reverse shock model, and a forward shock model with energy injection. All three provide reasonable explanations. The two-component jet model and the energy injection model imply a relatively small initial energy and velocity of the jet in the line of sight, while the forward-reverse shock model remains typical. Under the two-component jet model, EP240801a may resemble GRB 221009A (BOAT) if the bright narrow beam is viewed on-axis. Therefore, EP240801a can be interpreted as an off-beam (narrow) jet or an intrinsically weak GRB jet. Our findings provide crucial clues for uncovering the origin of XRFs.

Abstract We present the results of a search for nonlinear gravitational-wave (GW) memory in the NANOGrav 15 yr data set. We find no significant evidence for memory signals in the data set, with a maximum Bayes factor of 3.1 in favor of a model including memory. We therefore place upper limits on the strain of potential GW memory events as a function of sky location and observing epoch. We find upper limits that are not always more constraining than previous NANOGrav results. We show that it is likely due to the increase in common red noise between the 12.5 and 15 yr NANOGrav data sets.

Abstract Pulsar timing array observations have found evidence for an isotropic gravitational-wave background with the Hellings–Downs angular correlations between pulsar pairs. This interpretation hinges on the measured shape of the angular correlations, which is predominantly quadrupolar under general relativity. Here we explore a more flexible parameterization: we expand the angular correlations into a sum of Legendre polynomials and use a Bayesian analysis to constrain their coefficients with the 15 yr pulsar timing data set collected by the North American Nanohertz Observatory for Gravitational Waves (NANOGrav). When including Legendre polynomials with multipoles ℓ ≥ 2, we only find a significant signal in the quadrupole with an amplitude consistent with general relativity and nonzero at the ∼95% confidence level and a Bayes factor of 200. When we include multipoles ℓ ≤ 1, the Bayes factor evidence for quadrupole correlations decreases by more than an order of magnitude due to evidence for a monopolar signal at approximately 4 nHz, which has also been noted in previous analyses of the NANOGrav 15 yr data. Further work needs to be done in order to better characterize the properties of this monopolar signal and its effect on the evidence for quadrupolar angular correlations.

Detecting imprints of orbital eccentricity in gravitational-wave (GW) signals promises to shed light on the formation mechanisms of binary black holes. To constrain the formation mechanisms, distributions of eccentricity derived from numerical simulations of astrophysical formation channels are compared to the estimates of eccentricity inferred from GW signals. We report that the definition of eccentricity typically used in astrophysical simulations is inconsistent with the one used while modeling GW signals, with the differences mainly arising due to the choice of reference frequency used in both cases. We also posit a prescription for calculating eccentricity from astrophysical simulations, by evolving ordinary differential equations obtained from post-Newtonian theory and using the dominant (ℓ = m = 2) mode’s frequency as the reference frequency; this ensures consistency in the definitions. On comparing the existing eccentricities of the binaries present in the Cluster Monte Carlo catalog of globular cluster simulations with the eccentricities calculated using the prescription presented here, we find a significant discrepancy at e ≳ 0.2; this discrepancy becomes worse with increasing eccentricity. We note the implications this discrepancy has for existing studies and recommend that care be taken when comparing data-driven constraints on eccentricity to expectations from astrophysical formation channels.

The millisecond pulsar J1713+0747 underwent a sudden and significant pulse shape change between 2021 April 16 and 17 (MJDs 59320 and 59321). Subsequently, the pulse shape gradually recovered over the course of several months. We report the results of continued multifrequency radio observations of the pulsar made using the Canadian Hydrogen Intensity Mapping Experiment and the 100 m Green Bank Telescope in a 3 yr period encompassing the shape change event, between 2020 February and 2023 February. As of 2023 February, the pulse shape had returned to a state similar to that seen before the event, but with measurable changes remaining. The amplitude of the shape change and the accompanying time-of-arrival residuals display a strong nonmonotonic dependence on radio frequency, demonstrating that the event is neither a glitch (the effects of which should be independent of radio frequency, ν) nor a change in dispersion measure alone (which would produce a delay proportional to ν −2). However, it does bear some resemblance to the two previous “chromatic timing events” observed in J1713+0747, as well as to a similar event observed in PSR J1643−1224 in 2015.

We present the results of a Bayesian search for gravitational wave (GW) memory in the NANOGrav 12.5 yr data set. We find no convincing evidence for any gravitational wave memory signals in this data set. We find a Bayes factor of 2.8 in favor of a model that includes a memory signal and common spatially uncorrelated red noise (CURN) compared to a model including only a CURN. However, further investigation shows that a disproportionate amount of support for the memory signal comes from three dubious pulsars. Using a more flexible red-noise model in these pulsars reduces the Bayes factor to 1.3. Having found no compelling evidence, we go on to place upper limits on the strain amplitude of GW memory events as a function of sky location and event epoch. These upper limits are computed using a signal model that assumes the existence of a common, spatially uncorrelated red noise in addition to a GW memory signal. The median strain upper limit as a function of sky position is approximately 3.3 × 10−14. We also find that there are some differences in the upper limits as a function of sky position centered around PSR J0613−0200. This suggests that this pulsar has some excess noise that can be confounded with GW memory. Finally, the upper limits as a function of burst epoch continue to improve at later epochs. This improvement is attributable to the continued growth of the pulsar timing array.

The Gravity Spy project aims to uncover the origins of glitches, transient bursts of noise that hamper analysis of gravitational-wave data. By using both the work of citizen-science volunteers and machine learning algorithms, the Gravity Spy project enables reliable classification of glitches. Citizen science and machine learning are intrinsically coupled within the Gravity Spy framework, with machine learning classifications providing a rapid first-pass classification of the dataset and enabling tiered volunteer training, and volunteer-based classifications verifying the machine classifications, bolstering the machine learning training set and identifying new morphological classes of glitches. These classifications are now routinely used in studies characterizing the performance of the LIGO gravitational-wave detectors. Providing the volunteers with a training framework that teaches them to classify a wide range of glitches, as well as additional tools to aid their investigations of interesting glitches, empowers them to make discoveries of new classes of glitches. This demonstrates that, when giving suitable support, volunteers can go beyond simple classification tasks to identify new features in data at a level comparable to domain experts. The Gravity Spy project is now providing volunteers with more complicated data that includes auxiliary monitors of the detector to identify the root cause of glitches.