Farnik Nikakhtar

The linear point s _LP , a purely geometric feature in the monopole of the two-point correlation function, has been proposed as an alternative standard ruler that, unlike the peak in the correlation function, is more robust to late-time non-linear effects at the percent level. In light of improved simulations and high-quality data, we revisit this claimed robustness and test whether it persists at the sub-percent precision demanded by current surveys, using the linear point as an alternative to the template-based fitting approaches employed in standard BAO analyses. We present the first linear point measurements on galaxy samples from the first and second data releases (DR1 and DR2) of the Dark Energy Spectroscopic Instrument (DESI). We convert the linear point to a dimensionless parameter α _iso,LP , defined as the ratio of the fiducial linear point to the observed value, analogous to the isotropic BAO scaling parameter α _iso,BAO obtained from template-based BAO fits. Using the second generation of AbacusSummit mock catalogs for DESI DR1 ( Abacus - 2 ), we find that post-reconstruction linear point measurements are more precise than pre-reconstruction, with 15–60% smaller uncertainties. We compare the mean α _iso,LP across the 25 Abacus - 2 mocks for each tracer to the mean α _iso,BAO from the DESI template-based fits, and find a systematic offset which we attribute to the smearing of the correlation function, and thereby the linear point, in the non-linear regime. This shift is small relative to the method's original percent-level robustness, but becomes significant at the sub-percent level. We propose a sample-dependent correction to α _iso,LP , as a function of the isotropic damping parameter Σ _iso , that mitigates this offset; the resulting α _iso,LP ^corrected values show excellent agreement with α _iso,BAO in both mocks and data, particularly post-reconstruction. This agreement comes at the price of the model-independence that motivated the linear point in the first place, since both reconstruction and the proposed correction are cosmology-dependent. We conclude that, at present precision, the linear point is best regarded as a complementary geometric cross-check to BAO analyses rather than a strict alternative to template-based fitting approaches — one that grows more valuable as DESI hints at time-evolving dark energy, where independent geometric probes of the expansion history help test its robustness.

The active galactic nuclei (AGNs) glossary is vast and complex. Depending on the selection method, observing wavelength, and brightness, AGNs are assigned distinct labels, yet the relationship between different selection methods and the diversity of time-domain behavior within and across classes remains difficult to characterize in a unified framework. Changing-look AGNs (CLAGNs), which transition between classifications over time, further complicate this picture. In this work, we learn a data-driven, low-dimensional representation of multiwavelength photometric light curves of AGNs, in which the structure of the projected manifold correlates with AGN class and independent spectroscopic properties. Using the NASA Fornax Science Platform, we assemble light curves from Zwicky Transient Facility, Pan-STARRS, Gaia, and Wide-field Infrared Survey Explorer/NEOWISE for two samples: (1) a heterogeneous set of ∼2000 AGNs spanning z ≲ 1, including Sloan Digital Sky Survey quasars, variability-selected sources, and CLAGNs; and (2) a homogeneous sample of ∼65,000 narrow-line AGNs at z ≈ 0.1 with well-characterized optical emission-line measurements. Without using class labels during training, the learned manifolds organize variability-selected AGNs into coherent regions of the low-dimensional space, distinguish between turn-on and turn-off CLAGNs, and place tidal disruption events in distinct regions. Manifold coordinates correlate with key spectroscopic and host-galaxy properties—including stellar mass, [O III ] luminosity, and D _n (4000)—demonstrating that heterogeneous multiband variability can be combined in a purely data-driven manner to recover correlations with independent physical diagnostics, without requiring explicit physical modeling. These results show that manifold learning offers a practical, assumption-light approach for integrating time-domain surveys and prioritizing spectroscopic follow-up.

The baryon acoustic oscillations (BAO) feature in the two-point correlation function (TPCF) of discrete tracers such as galaxies is an accurate standard ruler. The covariance matrix of the TPCF plays an important role in determining how the precision of this ruler depends on the number density and clustering strength of the tracers, as well as the survey volume. An eigen-decomposition of this matrix provides an objective way to separate the contributions of cosmic variance from those of shot-noise to the statistical uncertainties. For the signal-to-noise levels that are expected in ongoing and next-generation surveys, the cosmic variance eigen-modes dominate. These modes are smooth functions of scale, meaning that they are insensitive to the modest changes in binning that are allowed if one wishes to resolve the BAO feature in the TPCF; they provide a good description of the correlated residuals which result from fitting smooth functional forms to the measured TPCF; they motivate a simple but accurate approximation for the uncertainty on the linear point (LP) estimate of the BAO distance scale. This approximation allows one to quantify the precision of the BAO distance scale estimate without having to generate a large ensemble of mock catalogs and explains why: the uncertainty on the LP does not depend on the functional form fitted to the TPCF or the binning used; the LP is more constraining than the peak or dip scales in the TPCF; the evolved TPCF is less constraining than the initial one, so that reconstruction schemes can yield significant gains in precision.

We find ourselves on the brink of an exciting era in observational astrophysics, driven by groundbreaking facilities like JWST, Euclid, Rubin, Roman, SKA, or ELT. Simultaneously, computational astrophysics has shown significant strides, yielding highly realistic galaxy formation simulations, thanks to both hardware and software enhancements. Bridging the gap between simulations and observations has become paramount for meaningful comparisons.

The location of the baryon acoustic oscillation (BAO) feature in the two-point correlation function (2PCF) of matter produces a standard ruler that is useful for the measurement of the expansion history of the Universe. Inspired by the possibility of reconstructing the positions of protohalos in the initial density field with a novel method rooted in optimal transport theory, we revisit the BAO signal in the protohalo correlation function. Our work examines the performance of a template 2PCF built on a tracer bias relation that includes scale dependence -- a term that can be motivated by peaks theory or a general bias expansion. Working in protohalos, halos, and the linear combination of the protohalo and matter fields that is motivated by the continuity equation, we demonstrate that this model accurately captures the shape of the BAO feature and improves the precision of the BAO scale measurement relative to a model that does not include scale-dependent bias by 47% in protohalos, 15% in halos, and 14% in the linear combination of the protohalo and matter fields. Allowing for scale dependence does not appear to introduce any shift in the BAO feature. The precision of the BAO distance scale estimate is highest with the linear combination of the protohalo and matter fields, which offers a factor of 3.5 improvement over Eulerian-space measurements and a factor of 4-8 improvement over the estimate made with protohalos alone.

We demonstrate the effectiveness of one of the many multi-tracer analyses enabled by Optimal Transport (OT) reconstruction. Leveraging a semi-discrete OT algorithm, we determine the displacements between initial and observed positions of biased tracers and the remaining matter field. With only redshift-space distorted final positions of biased tracers and a simple premise for the remaining mass distribution as input, OT solves the displacement field. This extracted field, assuming asymptotically uniform density and a gradient flow displacement, enables reconstruction of the initial overdensity fluctuation field. We show that the divergence of the OT displacement field is a good proxy of the linear density field, even though the method never assumes the linear theory growth. Additionally, this divergence field can be combined with the reconstructed protohalos to provide a higher signal-to-noise measurement of the BAO standard ruler than was possible with either measurement individually.