arXiv subjects

Agnieszka Pollo

Publications and source records attributed to Agnieszka Pollo.

At least 37 records · Page 2Linked to original sources

arXiv2024

Response of the first POLAR-2 Prototype to Polarized Beams

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.

astro-ph.IM↗

arXiv2024

TEGLIE: Transformer encoders as strong gravitational lens finders in KiDS

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.

astro-ph.GA↗

arXiv2024

Halo asymmetry in the modelling of galaxy clustering

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 $ϕ$ 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 $ϕ$ (and other halo parameters) to the asymmetry of dark matter haloes measured directly from the simulations and find that they agree within 1$σ$. 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.

astro-ph.CO↗

arXiv2024

Inferring the redshift of more than 150 GRBs with a Machine Learning Ensemble model

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 $\langleΔz^2\rangle$, 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.

astro-ph.CO↗

arXiv2023

Covering factor in AGNs: evolution versus selection

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 $μ$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.

astro-ph.GA↗

arXiv2023

GRB Optical and X-ray Plateau Properties Classifier Using Unsupervised Machine Learning

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.

astro-ph.HE↗

arXiv2023

POLAR-2, the next generation of GRB polarization detector

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.

astro-ph.IM↗

arXiv2023

Energy-dependent polarization of Gamma-Ray Bursts' prompt emission with the POLAR and POLAR-2 instruments

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.

astro-ph.HE↗

arXiv2023

A Stochastic Approach To Reconstruct Gamma Ray Burst Lightcurves

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.

astro-ph.HE↗

arXiv2022

Finding Strong Gravitational Lenses Through Self-Attention

The upcoming large scale surveys like LSST are expected to find approximately $10^5$ strong gravitational lenses by analysing data of many orders of magnitude larger than those in contemporary astronomical surveys. In this case, non-automated techniques will be highly challenging and time-consuming, even if they are possible at all. We propose a new automated architecture based on the principle of self-attention to find strong gravitational lenses. The advantages of self-attention-based encoder models over convolution neural networks are investigated, and ways to optimise the outcome of encoder models are analysed. We constructed and trained 21 self-attention based encoder models and five convolution neural networks to identify gravitational lenses from the Bologna Lens Challenge. Each model was trained separately using 18,000 simulated images, cross-validated using 2,000 images, and then applied to a test set with 100,000 images. We used four different metrics for evaluation: classification accuracy, area under the receiver operating characteristic curve (AUROC), the TPR$_0$ score and the TPR$_{10}$ score. The performances of self-attention-based encoder models and CNNs participating in the challenge are compared. They were able to surpass the CNN models that participated in the Bologna Lens Challenge by a high margin for the TPR$_0$ and TPR_${10}$. Self-Attention based models have clear advantages compared to simpler CNNs. They have highly competing performance in comparison to the currently used residual neural networks. Compared to CNNs, self-attention based models can identify highly confident lensing candidates and will be able to filter out potential candidates from real data. Moreover, introducing the encoder layers can also tackle the over-fitting problem present in the CNNs by acting as effective filters.

cs.CV↗

arXiv2022

Proton Irradiation of SiPM arrays for POLAR-2

POLAR-2 is a space-borne polarimeter, built to investigate the polarization of Gamma-Ray Bursts and help elucidate their mechanisms. The instrument is targeted for launch in 2024 or 2025 aboard the China Space Station and is being developed by a collaboration between institutes from Switzerland, Germany, Poland and China. The instrument will orbit at altitudes between 340km and 450km with an inclination of 42$^{\circ}$ and will be subjected to background radiation from cosmic rays and solar events. It is therefore pertinent to better understand the performance of sensitive devices under space-like conditions. In this paper we focus on the radiation damage of the silicon photomultiplier arrays S13361-6075NE-04 and S14161-6050HS-04 from Hamamatsu. The S13361 are irradiated with 58MeV protons at several doses up to 4.96Gy, whereas the newer series S14161 are irradiated at doses of 0.254Gy and 2.31Gy. Their respective performance degradation due to radiation damage are discussed. The equivalent exposure time in space for silicon photomultipliers inside POLAR-2 with a dose of 4.96Gy is 62.9 years (or 1.78 years when disregarding the shielding from the instrument). Primary characteristics of the I-V curves are an increase in the dark current and dark counts, mostly through cross-talk events. Annealing processes at $25^{\circ}C$ were observed but not studied in further detail. Biasing channels while being irradiated have not resulted in any significant impact. Activation analyses showed a dominant contribution of $β^{+}$ particles around 511keV. These resulted primarily from copper and carbon, mostly with decay times shorter than the orbital period.

astro-ph.IM↗

arXiv2022

The first catalogue of spectroscopically confirmed red nuggets at z~0.7 from the VIPERS survey. Linking high-z red nuggets and local relics

'Red nuggets' are a rare population of passive compact massive galaxies thought to be the first massive galaxies that formed in the Universe. First found at $z \sim 3$, they are even less abundant at lower redshifts, and it is believed that with time they mostly transformed through mergers into today's giant ellipticals. Those red nuggets which managed to escape this fate can serve as unique laboratories to study the early evolution of massive galaxies. In this paper, we aim to make use of the VIMOS Public Extragalactic Redshift Survey to build the largest up-to-date catalogue of spectroscopically confirmed red nuggets at the intermediate redshift $0.5 8\times10^{10}$ $\rm{M}_{\odot}$ and effective radii $R_\mathrm{e}<1.5$ kpc. Among them, we select red, passive galaxies with old stellar population based on colour--colour NUVrK diagram, star formation rate values, and verification of their optical spectra. Verifying the influence of the limit of the source compactness on the selection, we found that the sample size can vary even up to two orders of magnitude, depending on the chosen criterion. Using one of the most restrictive criteria with additional checks on their spectra and passiveness, we spectroscopically identified only 77 previously unknown red nuggets. The resultant catalogue of 77 red nuggets is the largest such catalogue built based on the uniform set of selection criteria above the local Universe. Number density calculated on the final sample of 77 VIPERS passive red nuggets per comoving Mpc$^3$ increases from 4.7$\times10^{-6}$ at $z \sim 0.61$ to $9.8 \times 10^{-6}$ at $z \sim 0.95$, which is higher than values estimated in the local Universe, and lower than the ones found at $z>2$. It fills the gap at intermediate redshift.

astro-ph.GA↗

arXiv2022

Predicting the redshift of gamma-ray loud AGNs using Supervised Machine Learning: Part 2

Measuring the redshift of active galactic nuclei (AGNs) requires the use of time-consuming and expensive spectroscopic analysis. However, obtaining redshift measurements of AGNs is crucial as it can enable AGN population studies, provide insight into the star formation rate, the luminosity function, and the density rate evolution. Hence, there is a requirement for alternative redshift measurement techniques. In this project, we aim to use the Fermi gamma-ray space telescope's 4LAC Data Release (DR2) catalog to train a machine learning model capable of predicting the redshift reliably. In addition, this project aims at improving and extending with the new 4LAC Catalog the predictive capabilities of the machine learning (ML) methodology published in Dainotti et al. (2021). Furthermore, we implement feature engineering to expand the parameter space and a bias correction technique to our final results. This study uses additional machine learning techniques inside the ensemble method, the SuperLearner, previously used in Dainotti et al.(2021). Additionally, we also test a novel ML model called Sorted L-One Penalized Estimation (SLOPE). Using these methods we provide a catalog of estimated redshift values for those AGNs that do not have a spectroscopic redshift measurement. These estimates can serve as a redshift reference for the community to verify as updated Fermi catalogs are released with more redshift measurements.

astro-ph.HE↗

arXiv2022

AGN in the ULIRG HE 0435-5304

HE 0435-5304 from Hamburg European Southern Observatory survey is a quasar that appears in the literature with two conflicting redshift values: $\sim 1.2$ and $\sim 0.4$. It was used in the studies of the intergalactic medium through fitting of the narrow absorption lines in its ultraviolet (UV) spectrum. This source is also known historically as a luminous infrared galaxy. We present optical spectra of HE 0435-5304, aiming to precisely measure its redshift and to study its physical properties. In particular, properties of its active nucleus, which is studied in the context of the source being identified here as an ultra-luminous infrared galaxy, allow us to place this quasar in the context of the general population. Fitting the spectra, we focused on modeling H$β$ and [O III] lines. Based on these, we derived the virial black hole mass, bolometric luminosity, and Eddington ratio of the active galactic nucleus (AGN). Additionally, we performed broad band photometry fitting which allows us to quantify host galaxy parameters. The improved redshift value of HE 0435-5304 is estimated to $0.42788 \pm 0.00027$ based on the [O II] line, which is mostly consistent with the narrowest components of the other emission lines. The source was found to be a relatively massive and luminous AGN whose host galaxy is actively forming stars. Although its stellar population seems to be heavily obscured, we did not find evidence for significant obscuration of the nucleus. We conclude that the AGN HE 0435-5304 is a rather prominent iron emitter from the extreme type-A population very close to the narrow-line Seyfert 1 group. The fact that the width of the H$β$ line appears to be systematically growing in its broadest component with time may suggest that this AGN is changing its broad line region. Due to the influence of atmospheric effects, this finding is uncertain.

astro-ph.GA↗

arXiv2021

Viewing angle observations and effects of evolution with redshift, black hole mass, and Eddington ratio in quasar based cosmology

This study is focused on the observational measurement of the viewing angle of individual quasars by modeling the broadband quasar spectrum ranging from the infra-red (IR) to the soft X-ray band. Sources are selected from various published catalogs, and their broadband quasi-simultaneous spectral data points were collected and used to model. We started with a COSMOS sample of type-1 sources which have broadband photometric points. Then, to include more data points, we cross-matched the COSMOS with the SDSS DR14 quasar catalog, and eventually, we find 90 sources that have broadband data ranging from IR to soft X-ray. The broadband spectral energy distribution (SED) modeling is done in Xspec by using the optxagnf and the SKIRTOR models for the X-ray, UV, Optical, and IR regimes for each source. The whole sample is divided into four bins with respect to redshift, black hole (BH) mass, and Eddington ratio with an equal number of sources in each bin. The viewing angle is estimated in each bin, and its evolution with respect to redshift, BH mass, and Eddington ratio is examined. As a result, we did not find any significant evolution of viewing angle with those parameters within the 95$\%$ confidence interval. We conclude that the use of quasars in cosmology to determine the expansion rate of the universe is therefore justified, and biases are not expected.

astro-ph.GA↗

arXiv2021

Environmental Effects on AGN activity via Extinction-free Mid-Infrared Census

How does the environment affect active galactic nucleus (AGN) activity? We investigated this question in an extinction-free way, by selecting 1120 infrared galaxies in the $AKARI$ North Ecliptic Pole Wide field at redshift $z$ $\leq$ 1.2. A unique feature of the $AKARI$ satellite is its continuous 9-band infrared (IR) filter coverage, providing us with an unprecedentedly large sample of IR spectral energy distributions (SEDs) of galaxies. By taking advantage of this, for the first time, we explored the AGN activity derived from SED modelling as a function of redshift, luminosity, and environment. We quantified AGN activity in two ways: AGN contribution fraction (ratio of AGN luminosity to the total IR luminosity), and AGN number fraction (ratio of number of AGNs to the total galaxy sample). We found that galaxy environment (normalised local density) does not greatly affect either definitions of AGN activity of our IRG/LIRG samples (log ${\rm L}_{\rm TIR}$ $\leq$ 12). However, we found a different behavior for ULIRGs (log ${\rm L}_{\rm TIR}$ $>$ 12). At our highest redshift bin (0.7 $\lesssim$ z $\lesssim$ 1.2), AGN activity increases with denser environments, but at the intermediate redshift bin (0.3 $\lesssim$ z $\lesssim$ 0.7), the opposite is observed. These results may hint at a different physical mechanism for ULIRGs. The trends are not statistically significant (p $\geq$ 0.060 at the intermediate redshift bin, and p $\geq$ 0.139 at the highest redshift bin). Possible different behavior of ULIRGs is a key direction to explore further with future space missions (e.g., $JWST$, $Euclid$, $SPHEREx$).

astro-ph.GA↗

arXiv2021

Predicting the redshift of gamma-ray loud AGNs using supervised machine learning

AGNs are very powerful galaxies characterized by extremely bright emissions coming out from their central massive black holes. Knowing the redshifts of AGNs provides us with an opportunity to determine their distance to investigate important astrophysical problems such as the evolution of the early stars, their formation along with the structure of early galaxies. The redshift determination is challenging because it requires detailed follow-up of multi-wavelength observations, often involving various astronomical facilities. Here, we employ machine learning algorithms to estimate redshifts from the observed gamma-ray properties and photometric data of gamma-ray loud AGN from the Fourth Fermi-LAT Catalog. The prediction is obtained with the Superlearner algorithm, using LASSO selected set of predictors. We obtain a tight correlation, with a Pearson Correlation Coefficient of 71.3% between the inferred and the observed redshifts, an average Δz_norm = 11.6 x 10^-4. We stress that notwithstanding the small sample of gamma-ray loud AGNs, we obtain a reliable predictive model using Superlearner, which is an ensemble of several machine learning models.

astro-ph.HE↗

arXiv2021

Getting ready for the LSST data -- estimating the physical properties of $z<2.5$ main sequence galaxies

In this work we study how to employ the upcoming Legacy Survey of Space and Time (LSST) data to constrain physical properties of normal, star forming galaxies. We use simulated LSST data and existing real observations to test the estimations of the physical properties of galaxies, such as star formation rate (SFR), stellar mass ($M_{star}$), and dust luminosity ($L_{dust}$). We focus on normal star-forming galaxies, as they form the majority of the galaxy population in the universe and therefore are more likely to be observed by the LSST. We perform a simulation of LSST observations and uncertainties of 50,385 real galaxies within redshift range $0<z<2.5$. In order to achieve this goal, we used the unique multi-wavelength data from the Herschel Extragalactic Legacy Project (HELP) survey. Our analysis focus on two fields: ELAIS-N1 and COSMOS. To obtain galaxy physical properties we fit their Spectral Energy Distributions (SEDs) using the Code Investigating GALaxy Emission (CIGALE). We compare the main galaxy physical properties obtained from the fit of the observed multi-wavelength photometry of galaxies (from UV to FIR) to the ones obtained from the simulated LSST optical measurements only. The stellar masses estimated based on the LSST measurements are in agreement with the full UV-FIR SED estimations, as they depend mainly on the UV and optical emission, well covered by LSST in the considered redshift range. We obtain a clear overestimation of SFR, $L_{dust}$, $M_{dust}$ estimated with LSST only, highly correlated with redshift. We investigate the cause of this overestimation and we conclude that it is related to an overestimation of the dust attenuation, both UV and NIR. We find that it is necessary to employ auxiliary rest-frame mid-infrared observations, simulated UV observations, or FUV attenuation (AFUV)- Mstar relation, to correct the overestimation.

astro-ph.GA↗