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The current knowledge in cosmology deals with open problems whose solutions are still under investigation. The main issue is the so-called Hubble constant ($H_0$) tension, namely, the $4-6 \sigma$ discrepancy between the local value of $H_0$ obtained with Cepheids+Supernovae Ia (SNe Ia) and the cosmological one estimated from the observations of the Cosmic Microwave Background (CMB). For the investigation of this problem, probes that span all over the redshift $z$ ranges are needed. Cepheids are local objects, SNe Ia reached up to $z=2.9$, and CMB is observed at $z=1100$. In this context, the use of probes at intermediate redshift $z>3$ is auspicious for casting more light on modern cosmology. The Gamma-ray Bursts (GRBs) are particularly suitable for this task, given their observability up to $z=9.4$. The use of GRBs as standardizable candles requires the use of tight and reliable astrophysical correlations and the presence of gaps in the GRB time-domain data represents an obstacle in this sense. In this work, we propose to improve the precision of the lightcurve (LC) parameters through a reconstruction process performed with the functional forms of GRB LCs and the Gaussian Processes (GP). The filling of gaps in the GRB LCs through these processes shows an improvement up to $41.5\%$ on the precision of the LC parameters fitting, which lead to a reduced scatter in the astrophysical correlations and, thus, in the estimation of cosmological parameters., Comment: 9 pages, 1 figure. Submitted as proceeding for the FRAPWS24 (PoS-Sissa)

Strong lensing systems, expected to be abundantly discovered by next-generation surveys, offer a powerful tool for studying cosmology and galaxy evolution. The connection between galaxy structure and cosmology through distance ratios highlights the need to examine the evolution of lensing galaxy mass density profiles. We propose a novel, dark energy-model-independent method to investigate the mass density slopes of lensing galaxies and their redshift evolution using an extended power-law (EPL) model. We employ a non-parametric approach based on Artificial Neural Networks (ANNs) trained on Type Ia Supernovae (SNIa) data to reconstruct distance ratios of strong lensing systems. These ratios are compared with theoretical predictions to estimate the evolution of EPL model parameters. Analyses conducted at three levels, including the combined sample, individual lenses, and binned groups, ensure robust and reliable estimates. A negative trend in the mass density slope with redshift is observed, quantified as $\partial\gamma/\partial z = -0.20 \pm 0.12$ under a triangular prior for anisotropy. This study demonstrates that the redshift evolution of density slopes in lensing galaxies can be determined independently of dark energy models. Simulations based on LSST Rubin Observatory forecasts, which anticipate 100,000 strong lenses, show that spectroscopic follow-up of just 10 percent of these systems can constrain the redshift evolution coefficient with uncertainty ($\Delta\partial\gamma/\partial z$) to 0.021. This precision distinguishes evolving and non-evolving density slopes, providing new insights into galaxy evolution and cosmology., Comment: 16 pages, 16 figures

Gamma-ray bursts (GRBs), which are highly energetic bursts, have been detected at high redshifts (z = 9.4) and are pivotal for cosmological research and the study of Population III stars. To address these studies, it is important to identify correlations between the key GRB variables while minimizing uncertainties. This requires comprehensive coverage of GRB Light curves (LCs), which are challenging to obtain given the observational gaps and noise. This study explores the reconstruction of GRB LCs using three machine-learning models: Bi-LSTM, CGAN, and a Sarimax-based Kalman filter. To evaluate these approaches, we also include well-established functional form parametric methods such as the Willingale model (W07) and the non-parametric Gaussian Processes (GPs), expanding upon arXiv:2305.12126 by applying our techniques to a larger sample of 545 GRBs, covering four different GRB classifications. Our findings reveal that the LSTM model achieves the highest reductions in the uncertainties of the plateau parameters, with decreases of 29% in log Ta, 31% in log Fa and 40% in {\alpha}. In comparison, GPs show a slightly smaller reduction of 22% in both log Ta and log Fa, and 32% in {\alpha} while CGANs exhibit a decrease of 23% in log Ta, 24% in log Fa, and 31% in {\alpha}. The Sarimax-based Kalman filter performs well for well-sampled and flare-free GRBs, but it has problems with reconstruction in more complex cases. It shows a decrease of 14% in log Ta, 5% in log Fa and 6% in {\alpha}. [..] For the subset of 299 Good GRBs, namely with well-defined plateau emission with no flares and bumps, the LSTM model achieved a reduction of approximately 40%, with a percentage increase of 8% compared to the results observed in arXiv:2305.12126. [..] These advancements are essential for using GRBs as standard candles in cosmology and investigating theoretical models.[..], Comment: 27 pages, 18 figures (52 panels), 7 Tables, Submitted to JHEAP. Comments are welcome

Estimating the evolution of the battery's State of Charge (SoC) in response to its usage is critical for implementing effective power management policies and for ultimately improving the system's lifetime. Most existing estimation methods are either physics-based digital twins of the battery or data-driven models such as Neural Networks (NNs). In this work, we propose two new contributions in this domain. First, we introduce a novel NN architecture formed by two cascaded branches: one to predict the current SoC based on sensor readings, and one to estimate the SoC at a future time as a function of the load behavior. Second, we integrate battery dynamics equations into the training of our NN, merging the physics-based and data-driven approaches, to improve the models' generalization over variable prediction horizons. We validate our approach on two publicly accessible datasets, showing that our Physics-Informed Neural Networks (PINNs) outperform purely data-driven ones while also obtaining superior prediction accuracy with a smaller architecture with respect to the state-of-the-art.

Context. Gamma-ray bursts (GRBs), observed at redshifts as high as 9.4, could serve as valuable probes for investigating the distant Universe. However, this necessitates an increase in the number of GRBs with determined redshifts, as currently, only 12% of GRBs have known redshifts due to observational biases. Aims. We aim to address the shortage of GRBs with measured redshifts, enabling us to fully realize their potential as valuable cosmological probes Methods. Following Dainotti et al. (2024c), we have taken a second step to overcome this issue by adding 30 more GRBs to our ensemble supervised machine learning training sample, an increase of 20%, which will help us obtain better redshift estimates. In addition, we have built a freely accessible and user-friendly web app that infers the redshift of long GRBs (LGRBs) with plateau emission using our machine learning model. The web app is the first of its kind for such a study and will allow the community to obtain redshift estimates by entering the GRB parameters in the app. Results. Through our machine learning model, we have successfully estimated redshifts for 276 LGRBs using X-ray afterglow parameters detected by the Neil Gehrels Swift Observatory and increased the sample of LGRBs with known redshifts by 110%. We also perform Monte Carlo simulations to demonstrate the future applicability of this research. Conclusions. The results presented in this research will enable the community to increase the sample of GRBs with known redshift estimates. This can help address many outstanding issues, such as GRB formation rate, luminosity function, and the true nature of low-luminosity GRBs, and enable the application of GRBs as standard candles, Comment: 18 Figures, 3 tables. Submitted for publication in Astronomy and Astrophysics journal

PPG-based Blood Pressure (BP) estimation is a challenging biosignal processing task for low-power devices such as wearables. State-of-the-art Deep Neural Networks (DNNs) trained for this task implement either a PPG-to-BP signal-to-signal reconstruction or a scalar BP value regression and have been shown to outperform classic methods on the largest and most complex public datasets. However, these models often require excessive parameter storage or computational effort for wearable deployment, exceeding the available memory or incurring too high latency and energy consumption. In this work, we describe a fully-automated DNN design pipeline, encompassing HW-aware Neural Architecture Search (NAS) and Quantization, thanks to which we derive accurate yet lightweight models, that can be deployed on an ultra-low-power multicore System-on-Chip (SoC), GAP8. Starting from both regression and signal-to-signal state-of-the-art models on four public datasets, we obtain optimized versions that achieve up to 4.99% lower error or 73.36% lower size at iso-error. Noteworthy, while the most accurate SoA network on the largest dataset can not fit the GAP8 memory, all our optimized models can; our most accurate DNN consumes as little as 0.37 mJ while reaching the lowest MAE of 8.08 on Diastolic BP estimation.

Gamma-ray bursts (GRBs) are intense, short-lived bursts of gamma-ray radiation observed up to a high redshift ($z \sim 10$) due to their luminosities. Thus, they can serve as cosmological tools to probe the early Universe. However, we need a large sample of high$-z$ GRBs, currently limited due to the difficulty in securing time at the large aperture Telescopes. Thus, it is painstaking to determine quickly whether a GRB is high$z$ or low$-z$, which hampers the possibility of performing rapid follow-up observations. Previous efforts to distinguish between high$-$ and low$-z$ GRBs using GRB properties and machine learning (ML) have resulted in limited sensitivity. In this study, we aim to improve this classification by employing an ensemble ML method on 251 GRBs with measured redshifts and plateaus observed by the Neil Gehrels Swift Observatory. Incorporating the plateau phase with the prompt emission, we have employed an ensemble of classification methods to enhance the sensitivity unprecedentedly. Additionally, we investigate the effectiveness of various classification methods using different redshift thresholds, $z_{threshold}$=$z_t$ at $z_{t}=$ 2.0, 2.5, 3.0, and 3.5. We achieve a sensitivity of 87\% and 89\% with a balanced sampling for both $z_{t}=3.0$ and $z_{t}=3.5$, respectively, representing a 9\% and 11\% increase in the sensitivity over Random Forest used alone. Overall, the best results are at $z_{t} = 3.5$, where the difference between the sensitivity of the training set and the test set is the smallest. This enhancement of the proposed method paves the way for new and intriguing follow-up observations of high$-z$ GRBs., Comment: 45 pages, 16 Figures (6 Figures with single panel, 4 Figures with 8 panels, 2 Figures with 4 panels, 1 Figure with 12 panels, 1 figure with 6 panels, 2 Figure with 2 panels), 6 Tables, accepted for publication in ApJS

The first generation of ELT instruments includes an optical-infrared high-resolution spectrograph, indicated as ELT-HIRES and recently christened ANDES (ArmazoNes high Dispersion Echelle Spectrograph). ANDES consists of three fibre-fed spectrographs ([U]BV, RIZ, YJH) providing a spectral resolution of $\sim$100,000 with a minimum simultaneous wavelength coverage of 0.4-1.8 $\mu$m with the goal of extending it to 0.35-2.4 $\mu$m with the addition of a U arm to the BV spectrograph and a separate K band spectrograph. It operates both in seeing- and diffraction-limited conditions and the fibre feeding allows several, interchangeable observing modes including a single conjugated adaptive optics module and a small diffraction-limited integral field unit in the NIR. Modularity and fibre-feeding allow ANDES to be placed partly on the ELT Nasmyth platform and partly in the Coud\'e room. ANDES has a wide range of groundbreaking science cases spanning nearly all areas of research in astrophysics and even fundamental physics. Among the top science cases, there are the detection of biosignatures from exoplanet atmospheres, finding the fingerprints of the first generation of stars, tests on the stability of Nature's fundamental couplings, and the direct detection of the cosmic acceleration. The ANDES project is carried forward by a large international consortium, composed of 35 Institutes from 13 countries, forming a team of almost 300 scientists and engineers which include the majority of the scientific and technical expertise in the field that can be found in ESO member states., Comment: SPIE astronomical telescope and instrumentation 2024, in press

POLAR-2 is a dedicated gamma-ray polarimeter currently foreseen to be launched towards the China Space Station around 2027. The design of the detector is based on the legacy of its predecessor mission POLAR which was launched in 2016. POLAR-2 aims to measure the polarization of the Gamma-ray Burst prompt emission within the 30-800 keV energy range. Thanks to its high sensitivity to gamma-ray polarization, as well as its large effective area, POLAR-2 will provide the most precise measurements of this type to date. Such measurements are key to improve our understanding of the astrophysical processes responsible for Gamma-Ray Bursts. The detector consists of a segmented array of plastic scintillator bars, each one of which is read out by a Silicon PhotoMultiplier channel. The flight model of POLAR-2 will contain a total of 6400 scintillators. These are divided into 100 groups of 64 bars each, in so-called polarimeter modules. In recent years, the collaboration has designed and produced the first prototypes of these polarimeter modules and subjected these to space qualification tests. In addition, in April 2023, the first of these modules were calibrated using fully polarized gamma-ray beams at the European Synchrotron Radiation Facility (ESRF) in France. In this work, we will present the results of this calibration campaign and compare these to the simulated performance of the POLAR-2 modules. Potential improvements to the design are also discussed. Finally, the measurements are used, in combination with the verified simulation framework, to estimate the scientific performance of the full POLAR-2 detector and compare it to its predecessor., Comment: Accepted for publication in JINST

We apply a state-of-the-art transformer algorithm to 221 deg$^2$ of the Kilo Degree Survey (KiDS) to search for new strong gravitational lenses (SGL). We test four transformer encoders trained on simulated data from the Strong Lens Finding Challenge on KiDS survey data. The best performing model is fine-tuned on real images of SGL candidates identified in previous searches. To expand the dataset for fine-tuning, data augmentation techniques are employed, including rotation, flipping, transposition, and white noise injection. The network fine-tuned with rotated, flipped, and transposed images exhibited the best performance and is used to hunt for SGL in the overlapping region of the Galaxy And Mass Assembly (GAMA) and KiDS surveys on galaxies up to $z$=0.8. Candidate SGLs are matched with those from other surveys and examined using GAMA data to identify blended spectra resulting from the signal from multiple objects in a fiber. We observe that fine-tuning the transformer encoder to the KiDS data reduces the number of false positives by 70%. Additionally, applying the fine-tuned model to a sample of $\sim$ 5,000,000 galaxies results in a list of $\sim$ 51,000 SGL candidates. Upon visual inspection, this list is narrowed down to 231 candidates. Combined with the SGL candidates identified in the model testing, our final sample includes 264 candidates, with 71 high-confidence SGLs of which 44 are new discoveries. We propose fine-tuning via real augmented images as a viable approach to mitigating false positives when transitioning from simulated lenses to real surveys. Additionally, we provide a list of 121 false positives that exhibit features similar to lensed objects, which can benefit the training of future machine learning models in this field., Comment: 28 pages, 21 figures, accepted in A&A on 16/05/2024

How the quiescent galaxies evolve with redshift and the factors that impact their evolution are still debated. It is still unclear what the dominant mechanisms of passive galaxy growth are and what role is played by the environment in shaping their evolutionary paths over cosmic time. Our aim is to study the mass-size relation (MSR) of the quiescent population and to understand how the environment shapes the MSR at intermediate redshift. We used the VIPERS, a large spectroscopic survey of $\sim$90~000 galaxies in the redshift range $0.5\le z \le 1.2$. We selected a mass-complete sample of 4786 passive galaxies based on the NUVrK diagram and refined it using the $D_n4000$ spectral index to study the MSR of the passive population over $0.5\le z \le 0.9$. The impact of the environment on the MSR and on the growth of the quiescent population is studied through the density contrast. The slope and the intercept of the MSR, $\alpha=0.62\pm 0.04$ and $\textrm{log}(A)=0.52\pm 0.01$, agree well with values from the literature at the same redshift. The intercept decreases with redshift, $R_e(z)=8.20\times (1+z)^{-1.70}$, while the slope remains roughly constant, and the same trend is observed in the low-density (LD) and high-density (HD) environments. We find that the average size of the quiescent population in the LD and HD environments are identical within $3\sigma$ and this result is robust against a change in the definition of the LD and HD environments or a change in the selection of quiescent galaxies. In the LD and HD environments, $\sim$30 and $\sim$40% of the population have experienced a minor merger process between $0.5\le z \le 0.9$. However, minor mergers account only for 30 to 40% of the size evolution in this redshift range, the remaining evolution likely being due to the progenitor bias., Comment: 23 pages, 17 figures, accepted for publication in A&A

Conventional studies of galaxy clustering within the framework of halo models typically assume that the density profile of all dark matter haloes can be approximated by the Navarro-Frenk-White (NFW) spherically symmetric profile. However, both modern N-body simulations and observational data suggest that most haloes are either oblate or prolate, and almost never spherical. In this paper we present a modified model of the galaxy correlation function. In addition to the five "classical" HOD parameters proposed by Zheng et al. 2007, it includes an additional free parameter $\phi$ in the modified NFW density profile describing the asymmetry of the host dark matter halo. Using a subhalo abundance matching model (SHAM), we populate galaxies within BolshoiP N-body simulations. We compute the projected two-point correlation function $w_p(r_p)$ for six stellar mass volume limited galaxy samples. We fit our model to the results, and then compare the best-fit asymmetry parameter $\phi$ (and other halo parameters) to the asymmetry of dark matter haloes measured directly from the simulations and find that they agree within 1$\sigma$. We then fit our model to the $w_p(r_p)$ results from Zehavi et al. 2011 and compare halo parameters. We show that our model accurately retrieves the halo asymmetry and other halo parameters. Additionally, we find $2-6\%$ differences between the halo masses ($\log M_{min}$ and $\log M_1$) estimated by our model and "classical" HOD models. The model proposed in this paper can serve as an alternative to multiparameter HOD models, since it can be used for relatively small samples of galaxies., Comment: 22 pages, 11 figures, submitted to ApJ

RISC-V cores have gained a lot of popularity over the last few years. However, being quite a recent and novel technology, there is still a gap in the availability of comprehensive simulation frameworks for RISC-V that cover both the functional and extra-functional aspects. This gap hinders progress in the field, as fast yet accurate system-level simulation is crucial for Design Space Exploration (DSE). This work presents an open-source framework designed to tackle this challenge, integrating functional RISC-V simulation (achieved with GVSoC) with SystemC-AMS (used to model extra-functional aspects, in detail power storage and distribution). The combination of GVSoC and SystemC-AMS in a single simulation framework allows to perform a DSE that is dependent on the mutual impact between functional and extra-functional aspects. In our experiments, we validate the framework's effectiveness by creating a virtual prototype of a compact, battery-powered embedded system., Comment: 4 pages, 4 figures, to be published in Computing Frontiers Workshops and Special Sessions (CF '24 Companion), May 7--9, 2024, Ischia, Italy

Aims. In this work, we intend to examine how environment influences the merger fraction, from the low density field environment to higher density groups and clusters. We also aim to study how the properties of a group or cluster, as well as the position of a galaxy in the group or cluster, influences the merger fraction. Methods. We identified galaxy groups and clusters in the North Ecliptic Pole using a friends-of-friends algorithm and the local density. Once identified, we determined the central galaxies, group radii, velocity dispersions, and group masses of these groups and clusters. Merging systems were identified with a neural network as well as visually. With these, we examined how the merger fraction changes as the local density changes for all galaxies as well as how the merger fraction changes as the properties of the groups or clusters change. Results. We find that the merger fraction increases as local density increases and decreases as the velocity dispersion increases, as is often found in literature. A decrease in merger fraction as the group mass increases is also found. We also find groups with larger radii have higher merger fractions. The number of galaxies in a group does not influence the merger fraction. Conclusions. The decrease in merger fraction as group mass increases is a result of the link between group mass and velocity dispersion. Hence, this decrease of merger fraction with increasing mass is a result of the decrease of merger fraction with velocity dispersion. The increasing relation between group radii and merger fraction may be a result of larger groups having smaller velocity dispersion at a larger distance from the centre or larger groups hosting smaller, infalling groups with more mergers. However, we do not find evidence of smaller groups having higher merger fractions., Comment: 15 pages, 10 figures, 8 tables, 2 appendices, accepted for publication in Astronomy & Astrophysics

Galaxy mergers play a crucial role in galaxy evolution. However, the correlation between mergers and the local environment of galaxies is not fully understood. We aim to address the question of whether galaxy mergers prefer denser or less dense environments by quantifying the spatial clustering of mergers and non-mergers. We use two different indicators to classify mergers and non-mergers - classification based on a deep learning technique ($f$) and non-parametric measures of galaxy morphology, Gini-$M_{20}$ ($g$). We used a set of galaxy samples in the redshift range $0.1 < z < 0.15$ from the Galaxy and Mass Assembly (GAMA) survey with a stellar mass cut of $\log (M_{\star}/M_{\odot} ) > 9.5$. We measured and compared the two-point correlation function (2pCF) of mergers and non-mergers classified using the two merger indicators $f$ and $g$. We measured the marked correlation function (MCF), in which the galaxies are weighted by $f$ to probe the environmental dependence of galaxy mergers. We do not observe a statistically significant difference between the clustering strengths of mergers and non-mergers obtained using 2pCF. However, using the MCF measurements with $f$ as a mark, we observe an anti-correlation between the likelihood of a galaxy being a merger and its environment. Our results emphasise the advantage of MCF over 2pCF in probing the environmental correlations. Based on the MCF measurements, we conclude that the galaxy mergers prefer to occur in the under-dense environments on scales $> 50 \, h^{-1} \mathrm{kpc}$ of the large-scale structure (LSS). We attribute this observation to the high relative velocities of galaxies in the densest environments that prevent them from merging., Comment: 13 pages, 9 figures, matches the version published in A&A

Gamma-ray bursts (GRBs) can be probes of the early universe, but currently, only 26% of GRBs observed by the Neil Gehrels Swift Observatory GRBs have known redshifts ($z$) due to observational limitations. To address this, we estimated the GRB redshift (distance) via a supervised statistical learning model that uses optical afterglow observed by Swift and ground-based telescopes. The inferred redshifts are strongly correlated (a Pearson coefficient of 0.93) with the observed redshifts, thus proving the reliability of this method. The inferred and observed redshifts allow us to estimate the number of GRBs occurring at a given redshift (GRB rate) to be 8.47-9 $yr^{-1} Gpc^{-1}$ for $1.9

Large future sky surveys, such as the LSST, will provide optical photometry for billions of objects. This paper aims to construct a proxy for the far ultraviolet attenuation (AFUVp) from the optical data alone, enabling the rapid estimation of the star formation rate (SFR) for galaxies that lack UV or IR data. To mimic LSST observations, we use the deep panchromatic optical coverage of the SDSS Photometric Catalogue DR~12, complemented by the estimated physical properties for the SDSS galaxies from the GALEX-SDSS-WISE Legacy Catalog (GSWLC) and inclination information obtained from the SDSS DR7. We restricted our sample to the 0.025-0.1 z-spec range and investigated relations among surface brightness, colours, and dust attenuation in the far UV range for star-forming galaxies obtained from the spectral energy distribution (SED). {Dust attenuation is best correlated with (u-r) colour and the surface brightness in the u band ($\rm \mu_{u}$). We provide a dust attenuation proxy for galaxies on the star-forming main sequence, which can be used for the LSST or any other type of broadband optical survey. The mean ratio between the catalogue values of SFR and those estimated using optical-only SDSS data with the AFUVp prior calculated as $\Delta$SFR=log(SFR$_{\tiny{\mbox{this work}}}$/SFR$_{\tiny{}\texttt{GSWLC}}$) is found to be less than 0.1~dex, while runs without priors result in an SFR overestimation larger than 0.3~dex. The presence or absence of theAFUVp has a negligible influence on the stellar mass estimation (with $\Delta$M$_{star}$ in the range from 0 to $-0.15$ dex). Forthcoming deep optical observations of the LSST Deep Drilling Fields, which also have multi-wavelength data, will enable one to calibrate the obtained relation for higher redshift galaxies and, possibly, extend the study towards other types of galaxies, such as early-type galaxies off the main sequence., Comment: 18 pages, accepted for publication in Astronomy and Astrophysics

Estimating the State of Health (SOH) of batteries is crucial for ensuring the reliable operation of battery systems. Since there is no practical way to instantaneously measure it at run time, a model is required for its estimation. Recently, several data-driven SOH models have been proposed, whose accuracy heavily relies on the quality of the datasets used for their training. Since these datasets are obtained from measurements, they are limited in the variety of the charge/discharge profiles. To address this scarcity issue, we propose generating datasets by simulating a traditional battery model (e.g., a circuit-equivalent one). The primary advantage of this approach is the ability to use a simulatable battery model to evaluate a potentially infinite number of workload profiles for training the data-driven model. Furthermore, this general concept can be applied using any simulatable battery model, providing a fine spectrum of accuracy/complexity tradeoffs. Our results indicate that using simulated data achieves reasonable accuracy in SOH estimation, with a 7.2% error relative to the simulated model, in exchange for a 27X memory reduction and a =2000X speedup., Comment: 6 pages, 5 figures, conference paper at the 2023 IEEE/ACM ISLPED

Gamma-Ray Bursts (GRBs), due to their high luminosities are detected up to redshift 10, and thus have the potential to be vital cosmological probes of early processes in the universe. Fulfilling this potential requires a large sample of GRBs with known redshifts, but due to observational limitations, only 11\% have known redshifts ($z$). There have been numerous attempts to estimate redshifts via correlation studies, most of which have led to inaccurate predictions. To overcome this, we estimated GRB redshift via an ensemble supervised machine learning model that uses X-ray afterglows of long-duration GRBs observed by the Neil Gehrels Swift Observatory. The estimated redshifts are strongly correlated (a Pearson coefficient of 0.93) and have a root mean square error, namely the square root of the average squared error $\langle\Delta z^2\rangle$, of 0.46 with the observed redshifts showing the reliability of this method. The addition of GRB afterglow parameters improves the predictions considerably by 63\% compared to previous results in peer-reviewed literature. Finally, we use our machine learning model to infer the redshifts of 154 GRBs, which increase the known redshifts of long GRBs with plateaus by 94\%, a significant milestone for enhancing GRB population studies that require large samples with redshift., Comment: 12 Figures, 24 pages. Accepted for publication at The Astrophysical Journal Supplement Series

We used 5487 star-forming galaxies at redshift z~0.63 extracted from the VIPERS and 143774 comparison galaxies in the local Universe from the GSWLC catalog. We employed two families of methods: parametric and non-parametric. In the former approaches, we compared the FMR projections plagued by observational biases on differently constructed control samples at various redshifts. Then, the metallicity difference between different redshifts in M*-SFR bins. In the latter approach, we related the metallicity and the normalized sSFR. The methodologies implemented to construct fair, complete samples for studying the MZR and the FMR produced consistent results showing a small, but still statistically significant evolution of both relations up to z~0.63. In particular, we observed a systematic trend where the median metallicity of the sample at z=0.63 is lower than that of the local sample at the same M* and SFR. The average difference in the metallicity of the low and intermediate redshifts is approximately 1.8 times the metallicity standard deviation of the median, of the intermediate redshift sample, in M*-SFR bins. We confirmed this result using the Kolmogorov-Smirnov test. When we applied the M*-completeness criterion to catalogs, the metallicity difference in redshifts decreased to approximately 0.96 times the metallicity standard deviation of the median, thus not statistically significant. This result may be dominated by the limited parameter space, being the lower M* galaxies where the difference is larger out from the analysis. A careful reading of the results, and their underlying selection criteria, are crucial in studies of the mass-metallicity and FMRs., Comment: Accepted for publication in A&A, 22 pages, 20 figures

In every proposed unification scheme for Active Galactic Nuclei (AGN), an integral element is the presence of circumnuclear dust arranged in torus-like structures. A crucial model parameter in this context is the covering factor (CF), defined as the ratio between the infrared luminosity of the dusty torus $L_{\rm IR}$, and the accretion disk bolometric luminosity $L_{\rm agn}$. Our study aims to determine whether CF evolution is genuine or if selection effects significantly influence it. Based on cross-matched multiwavelength photometrical data from the five major surveys (SDSS, GALEX, UKIDSS, WISE, SPITZER), a sample of almost 2,000 quasars was derived. The main parameters of quasars, such as black hole masses and the Eddington ratios, were calculated based on the spectroscopic data. The data were divided into two redshift bins: Low-$z$ (redshift ~1) and High-$z$ (redshift ~2) quasars. We identified an issue with the accuracy of the WISE W4 filter. Whenever feasible, it is recommended to utilize SPITZER MIPS 24 $\mu$m data. The calculated median CF values for the highest quality SPITZER data are comparable within errors $\log$ CF$_{\textrm{low}-z} = -0.19\pm 0.11$ and $\log$ CF$_{\textrm{high}-z}= -0.18\pm 0.11$. The Efron & Petrosian test confirmed the presence of luminosity evolution with redshift for both $L_{\rm IR}$ and $L_{\rm agn}$. Both the Low-$z$ and High-$z$ samples exhibit a similar correlation between $L_{\rm agn}$ and $L_{\rm IR}$. No discernible evolution of the CF was observed in the subsample of quasars with high SMBH mass bin or high luminosities. The relationship between $L_{\rm IR}$ and $L_{\rm agn}$ deviates slightly from the expected 1:1 scaling. However, no statistically significant dependence of CF on luminosities could be claimed across the entire dataset., Comment: 21 pages, 22 figures, Astronomy & Astrophysics accepted

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&A

We combine the LOw-Frequency ARray (LOFAR) Two-metre Sky Survey (LoTSS) second data release (DR2) catalogue with gravitational lensing maps from the Cosmic Microwave Background (CMB) to place constraints on the bias evolution of LoTSS radio galaxies, and on the amplitude of matter perturbations. We construct a flux-limited catalogue, and analyse its harmonic-space cross-correlation with CMB lensing maps from Planck, $C_\ell^{g\kappa}$, as well as its auto-correlation, $C_\ell^{gg}$. We explore the models describing the redshift evolution of the large-scale radio galaxy bias, discriminating between them through the combination of both $C_\ell^{g\kappa}$ and $C_\ell^{gg}$. Fixing the bias evolution, we then use these data to place constraints on the amplitude of large scale density fluctuations. We report the significance of the $C_\ell^{g\kappa}$ signal at a level of $26.6\sigma$. We determine that a linear bias evolution of the form $b_g(z) = b_{g,D} / D(z)$, where $D(z)$ is the growth rate, is able to provide a good description of the data, and measure $b_{g,D} = 1.41 \pm 0.06$ for a sample flux-limited at $1.5\,{\rm mJy}$, for scales $\ell < 250$ for $C_\ell^{gg}$, and $\ell < 500$ for $C_\ell^{g\kappa}$. At the sample's median redshift, we obtain $b(z = 0.82) = 2.34 \pm 0.10$. Using $\sigma_8$ as a free parameter, while keeping other cosmological parameters fixed to the Planck values, we find fluctuations of $\sigma_8 = 0.75^{+0.05}_{-0.04}$. The result is in agreement with weak lensing surveys, and at $1\sigma$ difference with Planck CMB constraints. We also attempt to detect the late-time integrated Sachs-Wolfe effect with LOFAR, but with the current sky coverage, the cross-correlation with CMB temperature maps is consistent with zero. Our results are an important step towards constraining cosmology with radio continuum surveys from LOFAR and other future large radio surveys., Comment: The code is available at https://github.com/snakoneczny/cosmo-pipe

We aim to understand what drives the IRX-\beta dust attenuation relation at intermediate redshift (0.5 < z < 0.8) in star-forming galaxies. We investigate the role of various galaxy properties in shaping this observed relation. We use robust [O ii] {\lambda}3727, [O iii] {\lambda}{\lambda}4959, 5007, and H\beta line detections of our statistical sample of 1049 galaxies to estimate the gas-phase metallicities. We derive key physical properties that are necessary to study galaxy evolution, such as the stellar masses and the star formation rates, using the spectral energy distribution fitting tool CIGALE. Equivalently, we study the effect of galaxy morphology (mainly the S\'ersic index n and galaxy inclination) on the observed IRX-\beta scatter. We also investigate the role of the environment in shaping dust attenuation in our sample. We find a strong correlation of the IRX-\beta relation on gas-phase metallicity in our sample, and also strong correlation with galaxy compactness characterized by the S\'ersic indexes. Correlations are also seen with stellar masses, specific star formation rates and the stellar ages of our sources. Metallicity strongly correlates with the IRX-\beta scatter, this also results from the older stars and higher masses at higher beta values. Galaxies with higher metallicities show higher IRX and higher beta values. The correlation with specific dust mass strongly shifts the galaxies away from the IRX-\beta relation towards lower \b{eta} values. We find that more compact galaxies witness a larger amount of attenuation than less compact galaxies. There is a subtle variation in the dust attenuation scatter between edge-on and face-on galaxies, but the difference is not statistically significant. Galaxy environments do not significantly affect dust attenuation in our sample of star-forming galaxies at intermediate redshift., Comment: 14 pages, 13 figures, accepted for publication in A&A

Gamma-Ray Bursts are among the most powerful events in the Universe. Despite half a century of observations of these transient sources, many open questions remain about their nature. Polarization measurements of the GRB prompt emission have long been theorized to be able to answer most of these questions. With the aim of characterizing the polarization of these prompt emissions, a compact Compton polarimeter, called POLAR, has been launched to space in September 2016. Time integrated polarization analysis of the POLAR GRB catalog have shown that the prompt emission is lowly polarized or fully unpolarized. However, time resolved analysis depicted strong hints of an evolving polarization angle within single pulses, washing out the polarization degree in time integrated analyses. Here we will for the first time present energy resolved polarization measurements with the POLAR data. The novel analysis, performed on several GRBs, will provide new insights and alter our understanding of GRB polarization. The analysis was performed using the 3ML framework to fit polarization parameters versus energy in parallel to the spectral parameters. Although limited by statistics, the results could provide a very relevant input to disentangle between existing theoretical models. In order to gather more statistics per GRB and perform joint time and energy resolved analysis, a successor instrument, called POLAR-2, is under development with a launch window early 2025 to the CSS. After presenting the first energy resolved polarization results of the POLAR mission, we will present the prospects for such measurements with the upcoming POLAR-2 mission., Comment: Proceeding from the 38th International Cosmic Ray Conference (ICRC2023), 9 pages, 6 figures

The POLAR-2 Gamma-Ray Burst (GRB) Polarimetry mission is a follow-up to the successful POLAR mission. POLAR collected six months of data in 2016-2017 on board the Tiangong-2 Chinese Space laboratory. From a polarization study on 14 GRBs, POLAR measured an overall low polarization and a hint for an unexpected complexity in the time evolution of polarization during GRBs. Energy-dependent measurements of the GRB polarization will be presented by N. de Angelis in GA21-09 (August 2nd). These results demonstrate the need for measurements with significantly improved accuracy. Moreover, the recent discovery of gravitational waves and their connection to GRBs justifies a high-precision GRB polarimeter that can provide both high-precision polarimetry and detection of very faint GRBs. The POLAR-2 polarimeter is based on the same Compton scattering measurement principle as POLAR, but with an extended energy range and an order of magnitude increase in total effective area for polarized events. Proposed and developed by a joint effort of Switzerland, China, Poland and Germany, the device was selected for installation on the China Space Station and is scheduled to start operation for at least 2 years in 2025., Comment: Proceeding from the 38th International Cosmic Ray Conference (ICRC2023), 8 pages, 6 figures

This work aims to determine how the galaxy main sequence (MS) changes using seven different commonly used methods to select the star-forming galaxies within VIPERS data over $0.5 \leq z < 1.2$. The form and redshift evolution of the MS will then be compared between selection methods. The star-forming galaxies were selected using widely known methods: a specific star-formation rate (sSFR), Baldwin, Phillips and Terlevich (BPT) diagram, 4000\AA\ spectral break (D4000) cut and four colour-colour cuts: NUVrJ, NUVrK, u-r, and UVJ. The main sequences were then fitted for each of the seven selection methods using a Markov chain Monte Carlo forward modelling routine, fitting both a linear main sequence and a MS with a high-mass turn-over to the star-forming galaxies. This was done in four redshift bins of $0.50 \leq z < 0.62$, $0.62 \leq z < 0.72$, $0.72 \leq z < 0.85$, and $0.85 \leq z < 1.20$. The slopes of all star-forming samples were found to either remain constant or increase with redshift, and the scatters were approximately constant. There is no clear redshift dependency of the presence of a high-mass turn-over for the majority of samples, with the NUVrJ and NUVrK being the only samples with turn-overs only at low redshift. No samples have turn-overs at all redshifts. Star-forming galaxies selected with sSFR and u-r are the only samples to have no high-mass turn-over in all redshift bins. The normalisation of the MS increases with redshift, as expected. The scatter around the MS is lower than the $\approx$0.3~dex typically seen in MS studies for all seven samples. The lack, or presence, of a high-mass turn-over is at least partially a result of the method used to select star-forming galaxies. However, whether a turn-over should be present or not is unclear., Comment: 20 pages, 3 appendices, 14 figures, 5 tables, accepted for publication in Astronomy & Astrophysics

The division of Gamma-ray bursts (GRBs) into different classes, other than the "short" and "long", has been an active field of research. We investigate whether GRBs can be classified based on a broader set of parameters, including prompt and plateau emission ones. Observational evidence suggests the existence of more GRB sub-classes, but results so far are either conflicting or not statistically significant. The novelty here is producing a machine-learning-based classification of GRBs using their observed X-rays and optical properties. We used two data samples: the first, composed of 203 GRBs, is from the Neil Gehrels Swift Observatory (Swift/XRT), and the latter, composed of 134 GRBs, is from the ground-based Telescopes and Swift/UVOT. Both samples possess the plateau emission (a flat part of the light curve happening after the prompt emission, the main GRB event). We have applied the Gaussian Mixture Model (GMM) to explore multiple parameter spaces and sub-class combinations to reveal if there is a match between the current observational sub-classes and the statistical classification. With these samples and the algorithm, we spot a few micro-trends in certain cases, but we cannot conclude that any clear trend exists in classifying GRBs. These microtrends could point towards a deeper understanding of the physical meaning of these classes (e.g., a different environment of the same progenitor or different progenitors). However, a larger sample and different algorithms could achieve such goals. Thus, this methodology can lead to deeper insights in the future., Comment: 20 pages, 10 figures (one has 4 panels, two have a single panel, six have 8 panels, one has 6 panels), 4 tables. Accepted for publication in MNRAS

Although low surface brightness galaxies (LSBs) contribute a large fraction to the number density of galaxies, their properties are still poorly known. LSBs are often considered dust poor, based only on a few studies. We use, for the first time, a large sample of LSBs and high surface brightness galaxies (HSBs) with deep observational data to study their dust properties as a function of surface brightness. Our sample consists of 1631 optically selected galaxies at $z < 0.1$ from the North Ecliptic Pole (NEP) wide field. We use the large set of data available in this field, from UV to FIR. We measured the optical size and the surface brightness of the targets, and analyzed their spectral energy distribution using the CIGALE fitting code. We found that the specific star formation rate and specific infrared luminosity (total infrared luminosity per stellar mass) remain mostly flat as a function of surface brightness for both LSBs and HSBs that are star-forming but decline steeply for the quiescent galaxies. The majority of LSBs in our sample have negligible dust attenuation (A$_{V} < 0.1$ mag), except for about 4% of them that show significant attenuation with a mean A$_{V}$ of 0.8 mag. We found that these LSBs also have a high $\textit{r}$-band mass-to-light ratio ($M/L_r>3$ M$_{\odot}$/L$_{\odot}$), and show similarity to the extreme giant LSBs from the literature, indicating a possibly higher dust attenuation in giant LSBs as well. This work provides a large catalog of LSBs and HSBs with detailed measurements of their several optical and infrared physical properties. Our results suggest that the dust content of LSBs is more varied than previously thought, with some of them having significant attenuation making them fainter than their intrinsic value. This will have serious implications for the observation and analysis of LSBs with current/upcoming surveys like JWST and LSST., Comment: 16 pages, 12 figures, accepted for publication in A&A

Red ultra-compact massive galaxies, called red nuggets were formed at high redshifts ($\rm{z\sim2-3}$). Survivors of red nuggets, known as relics, observed at lower redshifts ($\rm{z<2}$) are believed to remain almost unchanged since their formation. For the first time, we verify the environmental properties of red nuggets at intermediate redshift ($0.5<\rm{z}<0.9$ ) using 42 red, massive ($\rm{log(M_{star}/M_{\odot}) \geq 10.9}$) and ultra-compact ($\rm{R_{e}}<1.5$ kpc) from the VIMOS Public Extragalactic Redshift Survey (VIPERS). We found that the increasing fraction of red galaxies, when moving to denser environments, is driven by the red massive normal-size galaxies. Red nuggets, similarly to red intermediate-mass ($\rm{10.4\lesssim log(M_{star}/M_{\odot})<10.9}$) ultra-compact galaxies, are found in various types of environments, with consistent (within $1\sigma$) fractions across all local densities. Analysis of red nugget stellar ages suggests that relics are preferably found in high-density regions while quiescent red nuggets are overabundant in low-density environments. We speculate that red nuggets have survived to lower redshifts via two channels: i) in low-density environments where the fraction of red nuggets decreases as time passes due to (very) limited merger activity, ii) in high-density environments, where the number of red nuggets drops at higher redshift due to merger activity and is preserved at lower redshift as the high velocities of clusters prevent them from being cannibalised. Even more, the fraction of red nuggets in clusters may increase due to the addition of red massive normal-size galaxies deprived of their envelopes with cosmic time., Comment: Accepted for publication in MNRAS, 15 pages, 8 figures, 3 appendices

Gamma-Ray Bursts (GRBs), being observed at high redshift (z = 9.4), vital to cosmological studies and investigating Population III stars. To tackle these studies, we need correlations among relevant GRB variables with the requirement of small uncertainties on their variables. Thus, we must have good coverage of GRB light curves (LCs). However, gaps in the LC hinder the precise determination of GRB properties and are often unavoidable. Therefore, extensive categorization of GRB LCs remains a hurdle. We address LC gaps using a 'stochastic reconstruction,' wherein we fit two pre-existing models (Willingale 2007; W07 and Broken Power Law; BPL) to the observed LC, then use the distribution of flux residuals from the original data to generate data to fill in the temporal gaps. We also demonstrate a model-independent LC reconstruction via Gaussian Processes. At 10% noise, the uncertainty of the end time of the plateau, its correspondent flux, and the temporal decay index after the plateau decreases, on average, by 33.3% 35.03%, and 43.32%, respectively for the W07, and by 33.3%, 30.78%, 43.9% for the BPL. The slope of the plateau decreases by 14.76% in the BPL. After using the Gaussian Process technique, we see similar trends of a decrease in uncertainty for all model parameters for both the W07 and BPL models. These improvements are essential for the application of GRBs as standard candles in cosmology, for the investigation of theoretical models and for inferring the redshift of GRBs with future machine learning analysis., Comment: 20 pages, 6 tables, 11 figures

While we already seem to have a general scenario of the evolution of different types of galaxies, a complete and satisfactory understanding of the processes that led to the formation of all the variety of today's galaxy types is still beyond our reach. To solve this problem, we need both large datasets reaching high redshifts and novel methodologies for dealing with them. The VIPERS survey statistical power, which observed $\sim90,000$ galaxies at $z > 0.5$, and the application of an unsupervised clustering algorithm allowed us to distinguish 12 galaxy classes. Studies of their environmental dependence indicate that this classification may actually reflect different galaxy evolutionary paths. For instance, a class of the most passive red galaxies gathers galaxies $\sim20\%$ smaller than other red galaxies of a similar stellar mass, revealing the first sample of red nuggets at intermediate redshift. On the other end, a class of blue dwarf galaxies is composed mainly of AGN, challenging commonly used mid-infrared AGN selections., Comment: Accepted for publication in the ML4ASTRO (ICML 2022) proceeding book

Aims. We present the application of a fully connected neural network (NN) for galaxy merger identification using exclusively photometric information. Our purpose is not only to test the method's efficiency, but also to understand what merger properties the NN can learn and what their physical interpretation is. Methods. We created a class-balanced training dataset of 5\,860 galaxies split into mergers and non-mergers. The galaxy observations came from SDSS DR6 and were visually identified in Galaxy Zoo. The 2$\,$930 mergers were selected from known SDSS mergers and the respective non-mergers were the closest match in both redshift and $r$ magnitude. The NN architecture was built by testing a different number of layers with different sizes and variations of the dropout rate. We compared input spaces constructed using: the five SDSS filters: $u$, $g$, $r$, $i$, and $z$; combinations of bands, colours, and their errors; six magnitude types; and variations of input normalization. Results. We find that the fibre magnitude errors contribute the most to the training accuracy. Studying the parameters from which they are calculated, we show that the input space built from the sky error background in the five SDSS bands alone leads to 92.64 $\pm$ 0.15 \% training accuracy. We also find that the input normalization, that is to say, how the data are presented to the NN, has a significant effect on the training performance. Conclusions. We conclude that, from all the SDSS photometric information, the sky error background is the most sensitive to merging processes. This finding is supported by an analysis of its five-band feature space by means of data visualization. Moreover, studying the plane of the $g$ and $r$ sky error bands shows that a decision boundary line is enough to achieve an accuracy of 91.59\%., Comment: 16 pages, 20 figures, accepted in A&A on 05/11/2022, final version submitted on 09/12/2022 (also current one in arXiv)