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When main-sequence stars expand into red giants, they are expected to engulf close-in planets. Until now, the absence of planets with short orbital periods around post-expansion, core-helium-burning red giants has been interpreted as evidence that short-period planets around Sun-like stars do not survive the giant expansion phase of their host stars. Here we present the discovery that the giant planet 8 Ursae Minoris b orbits a core-helium-burning red giant. At a distance of only 0.5 au from its host star, the planet would have been engulfed by its host star, which is predicted by standard single-star evolution to have previously expanded to a radius of 0.7 au. Given the brief lifetime of helium-burning giants, the nearly circular orbit of the planet is challenging to reconcile with scenarios in which the planet survives by having a distant orbit initially. Instead, the planet may have avoided engulfment through a stellar merger that either altered the evolution of the host star or produced 8 Ursae Minoris b as a second-generation planet. This system shows that core-helium-burning red giants can harbour close planets and provides evidence for the role of non-canonical stellar evolution in the extended survival of late-stage exoplanetary systems., Comment: Published in Nature on 28 June 2023. In press

Portfolio managers' orders trade off return and trading cost predictions. Return predictions rely on alpha models, whereas price impact models quantify trading costs. This paper studies what happens when trades are based on an incorrect price impact model, so that the portfolio either over- or under-trades its alpha signal. We derive tractable formulas for these misspecification costs and illustrate them on proprietary trading data. The misspecification costs are naturally asymmetric: underestimating impact concavity or impact decay shrinks profits, but overestimating concavity or impact decay can even turn profits into losses., Comment: 14 pages, 5 figures

The Galactic bulge and bar are critical to our understanding of the Milky Way. However, due to the lack of reliable stellar distances, the structure and kinematics of the bulge/bar beyond the Galactic center have remained largely unexplored. Here, we present a method to measure distances of luminous red giants using a period-amplitude-luminosity relation anchored to the Large Magellanic Cloud, with random uncertainties of 10-15% and systematic errors below 1-2%. We apply this method to data from the Optical Gravitational Lensing Experiment (OGLE) to measure distances to $190,302$ stars in the Galactic bulge and beyond out to 20 kpc. Using this sample we measure a distance to the Galactic center of $R_0$ = $8108\pm106_{\rm stat}\pm93_{\rm sys}$ pc, consistent with astrometric monitoring of stars orbiting Sgr A*. We cross-match our distance catalog with Gaia DR3 and use the subset of $39,566$ overlapping stars to provide the first constraints on the Milky Way's velocity field ($V_R,V_\phi,V_z$) beyond the Galactic center. We show that the $V_R$ quadrupole from the bar's near side is reflected with respect to the Galactic center, indicating that the bar is both bi-symmetric and aligned with the inner disk, and therefore dynamically settled along its full extent. We also find that the vertical height $V_Z$ map has no major structure in the region of the Galactic bulge, which is inconsistent with a current episode of bar buckling. Finally, we demonstrate with N-body simulations that distance uncertainty plays a major factor in the alignment of the major and kinematic axes of the bar and distribution of velocities, necessitating caution when interpreting results for distant stars., Comment: Accepted to the Astronomical Journal

The All-Sky Automated Survey for Supernovae (ASAS-SN) began observing in late-2011 and has been imaging the entire sky with nightly cadence since late 2017. A core goal of ASAS-SN is to release as much useful data as possible to the community. Working towards this goal, in 2017 the first ASAS-SN Sky Patrol was established as a tool for the community to obtain light curves from our data with no preselection of targets. Then, in 2020 we released static V-band photometry from 2013--2018 for 61 million sources. Here we describe the next generation ASAS-SN Sky Patrol, Version 2.0, which represents a major progression of this effort. Sky Patrol 2.0 provides continuously updated light curves for 111 million targets derived from numerous external catalogs of stars, galaxies, and solar system objects. We are generally able to serve photometry data within an hour of observation. Moreover, with a novel database architecture, the catalogs and light curves can be queried at unparalleled speed, returning thousands of light curves within seconds. Light curves can be accessed through a web interface (http://asas-sn.ifa.hawaii.edu/skypatrol/) or a Python client (https://asas-sn.ifa.hawaii.edu/documentation). The Python client can be used to retrieve up to 1 million light curves, generally limited only by bandwidth. This paper gives an updated overview of our survey, introduces the new Sky Patrol, and describes its system architecture. These results provide significant new capabilities to the community for pursuing multi-messenger and time-domain astronomy., Comment: Light curves can be accessed through a web interface http://asas-sn.ifa.hawaii.edu/skypatrol, or a Python client at http://asas-sn.ifa.hawaii.edu/documentation

We observed HD 19467 B with JWST's NIRCam in six filters spanning 2.5-4.6 $\mu m$ with the Long Wavelength Bar coronagraph. The brown dwarf HD 19467 B was initially identified through a long-period trend in the radial velocity of G3V star HD 19467. HD 19467 B was subsequently detected via coronagraphic imaging and spectroscopy, and characterized as a late-T type brown dwarf with approximate temperature $\sim1000$K. We observed HD 19467 B as a part of the NIRCam GTO science program, demonstrating the first use of the NIRCam Long Wavelength Bar coronagraphic mask. The object was detected in all 6 filters (contrast levels of $2\times10^{-4}$ to $2\times10^{-5}$) at a separation of 1.6 arcsec using Angular Differential Imaging (ADI) and Synthetic Reference Differential Imaging (SynRDI). Due to a guidestar failure during acquisition of a pre-selected reference star, no reference star data was available for post-processing. However, RDI was successfully applied using synthetic Point Spread Functions (PSFs) developed from contemporaneous maps of the telescope's optical configuration. Additional radial velocity data (from Keck/HIRES) are used to constrain the orbit of HD 19467 B. Photometric data from TESS are used to constrain the properties of the host star, particularly its age. NIRCam photometry, spectra and photometry from literature, and improved stellar parameters are used in conjunction with recent spectral and evolutionary substellar models to derive physical properties for HD 19467 B. Using an age of 9.4$\pm$0.9 Gyr inferred from spectroscopy, Gaia astrometry, and TESS asteroseismology, we obtain a model-derived mass of 62$\pm 1M_{J}$, which is consistent within 2-$\sigma$ with the dynamically derived mass of 81$^{+14}_{-12}M_{J}$., Comment: 21 pages, 19 figures. Accepted to AAS Journals

We studied 89 A- and F-type members of the Pleiades open cluster, including five escaped members. We measured projected rotational velocities (v sin i) for 49 stars and confirmed that stellar rotation causes a broadening of the main sequence in the color-magnitude diagram. Using time-series photometry from NASA's TESS Mission (plus one star observed by Kepler/K2), we detected delta Scuti pulsations in 36 stars. The fraction of Pleiades stars in the middle of the instability strip that pulsate is unusually high (over 80%), and their range of effective temperatures agrees well with theoretical models. On the other hand, the characteristics of the pulsation spectra are varied and do not correlate with stellar temperature, calling into question the existence of a useful nu_max relation for delta Scutis, at least for young stars. By including delta Scuti stars observed in the Kepler field, we show that the instability strip is shifted to the red with increasing distance by interstellar reddening. Overall, this work demonstrates the power of combining observations with Gaia and TESS for studying pulsating stars in open clusters., Comment: accepted by ApJ Letters (minor revisions during proof stage)

We describe the discovery of a solar neighborhood (d=468 pc) binary system with a main-sequence sunlike star and a massive non-interacting black hole candidate. The spectral energy distribution (SED) of the visible star is described by a single stellar model. We derive stellar parameters from a high signal-to-noise Magellan/MIKE spectrum, classifying the star as a main-sequence star with $T_{\rm eff} = 5972 \rm K$, $\log{g} = 4.54$, and $M = 0.91$ \msun. The spectrum shows no indication of a second luminous component. To determine the spectroscopic orbit of the binary, we measured radial velocities of this system with the Automated Planet Finder, Magellan, and Keck over four months. We show that the velocity data are consistent with the \textit{Gaia} astrometric orbit and provide independent evidence for a massive dark companion. From a combined fit of our spectroscopic data and the astrometry, we derive a companion mass of $11.39^{+1.51}_{-1.31}$\msun. We conclude that this binary system harbors a massive black hole on an eccentric $(e =0.46 \pm 0.02)$, $185.4 \pm 0.1$ d orbit. These conclusions are independent of \cite{ElBadry2022Disc}, who recently reported the discovery of the same system. A joint fit to all available data (including \cite{ElBadry2022Disc}'s) yields a comparable period solution, but a lower companion mass of $9.32^{+0.22}_{-0.21} M_{\odot}$. Radial velocity fits to all available data produce a unimodal solution for the period that is not possible with either data set alone. The combination of both data sets yields the most accurate orbit currently available., Comment: 30 pages, 17 figures, accepted to AAS journals (various changes in response to referee comments)

In asteroseismology, the surface effect refers to a disparity between the observed and the modelled frequencies in stars with solar-like oscillations. It originates from improper modelling of the surface layers. Correcting the surface effect usually requires using functions with free parameters, which are conventionally fitted to the observed frequencies. On the basis that the correction should vary smoothly across the H--R diagram, we parameterize it as a simple function of surface gravity, effective temperature, and metallicity. We determine this function by fitting a wide range of stars. The absolute amount of the surface correction decreases with luminosity, but the ratio between it and $\nu_{\rm max}$ increases, suggesting the surface effect is more important for red giants than dwarfs. Applying the prescription can eliminate unrealistic surface correction, which improves parameter estimations with stellar modelling. Using two open clusters, we found a reduction of scatter in the model-derived ages for each star in the same cluster. As an important application, we provide a new revision for the $\Delta\nu$ scaling relation that, for the first time, accounts for the surface correction. The values of the correction factor, $f_{\Delta\nu}$, are up to 2\% smaller than those determined without the surface effect considered, suggesting decreases of up to 4\% in radii and up to 8\% in masses when using the asteroseismic scaling relations. This revision brings the asteroseismic properties into an agreement with those determined from eclipsing binaries. The new correction factor and the stellar models with the corrected frequencies are available at {https://www.github.com/parallelpro/surface}., Comment: 13 pages, 11 figures. Accepted by MNRAS

This paper is concerned with solution algorithms for general convex vector optimization problems (CVOPs). So far, solution concepts and approximation algorithms for solving CVOPs exist only for bounded problems [Ararat et al. 2022, Doerfler et al. 2021, Loehne et al. 2014]. They provide a polyhedral inner and outer approximation of the upper image that have a Hausdorff distance of at most $\varepsilon$. However, it is well known (see [Ulus, 2018]), that for some unbounded problems such polyhedral approximations do not exist. In this paper, we will propose a generalized solution concept, called an $(\varepsilon,\delta)$--solution, that allows also to consider unbounded CVOPs. It is based on additionally bounding the recession cones of the inner and outer polyhedral approximations of the upper image in a meaningful way. An algorithm is proposed that computes such $\delta$--outer and $\delta$--inner approximations of the recession cone of the upper image. In combination with the results of [Loehne et al. 2014] this provides a primal and a dual algorithm that allow to compute $(\varepsilon,\delta)$--solutions of (potentially unbounded) CVOPs. Numerical examples are provided.

We have used NASA's TESS mission to study catalogued delta Scuti stars. We examined TESS light curves for 434 stars, including many for which few previous observations exist. We found that 62 are not delta Scuti pulsators, with most instead showing variability from binarity. For the 372 delta Scuti stars, we provide a catalogue of the period and amplitude of the dominant pulsation mode. Using Gaia DR3 parallaxes, we place the stars in the period-luminosity diagram and confirm previous findings that most stars lie on a ridge that corresponds to pulsation in the fundamental radial mode, and that many others fall on a second ridge that is a factor two shorter in period. This second ridge is seen more clearly than before, thanks to the revised periods and distances. We demonstrate the value of the period-luminosity diagram in distinguishing delta Scuti stars from short-period RR Lyrae stars, and we find several new examples of high-frequency delta Scuti stars with regular sequences of overtone modes, including XX Pyx and 29 Cyg. Finally, we revisit the sample of delta Scuti stars observed by Kepler and show that they follow a tight period-density relation, with a pulsation constant for the fundamental mode of Q = 0.0315 d., Comment: accepted by MNRAS

A star expands to become a red giant when it has fused all the hydrogen in its core into helium. If the star is in a binary system, its envelope can overflow onto its companion or be ejected into space, leaving a hot core and potentially forming a subdwarf-B star. However, most red giants that have partially transferred envelopes in this way remain cool on the surface and are almost indistinguishable from those that have not. Among $\sim$7000 helium-burning red giants observed by NASA's Kepler mission, we use asteroseismology to identify two classes of stars that must have undergone dramatic mass loss, presumably due to stripping in binary interactions. The first class comprises about 7 underluminous stars with smaller helium-burning cores than their single-star counterparts. Theoretical models show that these small cores imply the stars had much larger masses when ascending the red giant branch. The second class consists of 32 red giants with masses down to 0.5 M$_\odot$, whose implied ages would exceed the age of the universe had no mass loss occurred. The numbers are consistent with binary statistics, and our results open up new possibilities to study the evolution of post-mass-transfer binary systems., Comment: a Letter to Nature Astronomy

We perform mode identification for five $\delta$ Scuti stars in the Pleiades star cluster, using custom light curves from K2 photometry. By creating \'echelle diagrams, we identify radial and dipole mode ridges, comprising a total of 28 radial and 16 dipole modes across the five stars. We also suggest possible identities for those modes that lie offset from the radial and dipole ridges. We calculate non-rotating stellar pulsation models to verify our mode identifications, finding good agreement within the age and metallicity constraints of the cluster. We also find that for all stars, the least dense models are preferred, reflecting the lower density of these oblate, rotating stars. Three of the five stars show rotationally-split multiplets. We conclude that the sample shows promise for asteroseismic rotation rates, masses, and ages with rotating models in the future. Our preliminary modelling also indicates some sensitivity to the helium abundance., Comment: Submitted to MNRAS

A polyhedral convex set optimization problem is given by a set-valued objective mapping from the $n$-dimensional to the $q$-dimensional Euclidean space whose graph is a convex polyhedron. This problem can be seen as the most elementary subclass of set optimization problems, comparable to linear programming in the framework of optimization with scalar-valued objective function. Polyhedral convex set optimization generalizes both scalar and multi-objective (or vector) linear programming. In contrast to scalar linear programming but likewise to multi-objective linear programming, unbounded problems can indeed have minimizers and provide a rich class of problem instances. In this paper we extend the concept of finite infimizers from multi-objective linear programming to not necessarily bounded polyhedral convex set optimization problems. We show that finite infimizers can be obtained from finite infimizers of a reformulation of the polyhedral convex set optimization problem into a vector linear program. We also discuss two natural extensions of solution concepts based on the complete lattice approach. Surprisingly, the attempt to generalize the solution procedure for bounded polyhedral convex set optimization problems introduced in [A. L\"ohne and C. Schrage. An algorithm to solve polyhedral convex set optimization problems. {\em Optimization} 62(1):131--141, 2013.] to the case of not necessarily bounded problems uncovers some problems, which will be discussed., Comment: minor chances, to appear in: Minimax Theory and its Applications (https://www.heldermann.de/MTA/mtacon.htm), this version is identical to the paper version

The breakthrough in Deep Learning neural networks has transformed the use of AI and machine learning technologies for the analysis of very large experimental datasets. These datasets are typically generated by large-scale experimental facilities at national laboratories. In the context of science, scientific machine learning focuses on training machines to identify patterns, trends, and anomalies to extract meaningful scientific insights from such datasets. With a new generation of experimental facilities, the rate of data generation and the scale of data volumes will increasingly require the use of more automated data analysis. At present, identifying the most appropriate machine learning algorithm for the analysis of any given scientific dataset is still a challenge for scientists. This is due to many different machine learning frameworks, computer architectures, and machine learning models. Historically, for modelling and simulation on HPC systems such problems have been addressed through benchmarking computer applications, algorithms, and architectures. Extending such a benchmarking approach and identifying metrics for the application of machine learning methods to scientific datasets is a new challenge for both scientists and computer scientists. In this paper, we describe our approach to the development of scientific machine learning benchmarks and review other approaches to benchmarking scientific machine learning.

In this paper we present a catalog of 4584 eclipsing binaries observed during the first two years (26 sectors) of the TESS survey. We discuss selection criteria for eclipsing binary candidates, detection of hither-to unknown eclipsing systems, determination of the ephemerides, the validation and triage process, and the derivation of heuristic estimates for the ephemerides. Instead of keeping to the widely used discrete classes, we propose a binary star morphology classification based on a dimensionality reduction algorithm. Finally, we present statistical properties of the sample, we qualitatively estimate completeness, and discuss the results. The work presented here is organized and performed within the TESS Eclipsing Binary Working Group, an open group of professional and citizen scientists; we conclude by describing ongoing work and future goals for the group. The catalog is available from http://tessEBs.villanova.edu and from MAST., Comment: 32 pages, 21 figures, accepted to ApJ Supplement Series; comments welcome

Knowing the redshift of galaxies is one of the first requirements of many cosmological experiments, and as it's impossible to perform spectroscopy for every galaxy being observed, photometric redshift (photo-z) estimations are still of particular interest. Here, we investigate different deep learning methods for obtaining photo-z estimates directly from images, comparing these with traditional machine learning algorithms which make use of magnitudes retrieved through photometry. As well as testing a convolutional neural network (CNN) and inception-module CNN, we introduce a novel mixed-input model which allows for both images and magnitude data to be used in the same model as a way of further improving the estimated redshifts. We also perform benchmarking as a way of demonstrating the performance and scalability of the different algorithms. The data used in the study comes entirely from the Sloan Digital Sky Survey (SDSS) from which 1 million galaxies were used, each having 5-filter (ugriz) images with complete photometry and a spectroscopic redshift which was taken as the ground truth. The mixed-input inception CNN achieved a mean squared error (MSE)=0.009, which was a significant improvement (30%) over the traditional Random Forest (RF), and the model performed even better at lower redshifts achieving a MSE=0.0007 (a 50% improvement over the RF) in the range of z<0.3. This method could be hugely beneficial to upcoming surveys such as the Vera C. Rubin Observatory's Legacy Survey of Space and Time (LSST) which will require vast numbers of photo-z estimates produced as quickly and accurately as possible., Comment: 13 pages, 14 figures, submitted to MNRAS

Data from the Transiting Exoplanet Survey Satellite (TESS) has produced of order one million light curves at cadences of 120 s and especially 1800 s for every ~27-day observing sector during its two-year nominal mission. These data constitute a treasure trove for the study of stellar variability and exoplanets. However, to fully utilize the data in such studies a proper removal of systematic noise sources must be performed before any analysis. The TESS Data for Asteroseismology (T'DA) group is tasked with providing analysis-ready data for the TESS Asteroseismic Science Consortium, which covers the full spectrum of stellar variability types, including stellar oscillations and pulsations, spanning a wide range of variability timescales and amplitudes. We present here the two current implementations for co-trending of raw photometric light curves from TESS, which cover different regimes of variability to serve the entire seismic community. We find performance in terms of commonly used noise statistics to meet expectations and to be applicable to a wide range of different intrinsic variability types. Further, we find that the correction of light curves from a full sector of data can be completed well within a few days, meaning that when running in steady-state our routines are able to process one sector before data from the next arrives. Our pipeline is open-source and all processed data will be made available on TASOC and MAST., Comment: 27 pages, 15 figures, accepted for publication in The Astrophysical Journal Supplement Series

We search for transits around all known pulsating {\delta} Sct variables (6500 K < Teff < 10 000 K) in the long-cadence Kepler data after subtracting the pulsation signal through an automated routine. To achieve this, we devise a simple and computationally inexpensive method for distinguishing between low-frequency pulsations and transits in light curves. We find 3 new candidate transit events that were previously hidden behind the pulsations, but caution that they are likely to be false positive events. We also examined the Kepler Objects of Interest catalog and identify 13 additional host stars which show {\delta} Sct pulsations. For each star in our sample, we use the non-detection of pulsation timing variations for a planet that is known to be transiting a {\delta} Sct variable to obtain both an upper limit on the mass of the planet and the expected radial velocity semi-amplitude of the host star. Simple injection tests of our pipeline imply 100% recovery for planets of 0.5 RJup or greater. Extrapolating our number of Kepler {\delta} Sct stars, we expect 12 detectable planets above 0.5 RJup in TESS. Our sample contains some of the hottest known transiting planets around evolved stars, and is the first complete sample of transits around {\delta} Sct variables. We make available our code and pulsation-subtracted light curves to facilitate further analysis., Comment: 18 pages, accepted to AAS journals

We study the $\delta$ Scuti -- red giant binary KIC9773821, the first double-pulsator binary of its kind. It was observed by \textit{Kepler} during its four-year mission. Our aims are to ascertain whether the system is bound, rather than a chance alignment, and to identify the evolutionary state of the red giant via asteroseismology. An extension of these aims is to determine a dynamical mass and an age prior for a $\delta$ Sct star, which may permit mode identification via further asteroseismic modelling. We determine spectroscopic parameters and radial velocities (RVs) for the red giant component using HERMES@Mercator spectroscopy. Light arrival-time delays from the $\delta$ Sct pulsations are used with the red-giant RVs to determine that the system is bound and to infer its orbital parameters, including the binary mass ratio. We use asteroseismology to model the individual frequencies of the red giant to give a mass of $2.10^{+0.20}_{-0.10}$ M$_{\odot}$ and an age of $1.08^{+0.06}_{-0.24}$ Gyr. We find that it is a helium-burning secondary clump star, confirm that it follows the standard $\nu_{\rm max}$ scaling relation, and confirm its observed period spacings match their theoretical counterparts in the modelling code MESA. Our results also constrain the mass and age of the $\delta$ Sct star. We leverage these constraints to construct $\delta$ Sct models in a reduced parameter space and identify four of its five pulsation modes., Comment: Accepted for publication in MNRAS

We present the results of a systematic search for new rapidly oscillating Ap (roAp) stars using the 2-min cadence data collected by the Transiting Exoplanet Survey Satellite (TESS) during its Cycle 1 observations. We identify 12 new roAp stars. Amongst these stars we discover the roAp star with the longest pulsation period, another with the shortest rotation period, and six with multiperiodic variability. In addition to these new roAp stars, we present an analysis of 44 known roAp stars observed by TESS during Cycle 1, providing the first high-precision and homogeneous sample of a significant fraction of the known roAp stars. The TESS observations have shown that almost 60 per cent (33) of our sample of stars are multiperiodic, providing excellent cases to test models of roAp pulsations, and from which the most rewarding asteroseismic results can be gleaned. We report four cases of the occurrence of rotationally split frequency multiplets that imply different mode geometries for the same degree modes in the same star. This provides a conundrum in applying the oblique pulsator model to the roAp stars. Finally, we report the discovery of non-linear mode interactions in $\alpha$ Cir (TIC 402546736, HD 128898) around the harmonic of the principal mode -- this is only the second case of such a phenomenon., Comment: Accepted for publication in MNRAS. 38 Pages, 50 Figures, 1 Table, 2 Appendices

"exoplanet" is a toolkit for probabilistic modeling of astronomical time series data, with a focus on observations of exoplanets, using PyMC3 (Salvatier et al., 2016). PyMC3 is a flexible and high-performance model-building language and inference engine that scales well to problems with a large number of parameters. "exoplanet" extends PyMC3's modeling language to support many of the custom functions and probability distributions required when fitting exoplanet datasets or other astronomical time series. While it has been used for other applications, such as the study of stellar variability, the primary purpose of "exoplanet" is the characterization of exoplanets or multiple star systems using time-series photometry, astrometry, and/or radial velocity. In particular, the typical use case would be to use one or more of these datasets to place constraints on the physical and orbital parameters of the system, such as planet mass or orbital period, while simultaneously taking into account the effects of stellar variability., Comment: Published in the Journal of Open Source Software. Comments (still) welcome. Software available at https://docs.exoplanet.codes

Obtaining accurate photometric redshift estimations is an important aspect of cosmology, remaining a prerequisite of many analyses. In creating novel methods to produce redshift estimations, there has been a shift towards using machine learning techniques. However, there has not been as much of a focus on how well different machine learning methods scale or perform with the ever-increasing amounts of data being produced. Here, we introduce a benchmark designed to analyse the performance and scalability of different supervised machine learning methods for photometric redshift estimation. Making use of the Sloan Digital Sky Survey (SDSS - DR12) dataset, we analysed a variety of the most used machine learning algorithms. By scaling the number of galaxies used to train and test the algorithms up to one million, we obtained several metrics demonstrating the algorithms' performance and scalability for this task. Furthermore, by introducing a new optimisation method, time-considered optimisation, we were able to demonstrate how a small concession of error can allow for a great improvement in efficiency. From the algorithms tested we found that the Random Forest performed best in terms of error with a mean squared error, MSE = 0.0042; however, as other algorithms such as Boosted Decision Trees and k-Nearest Neighbours performed incredibly similarly, we used our benchmarks to demonstrate how different algorithms could be superior in different scenarios. We believe benchmarks such as this will become even more vital with upcoming surveys, such as LSST, which will capture billions of galaxies requiring photometric redshifts., Comment: 9 pages, 6 figures, submitted to MNRAS