Shedding Light on Low Surface Brightness Galaxies in Dark Energy Survey with Transformers

Low surface brightness galaxies (LSBGs) which are defined as galaxies that are fainter than the night sky, play a crucial role in understanding galaxy evolution and cosmological models. Upcoming large-scale surveys like Rubin Observatory Legacy Survey of Space and Time (LSST) and Euclid are expected to observe billions of astronomical objects. In this context, using semi-automatic methods to identify LSBGs would be a highly challenging and time-consuming process and demand automated or machine learning-based methods to overcome this challenge. We study the use of transformer models in separating LSBGs from artefacts in the data from the Dark Energy Survey (DES) data release 1. Using the transformer models, we then search for new LSBGs from the DES that the previous searches may have missed. Properties of the newly found LSBGs are investigated, along with an analysis of the properties of the total LSBG sample in DES. We identified 4,083 new LSBGs in DES, adding an additional $\sim17\%$ to the LSBGs already known in DES. This also increased the number density of LSBGs in DES to 5.5 deg$^{-2}$. We performed a clustering analysis of the LSBGs in DES using an angular two-point auto-correlation function and found that LSBGs cluster more strongly than their high surface brightness counterparts. We associated 1310 LSBGs with galaxy clusters and identified 317 among them as ultra-diffuse galaxies (UDGs). We found that these cluster LSBGs are getting bluer and larger in size towards the edge of the clusters when compared with those in the centre. Transformer models have the potential to be on par with convolutional neural networks as state-of-the-art algorithms in analysing astronomical data.Comment: 23 pages, 24 Figures and 4 Tables. Accepted for publication in A&

CSNL: A cost-sensitive non-linear decision tree algorithm

This article presents a new decision tree learning algorithm called CSNL that induces Cost-Sensitive Non-Linear decision trees. The algorithm is based on the hypothesis that nonlinear decision nodes provide a better basis than axis-parallel decision nodes and utilizes discriminant analysis to construct nonlinear decision trees that take account of costs of misclassification. The performance of the algorithm is evaluated by applying it to seventeen datasets and the results are compared with those obtained by two well known cost-sensitive algorithms, ICET and MetaCost, which generate multiple trees to obtain some of the best results to date. The results show that CSNL performs at least as well, if not better than these algorithms, in more than twelve of the datasets and is considerably faster. The use of bagging with CSNL further enhances its performance showing the significant benefits of using nonlinear decision nodes. The performance of the algorithm is evaluated by applying it to seventeen data sets and the results are compared with those obtained by two well known cost-sensitive algorithms, ICET and MetaCost, which generate multiple trees to obtain some of the best results to date. The results show that CSNL performs at least as well, if not better than these algorithms, in more than twelve of the data sets and is considerably faster. The use of bagging with CSNL further enhances its performance showing the significant benefits of using non-linear decision nodes

Narrow-band search for gravitational waves from known pulsars using the second LIGO observing run

Isolated spinning neutron stars, asymmetric with respect to their rotation axis, are expected to be sources of continuous gravitational waves. The most sensitive searches for these sources are based on accurate matched filtering techniques that assume the continuous wave to be phase locked with the pulsar beamed emission. While matched filtering maximizes the search sensitivity, a significant signal-to-noise ratio loss will happen in the case of a mismatch between the assumed and the true signal phase evolution. Narrowband algorithms allow for a small mismatch in the frequency and spin-down values of the pulsar while coherently integrating the entire dataset. In this paper, we describe a narrow-band search using LIGO O2 data for the continuous wave emission of 33 pulsars. No evidence of a continuous wave signal is found, and upper limits on the gravitational wave amplitude over the analyzed frequency and spin-down ranges are computed for each of the targets. In this search, we surpass the spin-down limit, namely, the maximum rotational energy loss due to gravitational waves emission for some of the pulsars already present in the LIGO O1 narrow-band search, such as J1400 − 6325, J1813 − 1246, J1833 − 1034, J1952 þ 3252, and for new targets such as J0940 − 5428 and J1747 − 2809. For J1400 − 6325, J1833 − 1034, and J1747 − 2809, this is the first time the spin-down limit is surpassed

A survey of cost-sensitive decision tree induction algorithms

The past decade has seen a significant interest on the problem of inducing decision trees that take account of costs of misclassification and costs of acquiring the features used for decision making. This survey identifies over 50 algorithms including approaches that are direct adaptations of accuracy based methods, use genetic algorithms, use anytime methods and utilize boosting and bagging. The survey brings together these different studies and novel approaches to cost-sensitive decision tree learning, provides a useful taxonomy, a historical timeline of how the field has developed and should provide a useful reference point for future research in this field

All-sky search for long-duration gravitational-wave transients in the second Advanced LIGO observing run

We present the results of a search for long-duration gravitational-wave transients in the data from the Advanced LIGO second observation run; we search for gravitational-wave transients of 2–500 s duration in the 24–2048 Hz frequency band with minimal assumptions about signal properties such as waveform morphologies, polarization, sky location or time of occurrence. Signal families covered by these search algorithms include fallback accretion onto neutron stars, broadband chirps from innermost stable circular orbit waves around rotating black holes, eccentric inspiral-merger-ringdown compact binary coalescence waveforms, and other models. The second observation run totals about 118.3 days of coincident data between November 2016 and August 2017. We find no significant events within the parameter space that we searched, apart from the already-reported binary neutron star merger GW170817. We thus report sensitivity limits on the root-sum-square strain amplitude hrss at 50% efficiency. These sensitivity estimates are an improvement relative to the first observing run and also done with an enlarged set of gravitationalwave transient waveforms. Overall, the best search sensitivity is h50% rss ¼ 2.7 × 10−22 Hz−1=2 for a millisecond magnetar model. For eccentric compact binary coalescence signals, the search sensitivity reaches h50% rss ¼ 9.6 × 10−22 Hz−1=2

Model Comparison from LIGO–Virgo Data on GW170817’s Binary Components and Consequences for the Merger Remnant

GW170817 is the very first observation of gravitational waves originating from the coalescence of two compact objects in the mass range of neutron stars, accompanied by electromagnetic counterparts, and offers an opportunity to directly probe the internal structure of neutron stars. We perform Bayesian model selection on a wide range of theoretical predictions for the neutron star equation of state. For the binary neutron star hypothesis, we find that we cannot rule out the majority of theoretical models considered. In addition, the gravitational-wave data alone does not rule out the possibility that one or both objects were low-mass black holes. We discuss the possible outcomes in the case of a binary neutron star merger, finding that all scenarios from prompt collapse to long-lived or even stable remnants are possible. For long-lived remnants, we place an upper limit of 1.9 kHz on the rotation rate. If a black hole was formed any time after merger and the coalescing stars were slowly rotating, then the maximum baryonic mass of non-rotating neutron stars is at most , and three equations of state considered here can be ruled out. We obtain a tighter limit of for the case that the merger results in a hypermassive neutron star

Binary Black Hole Population Properties Inferred from the First and Second Observing Runs of Advanced LIGO and Advanced Virgo

We present results on the mass, spin, and redshift distributions with phenomenological population models using the 10 binary black hole (BBH) mergers detected in the first and second observing runs completed by Advanced LIGO and Advanced Virgo. We constrain properties of the BBH mass spectrum using models with a range of parameterizations of the BBH mass and spin distributions. We find that the mass distribution of the more massive BH in such binaries is well approximated by models with no more than 1% of BHs more massive than 45M(circle dot) and a power-law index of alpha = 1.3(-1.7)(+1.4) (90% credibility). We also show that BBHs are unlikely to be composed of BHs with large spins aligned to the orbital angular momentum. Modeling the evolution of the BBH merger rate with redshift, we show that it is flat or increasing with redshift with 93% probability. Marginalizing over uncertainties in the BBH population, we find robust estimates of the BBH merger rate density of R = 53.2(-28.2)(+55.8) Gpc(-3) yr(-1) (90% credibility). As the BBH catalog grows in future observing runs, we expect that uncertainties in the population model parameters will shrink, potentially providing insights into the formation of BHs via supernovae, binary interactions of massive stars, stellar cluster dynamics, and the formation history of BHs across cosmic time

GW190521: A Binary Black Hole Merger with a Total Mass of 150 M⊙

On May 21, 2019 at 03:02:29 UTC Advanced LIGO and Advanced Virgo observed a short duration gravitational-wave signal, GW190521, with a three-detector network signal-to-noise ratio of 14.7, and an estimated false-alarm rate of 1 in 4900 yr using a search sensitive to generic transients. If GW190521 is from a quasicircular binary inspiral, then the detected signal is consistent with the merger of two black holes with masses of 85+21−14  M⊙ and 66+17−18  M⊙ (90% credible intervals). We infer that the primary black hole mass lies within the gap produced by (pulsational) pair-instability supernova processes, with only a 0.32% probability of being below 65  M⊙. We calculate the mass of the remnant to be 142+28−16  M⊙, which can be considered an intermediate mass black hole (IMBH). The luminosity distance of the source is 5.3+2.4−2.6  Gpc, corresponding to a redshift of 0.82+0.28−0.34. The inferred rate of mergers similar to GW190521 is 0.13+0.30−0.11  Gpc−3 yr−1